Adaptive Flux Morphologies

AFM Golnoush Jalali (MSc) Javier A. Card贸s Elena (MArch) Dennis Goff (MArch) Mary Polites (MArch)

AFM

ADAPTIVE FLUX MORPHOLOGIES

This research would not have been possible without the help of my EmTech colleagues. I want to especially thank my group mates Javi, Mary, and Dennis for their support and making this an unforgettable experience.

To Sima, Saeid and Shekoufeh.

EMERGENT TECHNOLOGIES & DESIGN

0-1 Slime Mould growth

CONTENTS

ADAPTIVE FLUX MORPHOLOGIES

0.0

Abstract

006

1.0

Introduction

008

2.0

Domain 2.1 Cities and public transport 2.2 Flow and movement 2.3 Metabolist movement 2.4 Spatial analysis of urban fabrics 2.5 Slime mould

012 014 016 018 020 022

3.0

Methods 3.1 Agent-based modelling 3.2 Space syntax 3.2 Network graphs

026 028 030 032

4.0

Site: Lagos (Nigeria) 5.1 Site chosen 5.2 Locating activity nodes

034 036 042

5.0

Research and network development 5.1 Slime Mould. Physical testing 5.2 Slime Mould. Digital simulation 5.3 Historic underground networks 5.4 Generation of the network 5.5 Node ranking 5.6 Space syntax 5.7 Line division 5.8 Station placement

044 046 052 058 064 066 068 080 082

6.0

Local implications 6.1 Area of influence 6.2 Station categories 6.3 Intermodal station case studies 6.4 Pedestrian node generation 6.5 Category A station design 6.6 Evaluation

096 098 100 104 114 122 128

7.0

Conclusions 7.1 Conclusions 7.2 Further development

130 132 136

8.0

Appendix 8.1 Scripts 8.2 Charts and tables 8.3 Station analysis

138 140 158 166

Bibliography

170

Illustration credits

173

ABSTRACT

0.0

0-1 Slime Mould growth simulating road network in different regions: Spain, Africa and China.

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EMERGENT TECHNOLOGIES & DESIGN

ABSTRACT

ADAPTIVE FLUX MORPHOLOGIES

0.0

An inevitable result of transportation networks is the variation of flow of people across the network. Too often, these flows are thought of simply as an output of a network. The network is created and implemented, and the flows are extracted from the resulting usage.

We suggest a new method, wherein the flows are predicted and used as inputs into a system that can provide more accurate predictions of the number of people that will be using the network on the global scale and on the more localised scale of individual stations. In this way, the individual network nodes and their corresponding local networks will reflect the rules of the network within the city. Our research utilises agent based computing as an adaptive, generative tool for creating network solutions. The outputs of this

system are evaluated using space syntax software in order to determine their potential effects on the urban context. By using the system to generate both global and local networks for an urban fabric, substantial improvements can be made in terms of the integration and connectivity of the city as a whole.

Keywords: Infrastructure, Transportation, Flow, Slime mould, Agent-Based Modeling, Adaptation, Space Syntax, Integration

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EMERGENT TECHNOLOGIES & DESIGN

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ADAPTIVE FLUX MORPHOLOGIES

INTRODUCTION

1.0

9

INTRODUCTION

1.0 1-1 Traffic congestion in Lagos, Nigeria

1-2 Slime Mould growth

1-3 Infrastructure network

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EMERGENT TECHNOLOGIES & DESIGN

INTRODUCTION

ADAPTIVE FLUX MORPHOLOGIES

1.0

The main objective of this research is to develop a system for generating transportation networks in growing urban areas. “Transportation and communication infrastructure systems have played a dramatic role in contributing to explosive urban growth, and therefore impact on its form”1, thus a system that is adaptable to urban growth is necessary in order to ensure the continued effectiveness of the network. Just as transportation networks can contribute to urban growth, they must also adapt to it. This research addresses the development of urbanism through network design, which aims to develop connections that are “able to adapt to new requirements over time.”2

the impracticality of this approach proved to be too much .A more adaptive system is necessary in order to better address urban changes such as exploding populations and sprawling urban growth.

Numerous factors go into the planning, design, and implementation of a transportation network. Typically, this process is a largely top-down approach based on imposing a network on the landscape that has been predefined according to some design criteria regarding a number of routes or the areas that need to be connected. This predefined process leaves little room for adjustment and does not address the development of nodes within the city, but rather general connectivity of the city. This process also has a limited ability to deal with changing conditions over time.

Typically, evaluation of a network occurs after its implementation. However, methods exist that allow for evaluation before implementation and for the relationships between the urban fabric and network to be explored simultaneously. One such method is Space Syntax analysis, which offers the possibility of making preliminary predictions and evaluations of the network’s effect. There are currently many different sources of software on the market that are capable of this type of evaluation. Other methods entail simulating human behaviour and are therefore highly computationally intensive.

The modern concept of the network is that it is not only a planned system of infrastructure; it is an emergent quality of the city. A single network is connected to multiple networks and functions on many social and ubiquitous levels. These complex relationships lend themselves to design methods that can accommodate large amounts of information and high degrees of connection.

We propose that through the use of agent-based computing there is great potential for creating adaptive systems. Through clear definition of rules of interaction, and flow of agents in the system, it is possible to develop a system that can quickly adjust to changing conditions. Given the proper rule-set, an agent-based model has the potential to generate multiple variations of network solutions which can be individually evaluated to further the robustness of the system.

Past movements such as the Metabolists attempted to create architectural systems that had the ability to adjust to changing urban conditions such as increasing populations, and rapidly expanding cities, but were limited by economic and social constraints of their time. Their designs centred on adapting to changing urban conditions through physically modifying the architecture as was seen in the capsule buildings that they have become known for. However

1.

Precedents by influential icons such as Ildefons Cerdâ and Frank Lloyd Wright have also dealt with the concept designing for the dynamic flows associated with urban growth. Cerdà envisioned a network with constant and unlimited movement of rapid, direct flows without bounds within the city2 and Wright suggested a concept that everyone is connected to the collective space and all directions are equally open for exploration.3

Stoll, Lloyd; 2010; p. 6.

2. Dupuy; 2008; p 20.

3. Dupuy; 2008; p 35.

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EMERGENT TECHNOLOGIES & DESIGN

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ADAPTIVE FLUX MORPHOLOGIES

DOMAIN

2.0

2.1 Cities and Public transport 2.2 Flow and movement 2.3 Metabolist Movement 2.4 Spatial analysis of urban fabrics 2.5 Slime mould

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CITIES AND PUBLIC TRANSPORT

EMERGENT TECHNOLOGIES & DESIGN

2.1 2-1 Density levels and populations size of metropolis regions.

2-2 Le Corbusier's Ville Radieuse.

1924.

The project was divided in four categories which organized into hierarchies different programs.

The use of automobile is essential for the mobility of the inhabitants.

Emergence of cities The year 2008 marked the first time that more than 50% of world’s population is living in cities (Fig. 2-1).

This number is expected to increase to 70% by 2050.

While urban populations in developed countries are expected to reach a plateau, the urban populations in developing parts of the world are expected to increase significantly, and many new cities will begin to appear.

Cities may have changed morphologically, but from the first settlements of humans to the latest newly-built megacities in China, the essence is still the same.

Cities consist of systems whose agents co-exist in an “amplified flow of materials, energy and information, and an increase in social and cultural complexity”1

1. Weinstock; 2010; p. 186.

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CITIES AND PUBLIC TRANSPORT

ADAPTIVE FLUX MORPHOLOGIES

2.1 While cities started from 20,000 inhabitants, the biggest cities in the world today exceed 42,000,000 people. As a result, the dynamics and processes within the city get more and more complex and are affected more sensitively by changes in its environment or in its internal dynamics.

Layout of cities change with the evolution of transportation The spatial structure of the city is directly linked to the means of transportation that prevails during the period of growth. Early settlements were constrained by the topography of the site coupled with the traces of historical settlements. Also, the proximity of a river and the possibility of moving commodities by boat determined the success of the city. During the Roman Empire the grid was first implemented in new settlements. The same layout was used for their camps because it adapted easily to the movement of troops by horse. With the emergence of electric streetcars and the automobile, street layouts became more curvilinear and presented more cul-de-sac streets.

In the beginning, the absence of long-distance transportation made human activity nodes compact and housed many different activities. Over time as the speed of transportation increased, the clustering of activities appeared. Segregation of activities has been present in the Modern Movement (Fig. 2-2) from different architects. The centralized cities started the craze of automobile as indispensable for moving through the city. Road traffic increased considerably, producing problems of connectivity, traffic jams and pollution. An increasing need for mobility was recognised and led to the use of rail-based means of transportation.

Means of urban mobility On average, people in cities spend 1.2 hours per day commuting. This value hasn’t changed in centuries as a result of increase in transportation speed. As a result, people are comfortable with longer trips in order to arrive at their destination. This fact marks a new horizon for transportation, as the influence of an urban network can be broadened past the city scale to the inter-urban scale as well. Both short and longlasting trips can be addressed by the network service.

As cities evolved, the levels of population density lead to a significant increase in road traffic and many problems regarding pollution and transportation arose. New means of transportation were needed to solve this problem and make mega-cities feasible. In this context, the underground emerged in 1863 as an efficient, powerful means of transportation. This multilayered system for the city allowed for independence from road dynamics and street layouts, which permitted building pathways connecting points in a more straight-forward fashion. The initial tunnels were

built using the cut-and cover procedure. This caused a lot of trouble over ground until new technologies were developed that allowed complete independence from surface dynamics. Direct paths from station to station were feasible which optimised travel time.

Today, many modes of urban mobility such as bus, light rail, railway, underground, ferries, etc., are implemented in cities at once, each with different advantages and disadvantages. They all interconnect forming a complex system in which flow dynamics are shared amongst the different systems. The way in which these systems are evaluated will be shown in a following section.

Problems with implementation of transportation networks While nodes are implemented in the urban tissue with local changes in a relatively simple way, the linkages between them carry more inconveniences to deal with.

Streets represent the basic level of connectivity, while other, more complex means such as underground or train affect more the over-ground situation regarding ecology, comfort, and pedestrian connectivity.

Despite this, the city is not a stable system. An area within a city that presents a huge amount of activity in a moment in time does not mean that is going to do so forever. Networks need to adapt to changes in service in different scales of time: for a special event, seasonal fluctuations, or overall changes in the city with new developments built.

Collapse of cities As explained before, the needs of commutability of population in every city means that transportation plays an important role in city dynamics, and a poorly functioning system can lead to a city not working at all. A long-term successful development of a transportation network requires a meticulous study of both physical facts and sociological behaviour within it. The implementation of this new network is vital to assure a decrease in the automobile flow and thus a reduction of pollution, street congestion, and energy consumption in transportation. Transport networks should not represent a problem, but rather an efficient way of moving through the city in the fastest way possible while benefitting as much of the population as possible. How can a transportation network adapt to every possible change avoiding the collapse of future mega-cities?

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FLOW AND MOVEMENT

2.2 2-3 Map of New York City showing the use of the city from geotagged Twitter Posts

2-4 Diagrams of movement in Philadelphia (Louis Kahn)

2-5 Highway interchanges have become a defining characteristic of urban areas

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EMERGENT TECHNOLOGIES & DESIGN

FLOW AND MOVEMENT

ADAPTIVE FLUX MORPHOLOGIES

2.2

Flow and Movement Infrastructure networks are the supporting material for movement and flow. “Manuel Castell conceives of the space of flows as the ‘specialization’ of today’s society, a society which is deeply infused by the complex and flexible morphology of the network”.1 Therefore, there should be an emphasis on the effect of movement and flows on the organization of cities. Although flows have certain directionality due to their intensity and the network that carries them, they remain fundamentally intangible. Therefore their supporting network is subject to an ever-changing geometry of flows. We suggest another view of the space of flows where the society (flows and movement) and physical places mutually constitute each other. Space is the material support of movement and flows, and if urban environments are constructed around flows, then a spatial form emerges, one which can determine the morphology of its supporting network.2 Today’s society is increasingly defined by its flows and the rate of its population growth. The fluctuations in the two create variable geometries, and as a result there can be no such thing as fixed position in a network whose shape is changing constantly in response to these fluctuations. The notion of specific places in a network is subject to constant changes as existing nodes temporarily gain or lose importance. “Since the origin and destination of flows can neither be controlled nor predicted, the key issue becomes flexibility and adaptability to the potential and requirements of the networks of flows”.3 The chaotic conditions of cities and urban landscapes today should be informed by these extreme fluctuations. Flows have an origin and a destination,

and their trajectories remain deeply governed by the morphology of the network. The change in flow that occurs along these networks invokes a new internal space which is characterized by a “fluid and flexible topology, and should be investigated as a space that is no less real than physical spaces we inhabit”.4 Cities respond to their increasing growth rate and flow fluctuations through their infrastructure, as it governs both their role of cultural and economic drivers, central to the production of flows. “Hence, cities cannot be conceived of without taking into account the network of flows within which they are positioned; neither is it possible to conceive of flows independently from cities that produce them. The global economy therefore creates a new global context of interaction, where flows and cities become mutually defining entities.”5 In this mutual relationship, infrastructures are the mediators as they both create the primary support for flow and movement within cities and lead to the emergence of a range of public spaces.

These changes in flow can play an integral role in development of an area. Placing a network node in an underdeveloped part of the city will obviously change the nature of that particular area. By increasing the area’s connection to the city, it will inherently attract more and more people, which will in turn lead to the area becoming more developed and more important to the city, which will then lead to it needing more connection. This kind of developing cycle can be used as a proactive approach to developing a city through the use of a network. By intentionally bringing a major network into an underdeveloped part of the city, it is possible to stimulate the regeneration of that area.

1. Delalex; 2006; p.60.

2. Delalex; 2006; p.60.

4. Delalex; 2006; p.70.

3. Delalex; 2006; p.65.

5. Delalex; 2006; p.179.

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METABOLIST MOVEMENT

EMERGENT TECHNOLOGIES & DESIGN

2.3 2-6 (Top Left) Illustrations used by Kenzo Tange to justify the linear growth of the city.

2-7 (Left) Phases of growth of Tange's plan for Tokyo Bay.

1st year 2nd year 3rd year

4th year

2-8 (right) Kenzo Tange plan for Tokyo.

1960 2-9 Nakagin Capsule Tower model.

Kisho Kurokawa

2-10 Nakagin Capsule Tower. Floor type plan.

Kisho Kurokawa

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METABOLIST MOVEMENT

ADAPTIVE FLUX MORPHOLOGIES

2.3 The Metabolists After the Second World War, Japan was left in ruins and the challenge of rebuilding the nation became a common topic among architects. This challenge, coupled with the rapid development of technology, led to the emergence of a group of young architects who would become known as the Metabolists. They envisioned a utopian architecture empowered by technological advances and unlimited resources.

A large focus of the Metabolist movement was urban design. With much of the city destroyed by bombing during the war, the Metabolists began rethinking traditional urban design. They strove for a new method which would create a “dynamic relationship between space and function”. 1 They saw the current standards for urban design as creating a “static” condition. They wanted cities to be able to grow organically by adapting to the increasing population. Tokyo was a major case study for many of their projects as its population “explode[d] from 3.5 million in 1945 to 9.5 million in 1960”.2 Several proposals were made to develop Tokyo Bay. The Metabolists rejected the system in place for land ownership, which led them to the idea of creating “an estate where there are no landowners”.3 This idea developed into the concept of “artificial ground”. They proposed reclaiming Tokyo bay in a series of projects all offering ideas of how to create new patches of land in Tokyo Bay for creating a new model of the city. Some projects did so sparingly, while other more extreme projects included proposals of using an atomic bomb to destroy a mountain in order to harvest enough earth to create the artificial islands. The most famous Tokyo Bay proposal was that of Kenzo Tange (Fig. 2-8).

European urban designers of the time were promoting the importance of “an identifiable core modeled after traditional civic centres such as Italian piazzas”.4 Tange’s proposal suggested a different solution: the “civic axis”. This was a departure from radial urban planning, moving into a more linear solution (Fig.2-6 and Fig. 2-7). Tange argued that this solution was analogous to biological development saying: The amoeba and the asteroid have radial centripetal forms, but vertebrates have linear bone structures with parallel radiations. When the living functions of organisms differentiate and perform composite function of life, the centripetal pattern evolves into a system of parallel lines grouped around an axis formed of a spine and arteries. The process whereby a vertebrate body hatches from an egg illustrates the possibility of gradual development of the part of a linear system.5

(both built and un-built) were based around the idea of modular units plugged into larger mega-structures.

The most famous example of this concept is the Nakagin capsule hotel by Kisho Kurokawa (Fig. 2-9).

The building consists of structural core into which individual capsules are inserted (Fig. 2-10). The capsule is a compact living or work space with builtin appliances. The original idea was that the building would be able to adapt to the changing city and the individual units could be removed, replaced or rearranged. However, since the tower’s completion, not one capsule has ever been replaced and in 2007 it was scheduled to be demolished and substituted with a taller new tower of dwellings.

This is a common outcome for the Metabolist built projects. Their work might have been highly visionary but, the built projects do not translate to reality very well. The expected time rate for each project was not determined correctly as the rapid pace of economic and social factors surpassed all expectations.

Nevertheless, this uncertainty is highly related to the consumerism which drives the use of private architecture. The time rates of private programs are subject to more uncertainty due to the changing trends and economic profitability for the same resource over time. With the unsuccessful built examples, the concept of mega-structure decayed over time being substituted by what Fumihiko Maki called micro-scale planning. These are small interventions in the city tissue that would generate local changes that adapt to the preexisting conditions of the area. This was in contrast to the excess of ambitions of Metabolists which Maki suggested.6 While private programs manifested many flaws, public developments may guarantee a longer life span. Many examples such as the designs in Tokyo Bay consisted of transport linkages and planning their growth with the city.

Fifty years ago, the Metabolist movement dealt with the same issues that affect our current cities. The unlimited expansion of cities due to the exponential increase of human population was and is a major problem, both in developed and developing countries.

The creation of artificial ground to increase the usable area in densely-built cities may represent an efficient mechanism for relieving city conditions. Infrastructure as a driver of urban sprawl has always been used in traditional urban planning not as a response to the unpredictable urban sprawl, but rather has imposed rules which are not always effective.

2-11 Kenzo Tange during the presentation of the Tokyo Bay project in 1960

Another common theme in Metabolist architecture was the idea of the capsule. Many Metabolist projects 1. Zhongjie, 2010, p.176.

2. Koolhaas, Obrist, 2011, p.267.

3. Koolhaas, Obrist, 2011, p.274.

4. Zhongjie, 2010, p.154.

5. Zhongjie, 2010, p.158.

6. Zhongjie, 2010, p.228.

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SPATIAL ANALYSIS OF URBAN FABRICS

2.4 2-12 Observation of how people use urban spaces is one method of evaluating urban networks

2-13 Wayfinding studies how easy it is to find your way in an urban network

2-14 Space syntax analysis of Greater London area

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EMERGENT TECHNOLOGIES & DESIGN

SPATIAL ANALYSIS OF URBAN FABRICS

ADAPTIVE FLUX MORPHOLOGIES

2.4

When designing a network, a major challenge that is presented is evaluating how people will actually use it in the urban fabric. Over time, many different methods have been proposed. Some are based purely on observation after the implementation of a network, while others can be employed before the network is actually implemented, which provides the benefit of giving preliminary data which can be fed back into the system in order to improve the proposed solution.

Observation Method One method of evaluating how people use a network comes from a study that was done in the 1970’s

by The Street Life Project, which was formed by a sociologist named William H. Whyte. This group’s research was a “firsthand observation project [that] studied how people inhabit the most intensely used urban spaces”1. The methods employed in this study involved personal interviews with people and by timelapse photography of the public spaces in order to track the movement of individuals through the space.

This method can tell a great deal about the personal choices that people make when occupying space or accessing a network (Fig. 2-12).

Wayfinding Analysis Another method which has become more common, especially with the advent of digital tools, is Wayfinding. Wayfinding deals with how easy it is for a person to navigate a spatial network. Initial attempts to model this behaviour computationally were based around simulated “people” building up a “knowledge base”. This base would grow as the person moves through the network and acquires more information about the overall patterns of the network. Eventually, there was a shift from trying to model knowledge to trying to model behaviour. The people designing these systems realised that “the ‘simple’ intelligence that they were trying to model (from knowledge based approach) was present in animals in their adaptive behaviour studied in ethology”2. This method draws

on concepts of emergence wherein individuals are given a simple intelligence and the interaction of large amounts of these simple individuals results in higher level, global intelligence (Fig. 2-13).

Space Syntax The third method of evaluation is Space Syntax analysis. Through abstraction of space between buildings into straight lines, this method is based on calculations which represent usage of networks by analysing “spatial configurations in relation to human socio-economics”3. The advantage of this method is that these values can be calculated without having to actually simulate the human behaviour. This makes this method a very fast and accurate way of evaluating networks on an urban scale. The calculation is very fast and easily visualised. This makes it a highly valuable tool for evaluating multiple solutions for an urban network side by side (Fig. 2-14).

Conclusion Even though all three of these methods have their own merits, Space Syntax analysis will be the tool we use in evaluating our system. The fact that it can produce accurate representations of human use without the need for the added computational burden of simulating human behaviour makes it an ideal tool for quickly evaluating various network solutions. Also, since the information is all quantified into numerical values, it is possible to easily integrate the results into algorithmic processes. It is also highly visual which makes the results comprehensible to outside viewers.

1. Therakomen; 2001; p. 9.

2. Therakomen; 2001; p. 13.

3. Therakomen; 2001; p. 13.

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SLIME MOULD

2.5 2-15 Plasmodial stage of physarum polycephalum.

2-16 Different phases of growth of slime mould when selecting the optimal path to the solution of the maze.

2-17 Network formation in physarum polycephalum in the city of Tokyo after 8 hours (left) and 26 hours (right)

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EMERGENT TECHNOLOGIES & DESIGN

SLIME MOULD

ADAPTIVE FLUX MORPHOLOGIES

2.5

Biological Network Systems The organism Physarum Polycephalum (commonly known as slime mould) has been studied extensively for its emergent behaviors (Fig. 2-15).This primitive organism is capable of solving relatively complex problems through its foraging behavior. This organism produces a plasmodium which is a “large amoebalike cell consisting of a dendritic network of tube-like structures”.1 At first, the spread of the plasmodium is generally homogenous until it encounters food sources. When it is presented with multiple food sources in a field, the plasmodium becomes more concentrated and produces a network of tubes which connect these food sources. Areas that are not in close proximity to food sources shrink and contract back into the bigger more connected tubes. The result is an efficient network which is able to transport nutrients to the extent of the organism’s body while minimizing overall length of the network paths.

Problem Solving In 2000, the scientists Toshiyuki Nakagaki, Hiroyasu Yamada, and Ágota Tóth published a paper showcasing the problem solving capabilities of this primitive organism. They first placed several pieces of a slime mould culture inside a labyrinth. Next, they placed food sources at the entrance and the exit of the labyrinth. The slime mould began to forage for food, and after four hours, a network was already beginning to emerge. The parts of the plasmodium occupying “dead-ends” of the labyrinth began to recede and integrate into the stronger parts of the network. After another four hours, the network had reduced down

to “one thick tube covering the shortest distance”2 through the labyrinth. In 8 hours, an organism with little to no actual intelligence was able to solve a complex problem in the most efficient way (Fig. 2-16).

Inter-City Network Simulation These studies were taken to a new scale in 2010, when a team of Japanese scientists began testing the physarum network on an urban scale. Rather than presenting the slime mould with a maze with 2 food sources, they presented the mould with a distributed group of food sources that were laid out to match the pattern of distribution of cities around Tokyo.

The slime growth was initiated from the location of the Tokyo metro area. At the beginning, the slime “filled much of the available land space”3 in order to discover all of the available food sources. However, as time went on, the plasmodium refined itself down to an interconnected network connecting all of the food sources. Terrain obstacles were represented by areas of high illumination. Physarum is sensitive to intense light, and therefore avoids areas that are brightly lit.

The resulting networks bore a high resemblance to the existing train networks connecting Tokyo to the surrounding cities (Fig. 2-17).

A similar experiment was carried out using the layout of cities in the United Kingdom (Fig. 2-18). This experiment followed similar procedures as the Tokyo experiment for generating national transportation networks, but compared the results to the existing road network rather than the existing train network.

This experiment also employed the use of a digital

2. Nakagaki, Yamada, Tóth; 2000; p.470.

1. Nakagaki, Yamada, Tóth; 2000; p.470.

3. Tero, Takagi, Saigusa, Ito, Bebber, Fricker, Yumiki, Kobayashi, Nakagaki; 2010; p.439.

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SLIME MOULD

2.5 2-18 Growth of 4 independent samples of Physarum Polycephalum in the layout of Great Britain. Food sources are placed where the 10 most populated cities are.

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EMERGENT TECHNOLOGIES & DESIGN

SLIME MOULD

ADAPTIVE FLUX MORPHOLOGIES

2.5 2-19 Digital simulation and optimization of the network applied in Great Britain layout.

simulation in parallel to the physical tests (Fig 2-19).

The results of this experiment produced a strange anomaly. For the most part, the resulting networks closely mimicked the existing transportation network in the UK as they did in the Tokyo experiment.

However, the results suggested that the M6/M74 route between Manchester and Glasgow is not “optimally positioned”.4 This route only appeared in “three of twenty-five experiments”5, or only 12% of the time. The simulation suggested that there should be a route connecting Newcastle to Glasgow, either in addition to the existing Manchester-Glasgow route, or without the Manchester-Glasgow route altogether.

One possible explanation for this phenomenon is that this experiment did not take terrain into account.

When looking at a map of the UK, it can be seen that the area between Newcastle and Glasgow is quite mountainous, which would explain why there is no major road link between the two. In a totally flat world, the best solution may very well be to eliminate the Manchester-Glasgow route in favor of a NewcastleGlasgow route as the slime mould suggested.

Another important strategy that was incorporated in the UK experiment was the simulation of natural disasters. In order to accomplish this simulation, a city was selected to have a “disaster”. Rather than placing a piece of food at this city, salt was placed there. The salt repels the slime mould, so the network abandons this particular node and rebuilds itself in

order to avoid the contaminated city. Once the salt has diffused out of the node, the network returns “reconnects with previously contaminated nodes”.6 Conclusions As a result of researching these different case studies, it is our conclusion that physarum polycephalum’s emergent behaviors develop patterns that can be applied to the physical world. They have been used at scales as small as solving a simple maze and scales as big as simulating the transportation networks of an entire country. These simulations are effective as they allow for mathematical and geometric patterns that define spatial relationships by connection. These connections are applicable to multiple degrees of scale. One scale where they have not been utilized so far is at the scale of a single city. In the example of the inter-city network studies that were discussed previously, cities are made up of activity centres which act as nodes for the movement of people.

By distributing food sources to these major activity centres within a city, similar experiments can be done in order to simulate the existing rail transportation networks in individual cities. The experiments can also be used in a way other than simulating an existing network. It is our conclusion that they can be used as a generative design tool in order to design networks that are efficient and evenly distributed in urban environments.

4. Adamatzky, Jones; 2009; p.14.

5. Adamatzky, Jones; 2009; p.13.

6. Adamatzky, Jones; 2009; p.20.

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ADAPTIVE FLUX MORPHOLOGIES

METHODS

3.0

3.1 Agent-based modelling 3.2 Space Syntax 3.3 Network graphs

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AGENT-BASED MODELLING

3.1

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EMERGENT TECHNOLOGIES & DESIGN

ADAPTIVE FLUX MORPHOLOGIES

AGENT-BASED MODELLING

3.1 3-1 (left) Flocks present a similar behavior as agent-based simulations.

3-2 (right) A shoal, another natural agentbased behavior.

3-3 Agent based simulation of rovers on the surface of Mars

Agent-Based Modeling Modeling the behaviour of physarum polycephalum in a digital environment requires a highly adaptive form of programming. Through our research we arrived at the conclusion that the best option was to use an agent-based system. Unlike Cellular Automata systems, which are made up of stationary cells which simply fluctuate between states (on/off, dead/ alive, etc.), an agent-based system is made up of autonomous “agents” which have a small intelligence built in to them and are able to move about and interact with each other and their environment. These agents can represent anything, be it a point in space, a bird, or even a human being. The important thing is their rules of interaction. Each individual has a set of rules for how it interacts with its surrounding environment and its neighbouring individuals. A possible example of these rules would be: If X happens, do Y. Through this interaction, these agents are capable of producing complex behaviours from simple rules. It is similar to natural phenomena such as starling murmurations (Fig. 3-1) or fish schooling (Fig. 3-2). In these behaviours, none of the individual birds or fish is aware of the global geometry of the group. All they are doing is sensing their immediate surroundings, and based on “rules” of proximity and

movement, making decisions regarding how to move next. The result is an amazing display of coherent group behaviour that is actually completely emergent.

This is what agent-based programming aims for.

By defining simple rules for a system, the system itself is able to generate complex solutions. In this type of system the designer makes certain assumptions and observes the system as the system runs in order to observe the phenomena that arise from the agent behaviours. Agent-based systems are highly emergent and allow for realistic simulations of natural phenomena (Fig. 3-3). It is for this reason that we pursued an agent-based model in our further research. The environment for our simulations will be Rhinoceros 5.0 beta running on laptop computers making use of the Grasshopper plugin and we will be scripting in the Python scripting language.

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SPACE SYNTAX

EMERGENT TECHNOLOGIES & DESIGN

3.2 3-4 Mean depth value (MD)

3

2

1 n = number of connected spaces d = depth values Σd = sum of depth values MD = Mean Depth = Σd / n I = Integration = 1/MD

1

R

1

1 n=3 d = 1,1,1 Σd = 3 MD = Σd/n = 1 I=1

R

Space Syntax Built environments are spatial configurations that are not formed by nature but rather a collection of objects that are generated through certain logic.1 These configurations have a long-term effect on the space they occupy. In other words, “they aggregate by occupying a particular region of space for a long time”.2 “The built environment is the largest and most complex artefact that human beings construct apart from society itself”.3 This results in the emergence of cities that are subject to continual evolution.

Space syntax is the relationship between space and society which allows for the representation of spatial relationships. The aim of space syntax is to capture the configuration of built elements and the emergence of their global complexity and translate them into spatial data. In order to analyse this data, the configuration of built elements needs to be converted to an axial map. The axial map is one of the main tools of space syntax. Axial maps are used in order to visualise the space between buildings and their surroundings. It is basically an abstraction of this space into straight lines, which results in a graph that can be further analysed in order to approximate how it is used by people. Space syntax analysis allows for a visual approximation of pedestrian movement in an urban settlement. It can identify areas of high flow and

1. Hillier; 1996; p.69.

2. Hillier; 1996; p.68.

3. Hillier; 1996; p.68.

30

2

2

1 n=3 d = 1,2,3 Σd = 6 MD = Σd/n = 2 I = 0.5

R

n=3 d = 1,2,2 Σd = 5 MD = Σd/n = 1.67 I = 0.6

which areas need to be better connected within an urban fabric.

Syntactic Measures In order to analyze the global complexity of cities, measures such as depth, integration, and connectivity can be used. Depth is a measure which represents the distance from one space to another space. For each individual space R, R is treated as the root space.

Next, every other space is assigned a value of depth which represents the number of spaces that you have to go through to get there from space R. Averaging these depth values will give the Mean Depth for space R which represents the average distance from space R to all other spaces (Fig. 3-4). “Integration measures how many turns one has to make from a street segment to reach all other street segments in the network within a radius, using shortest paths”.4 It is calculated as the inverse of Mean Depth (Fig. 3-5). “Theoretically, the integration measure shows the cognitive complexity of reaching a street, and is often argued to ‘predict' the pedestrian use of a street”.5 The first intersection segment requires only one turn, the second, two turns and so on. Integration is a measure of how accessible each segment is from all others, in other words it shows the potential of each segment as a destination for movement. “The closer a segment is to all others,

4. http://www.environment.gen.tr/human-settlements/557-what-isspace-syntax.html 5. http://www.environment.gen.tr/human-settlements/557-what-isspace-syntax.html

SPACE SYNTAX

ADAPTIVE FLUX MORPHOLOGIES

3.2

MD = 2.5 I = 0.4

3-5 Integration (r = n) analysis definition 1

x Every axial line

0

Connectivity analysis definition

2

3 3

3

Segment Evaluated

3-6

2

2

The number of iterations is the same number of streets in the axial map to analize.

Max value

1 1 3

2

2

3 3

the more promise it offers as a destination”.6 Connectivity is a value that represents the number of spaces that a particular space intersects (Fig. 3-6).

Connectivity and Integration are the two measures that deal with human movement. Connectivity is a property that can be seen from each space. Wherever one is in the space, one can see how many neighbouring spaces it connects to. Integration, on the other hand, cannot be seen from a space, since it is a value that is determined based on the entire network, most of which cannot be seen from one space. An intelligible system is one in which well-connected spaces also tend to be well-integrated spaces. An unintelligible system is one where well-connected spaces are not well integrated so that the visible connections are misleading about the integration of that space in the system as a whole. By plotting the integration and connectivity values of all spaces of a network, a trend line can be extracted, the slope of which (R2) represents the intelligibility of the network.

Impact Maps When adding a new network to an existing city map such as a metro or bus service, there is an effect on the integration of the original axial map. This effect is known as impact and can be visualised through the use of impact maps. Rather than simply measuring the integration of the streets a second time after the implementation of the new network, an impact map shows the change in integration. Impact maps don’t

1

show the areas that are the most integrated, but rather, the areas that were most affected by the new network. Impact maps will become an integral part of our research as we aim to implement new networks into an existing city. Impact maps will be used in order to ensure that the interventions that we suggest are actually positively affecting the existing urban environment.7 Designing with these measures “Because these techniques allow us to deal graphically with the numerical properties of spatial layouts, we can also use them creatively in design.

For example, extensive research has shown that patterns of movement in urban areas are strongly predicted by the distribution of integration in a simple line representation of the street grid”.8 By using these measures in urban design simulations, it is possible to gain more insight into urban patterns that are not clear to intuition. For example, we are able to determine areas that are segregated within the global network and need to be integrated or areas of high traffic flow that need more connection. This potential can be used in urban design in order to examine the effect of adding infrastructural networks to the urban network. By adding another network to an existing one, the spatial configuration of all other elements in the system will change and space syntax can be used as an evaluation tool to examine the effects of different networks on the urban network.

7. Gil; 2012; p.13.

6. Hillier; 2007-8; p.2.

8. Hillier; 1996; p.98.

31

NETWORK GRAPHS

EMERGENT TECHNOLOGIES & DESIGN

3.3 3-7 Success/failure rules for Gabriel graph and Relative Neighborhood graph.

Gabriel graph

Relative neighborhood graph

Pair of points evaluated A

dab

A

C

Successful connection A

Failed connection

B

C B

A

C A

B

C B

A

B

Connectivity Graphs

area of these two circles (Fig. 3-7 and Fig. 3-10).

When generating networks for a given set of points, there are a number of different graphs that can be generated: There is the Delaunay Triangulation, the Voronoi Diagram, the Gabriel Graph (GG), the Relative Neighbourhood Graph (RNG), and the Minimum Spanning Tree (MST) to name a few. The three graphs that we will use to analyse and evaluate our experiments are the GG, RNG, and the MST.

Minimum Spanning Tree

Gabriel Graph The process for computing the Gabriel Graph is as follows: For a set of points, two points (a,b) are considered to be connected if no other point (c) in the set is contained within a circle which passes through a and b and whose diameter is the distance between a and b(dab) (Fig. 3-7 and Fig. 3-9).

Relative Neighbourhood Graph The Relative Neighbourhood Graph (RNG) is a subgraph of the Gabriel Graph, meaning that all the lines in the RNG are present in the GG. The process for computing the RNG is as follows: for two points (a,b) circles are drawn centred on the each point with a radius equal to the distance between a and b (dab).

Points a and b are considered to be connected if no other point (c) is contained within the overlapping

32

B

The Minimum Spanning Tree (MST) is a sub-graph of the Relative Neighbourhood Graph. The MST is the network with the shortest possible length that connects all points in a set without creating any closed loops (Fig. 3-11). The MST is a highly important graph in analyzing networks. It speaks to the efficiency of a network in terms of material used. It is used frequently in communications industries. For example, cable companies will use the MST in order to lay the least amount of cable needed in order to connect the whole network.

Application The reason for using these graphs is that they make potential suggestions of which points in a network should be connected. Each graph has its own set of rules for determining this which results in a variation of the number of connections. By comparing these connectivity graphs to a designed network, it is possible to evaluate whether or not the network adheres to the same rules. It can also be determined whether or not the network design is actually a subgraph or a super-graph of one of the connectivity graphs.

ADAPTIVE FLUX MORPHOLOGIES

NETWORK GRAPHS

3.3 3-8 Initial set of random points

3-9 Gabriel graph

3-10 Relative neighborhood graph

3 -11 Minimum spanning tree graph

33

EMERGENT TECHNOLOGIES & DESIGN

34

ADAPTIVE FLUX MORPHOLOGIES

SITE: LAGOS (NIGERIA)

4.0

5.1 Site chosen 5.2 Locating activity nodes

35

SITE CHOSEN

EMERGENT TECHNOLOGIES & DESIGN

4.1 4-1 Diagram of Africa showing the biggest cities 4 -2 The three major African cities' population and expected in 2025

16 14 12 10 8

4-3 (Opposite page top) Population and percentage of population using public transport in different cities.

4-4 (Opposite page bottom) Comparison of city infrastructures

36

2010 2025

2010 2025

Cairo (Egypt)

LAGOS (Nigeria)

2010 2025 Kinshasa (Congo)

During our initial discussions, we focused on cities with a high projection of population growth because we felt this would inherently require public transit to accommodate the growth. The three sites that were considered were Beihai, China, São Paulo, Brazil, and Lagos, Nigeria. The decision to use Lagos was made after studying further the infrastructure networks that existed in these locations. In São Paulo, the transport infrastructure has already been developed to a degree that any design intervention would act as an addition to the multiple methods available. Neither Beihai nor Lagos had a major transportation network in place, which made both ideal sites for further study.

However, we selected Lagos because it has a larger projected population growth.

of transportation. It also contains the cities’ plans for developing their infrastructure. To compare the cities, we combined the information from the Green City Index, with the data from previous research on the commuting percentages of the populations and produced a matrix of networks (Fig. 4-4).

Case Studies

The city that had the closest population conditions to Lagos from our matrix was Mexico City. The official population is estimated at about 8 million and 20 million unofficially. In Lagos the official population is estimated at 10 million with an unofficial of 25 million.

These numbers address the large percentages of the populations that exist in informal living conditions. In terms of transportation networks, this is important as the development areas of these informal settlements are usually adjacent to the city districts or in close proximity to them. The Green City Index states that, “Africa has the highest proportion of city dwellers in informal settlements in the world”.1 The methods that

To contextualize our study, we began by looking at ratings of public transportation networks that were considered effective. Much of the initial research on evaluating public transport networks was highly subjective, but helped to develop a vocabulary of terms for evaluation of networks in cities. A more objective measure of evaluation is the Green City Index which is an evaluation of cities carried out by the Economist Intelligence Unit which considers governmental data. The Green City Index provided a basis for comparing different cities in the world and had already included Lagos within its study. From this index, information was provided on the populations, percentage of networks to area, and existing modes

The results showed that in terms of population, density, and commuters, Mexico City, Seoul, Tokyo, New York, and Paris were most similar to Lagos. Two cities were then selected for further study and we chose Mexico City and Tokyo. London was also added to our case studies because the information was available in surplus and we have a direct experience with the stations and the network.

1. Siemens, p. 4

SITE CHOSEN

ADAPTIVE FLUX MORPHOLOGIES

4.1 Current population (millions) 16 14 12 10 8 6 4 2 0 Lagos

London

New York

Mexico City

Paris

Seoul

Tokyo

Seoul

Tokyo

% population using public transport 80 70 60 50 40 30 20 10 0 Lagos

LAGOS (2025)

MEXICO CITY

London

New York

Mexico City

Paris

SEOUL

TOKYO

NEW YORK

PARIS

100%

100%

100%

100%

Population = 1 Million

Population traveling by public transportation

100%

100%

0%

Busiest Stations

50%

75%

0%

0%

R-18

= 79,894 DAILY

R-19

= 79,894 DAILY

R-20

= 79,894 DAILY

CUATRO CAMINOS

57%

63%

GANGNAM

0%

SHINJUKU

40%

75%

0%

42 STREET- TIMES SQ

0%

GARE DU NORD

= 11,130,567

= 125,810

= 3.64

= 189,4256

= 1.80

DAILY

DAILY

MILLION/ DAILY

DAILY

MILLION/ DAILY

TASQUENA

JAMSIL

= 10,034,507

= 96,216

DAILY

DAILY

INDIOS VERDES

SILLIM

= 10,391,215

= 95,467

DAILY

DAILY

IKEBUKURO

42 STREET

GARE SAINT - LAZARE

= 2.71

= 147,644

= 147,644

MILLION/ DAILY

DAILY

DAILY

SHIBUYA

34 STREET HERALD SQ

= 2.18

= 121,081

MILLION/ DAILY

DAILY

37

SITE CHOSEN

EMERGENT TECHNOLOGIES & DESIGN

4.1 4-5 Lagos Metropolitan Areas; Lagos State

Epe Ikorodu LAGOS Badagry

38

Ibeju Lekki

these populations use to get to work are a mixture of local methods with public and private vehicles.

These methods of transport are the Okada, Taxi, Danfos, Keke Marwa and bus. The Okada, Danfos and Keke are all local methods that transport one to three people and have the flexibility to move around congestion within the traffic. Since congestion is a common problem due to poor infrastructure, flooding, and rush hours, these methods serve large portions of the population that commute. The lack of public transport also creates a need for the public to find another method of transportation. To address this concern, the Lagos government has proposed new transportation methods to accommodate the city’s projected population. In 2008, the Lagos Master Plan was initiated to bring a bus rapid transit (BRT) system to the public. Mexico City has a similar BRT in place that runs on dedicated lines throughout the city. Therefore, for an estimate on the amount of passengers that can be carried by new systems put into place, we used an amount similar to that of Mexico City.

growth has continued and has primarily occurred in the southern parts of the city included westward in Ojo, eastward in Eti-Osa and increasingly now in Ogun State, north of the city (Fig. 4.6).4

Lagos Growth Statistics

Aim of Network Design

Lagos is currently the 18th most populous city in the world and is expected to be 11th by 2025.2 The population is conservatively projected to grow to more than 25 million by 20253 and Lagos will be the 7th fastest growing economy in the world. There are 20 local government areas (LGA’s) but only 16 make up what is considered the metropolitan Lagos area (Fig. 4-5). In this area, there are 8 million people according to a 2006 census, over an area of 999.6 km2, with an average density of 7.941 ppl/km2. The population increase is estimated at 275,000 people per year, a rate of 6%.3 “In the last twenty years, explosive urban

For our design we will accounted for the local modes of transportation and the overflows of these current systems in Lagos. The existing system is a collection of informally organized means of transport that can adjust quickly to environmental conditions and traffic delays. We are proposing an organized network system because the existing modes exacerbate the problems of the infrastructure. We have assumed that the projected growth will add to these issues and by suggesting a network design that accommodates this growth, the distribution of development will act in tandem with the network. Therefore, an array of

2. Siemens, p. 6

4. Oloto, E.N. and Adebayo;The new Lagos.

3. World Bank, 2011

5. pau|ipopo.hubpages.com/hub/Transportation-in-Lagos-Nigeria

Existing Modes of Transit Currently in Lagos the modes of transit are the Okada, Taxis, Danfos, Keke Marwa and Keke Napep, BRT and Lag buses, ferry, lorries, and trailers (Fig. 4-7 and Fig. 4-8).5 As of 2000 the transport infrastructure and services were at levels that could support a population of no more than six million.3 The conclusion from this information is that the efficiency and productivity in the metropolitan area has been adversely affected by the growing weakness in the physical infrastructure required to support basic needs of the population.

The current road network has a density at about .4 km/1000 population. 3 The conclusion is that due to the lack of infrastructure and projected population, the city will continue to experience large areas of congestions without an intervention (Fig. 4-9).

SITE CHOSEN

ADAPTIVE FLUX MORPHOLOGIES

4.1 4-6 Lagos densely-built areas expansion over time 1900 1900 - 1960 1960 - 1980 1980 - 2012 2012 - 2020

39

SITE CHOSEN

EMERGENT TECHNOLOGIES & DESIGN

4.1 4-7 Existing means of transportation

BRT Route 1 BRT Route 2 BRT Route 3 BRT Route 4 BRT Route 5 BRT Lite Route Lagbus Priority Bus Scheme Route Bus Corridor Pilot Bus Route

BRT known Bus Stop Railway Station Airport

4-8 Distribution of usage across different means of transportation

13%

81%

Buses and mini-buses (Danfos)

Taxi, private cars

1%

5%

Motorcycles (okada)

40

Railway

SITE CHOSEN

ADAPTIVE FLUX MORPHOLOGIES

4.1 4-9 Congested Routes of Lagos

Traffic stopped

Normal pace

growth and system nodes will result in which we can test the limitations of our system by the capacities at which it can handle.

mixed land uses and indeterminate inner and outer boundaries, and typically is split between a number of administrative areas. The land area which can be characterised as peri-urban shifts over time as cities expand.”7

We are also suggesting that with this network, the areas that would normally be separated would be redistributed by means of connection. Therefore, the peri-urban areas wouldn’t act as boundaries to the urban areas and the development would be integrated into the city. “Increasing peri-urbanisation leads to increasingly complex disparities with some relatively sparsely populated peri-urban areas inhabited by young, relatively high-income, households.”6 “The peri-urban interface is a dynamic zone both spatially and structurally. Spatially it is the transition zone between fully urbanised land in cities and areas in predominantly agricultural use. It is characterised by 6. Dupuy; 2008; P 211.

7. Taibat, Omoayena, Idris; 2012.

41

LOCATING ACTIVITY NODES

EMERGENT TECHNOLOGIES & DESIGN

4.2 4-10 Position of activity centres in Lagos

1 1 - Otta 2 - Agege 3 - Ikorodu 4 - Ayobo 5 - Iyana / Ipaja 6 - Ikeja 7 - Egbeda 8 - Alimosho 9 - Igando 10 - Ikotun 11 - Isolo 12 - Oshodi 13 - Mowe / Ibafo 14 - Bariga / Oworo 15 - Alaba 16 - Festac 17 - Mile 2 18 - Ajegunle 19 - Apapa 20 - Lagos Island 21 - Obalende / Ikoyi 22 - Victoria Island

2 4

5 7

6

8

3

12 9

14

10 11 13

15

16

17

20 18

21

19 22

Node Placement In order to begin to generate a new network in Lagos, we first needed to define nodes that would drive the development of the network. In order to do this, we researched various aspects of the city in order to locate areas that are vital to the city’s operation.

First, we looked at the existing public transportation networks in the city. In the case of Lagos, this consists of a Bus Rapid Transit system and other privately owned bus services. From this information, we were able to see which areas were not well connected by the existing transportation network. We also looked for areas where other modes of transportation converge such as the Nigerian Railway Corporation, Murtala Muhammed International Airport, or the ferry services that serve Lagos Lagoon. When considering the placement of nodes for our network, it was crucial that they be in close proximity to these other modes of transportation in order to better connect the existing fabric to the new network.

Another thing that we looked for in the city was cultural and economic centres. Lagos has several large scale markets which are highly important to the city. For example, the Alaba International Electronics Market is the largest importer of electronics in Africa.

It sees “up to15 shipping containers of discarded

electronics from Europe and Asia arrive every day”.1 Other important cultural and economic areas such as religious sites, universities, and the central business district were also considered as places that would need to be well connected (Fig. 4-10).

Node Ranking In order to choose the right activity points it was important to consider their development according to the evolution of the city. As a result we analyzed the growth and the development of the city from 1900 to 2020. The urban growth from 1900 started from Lagos Island where the central business district is currently located. As the city continued to grow, the central business district became a fixed location. The city expanded towards Lagos Mainland from 1900 to 1960 where the Nigerian Railway Corporation constructed its first rail line. The growth of the urban area with its corresponding activity points are also shown from 1960 to 1980 and from 1980 to 2012.

In order to evaluate the activity points for their relative importance, we used a method of evaluation that had been used in a previous study of London.2 The purpose of this study was to push for a stronger integration 1. http://www.moneyweek.com/news-and-charts/economics/ global/a-recycling-fraud-56116 2. Chiaradia, Alain; Law, Stephen; Schwander, Christian. 2012

42

LOCATING ACTIVITY NODES

ADAPTIVE FLUX MORPHOLOGIES

4.2 4-11

1900 Rank

1900-1960 Rank

1960-1980 Rank

1980-2012 Rank

2012-2020 Rank

20

13

13

12

5

21

12

12

5

7

20

14

14

8

19

20

13

12

21

17

11

2

18

17

14

19

8

4

21

18

6

8

7

13

6

10

20

22

6

1

20

11

9

10

2

17

4

18

16

9

1

21

19

16

21

15

15

3

22

19

3

22

between geometric analysis and geographical analysis. The nodes were ranked according to their closeness centrality (distance to the geometric center [GC]) of the urban boundary at that time period. In our evaluation the geometric method was coupled with neighborhood density (Fig. 4-12) and thereafter the nodes were ranked accordingly in five different time periods from 1900 to 2020 (Fig. 4-11). The simulation for future urban growth was taken from a previous study that was done for Lagos using a dynamic spatial model prototype.3 Conclusion Both similarities and greater variances in ranking of the nodes between the time periods were observed.

The nodes in Lagos Mainland have the highest rank due to their density and centrality to the GC of the boundary at each period. However, the nodes in Lagos Island have a lower rank which was expected due to their distance to the GC of the urban boundary.

The ranking was also done for the simulated urban boundary for 2020 which to some extent shows similar results. In addition it takes into account the urban growth and gives higher ranking to the nodes that are expected to become more important both geometrically and demographically.

Node ranking

4-12

Geometric Method

Methods for ranking nodes

C = Geometric center of urban fabric Di = Distance from node i to C Rank Di

Geometric Method coupled with neighbourhood density C = Geometric center of urban fabric Di = Distance from node i to C Ri = Radius of Node i (proportional to its density) Rank Di / Ri

3. Barredo I. Jose; Demicheli Luca. 2003

43

EMERGENT TECHNOLOGIES & DESIGN

44

ADAPTIVE FLUX MORPHOLOGIES

RESEARCH AND NETWORK DEVELOPMENT

5.0

6.1 Slime mould. Physical testing 6.2 Slime mould. Digital simulation 6.3 Historic underground network 6.4 Generation of the network 6.5 Node ranking 6.6 Space Syntax 6.7 Line division 6.8 Station placement

45

SLIME MOULD. PHYSICAL TESTING

EMERGENT TECHNOLOGIES & DESIGN

5.1 90

5-1

90

Test slime mould growth. The slime is positioned in the centre and the food in the edge of the petri dish. It is monitored daily until the network is fully mature. Then the slime pattern is compared with the Gabriel graph (left) and Relative neighborhood graph (right)

60

60

30

30 15

15

0 mm

0 mm

Day 01

Day 03

Day 05

Method

5-2 (Opposite Page) First experiment results. The top set shows the result of the 8 samples tested. At the bottom the evaluation diagrams of each once a mature slime is grown.

46

In order to better understand the growth of Physarum Polycephalum, we carried out several physical experiments using a kit for developing Physarum cultures. The experiments included a pre-grown culture of plasmodium which was used to subculture more plasmodia sample tests. The samples were monitored daily in order to understand the plasmodium’s foraging behavior and also to calibrate its growth behavior to a mathematical model using network graph analysis (Fig. 5-1). Each sample was prepared in a 90 mm petri dish. Non-nutrient agar was first poured into each petri dish. After the agar

was dried, sterile oatmeal flakes were placed on top of the agar surface for each petri dish. A 1 cm2 block of agar on which a piece of plasmodium was present was then cut from the pre-grown culture and placed, with the plasmodium side down, on to the non-nutrient agar plus oatmeal flakes.1 Experiment 1 The first experiment included eight samples (Fig. 5-2).

The observation from this experiment was that during plasmodium’s foraging process the cell senses the 1. Bozzone; 2004.

SLIME MOULD. PHYSICAL TESTING

ADAPTIVE FLUX MORPHOLOGIES

5.1

1 - Sample producing sporangia due to lack of nutrients

5

2

3

6

7- Network extends into 3D.

1

5

4- Mold escaped the petri dish.

2

6

3

7

8

4

8

Relative Neighborhood Graph Gabriel Graph

47

SLIME MOULD. PHYSICAL TESTING

EMERGENT TECHNOLOGIES & DESIGN

5.1 5-3 Experiment 2: Map of Tokyo underground with highest flow stations called out to be used as food sources

C-19 H-21

M-25

Y-09 A-20 N-10

I-10

S-01 E-27

F-16

T-23 G-09 Z-01

5-4

Station No.

Opening Date

~ passengers a day

Station Rankings

F-16

1885

~ 580,367

Z-01

1885

~ 580,367

M-25

1903

~ 470,284

Y-09

1903

~ 470,284

C-19

1943

~ 433,614

H-21

1896

~ 287,488

T-23

1958

~ 271,057

G-09

1934

~ 241,513

N-10

1928

~ 166,452

S-01

1885

~ 133,104

A-20

1960

~ 92,984

E-27

1885

~ 65,607

I-10

1972

~ 62,097

48

SLIME MOULD. PHYSICAL TESTING

ADAPTIVE FLUX MORPHOLOGIES

5.1 existing food sources and makes a decentralized route towards these food sources with a protoplasmic tube.2 It was also observed that the morphology of the protoplasmic network continuously changes and never reaches a fixed, stable point.3 Because this network doesn’t reach a stable configuration, for the next culture, the sample was monitored daily and compared to mathematical graphs such as the Gabriel Graph (GG) and the Relative Neighborhood Graph (RNG) (Fig. 5-2). This was done in order to extract a generalized network from our experiments.

We were aiming for a behavior closer to the RNG since the GG tends to gives a more redundant network. By comparing the experiment with the GG and the RNG, it was observed that the protoplasmic network is a super graph of the RNG and a sub graph of the GG. Experiment 2 After sub culturing the plasmodium and observing the continuous evolution of the plasmodium network in order to maximize its access to the available food sources, we narrowed our experiments to replicate transportation networks. A case study was carried out on the Tokyo subway system. Each line in the network was analyzed and areas of high traffic flow were extracted from the lines which would be represented by food sources in our experiment (Fig. 5-3 and Fig. 5-4). The experiment was again done in a 90 mm petri dish with non-nutrient agar and oat flakes. After the agar was poured and dried in the petri dish, oat flakes were arranged in the pattern of busiest stations in each line of the Tokyo subway system. The 1

cm2 plasmodium was placed on the oat flake which represented Shibuya station which has the highest flow and is one of the oldest stations in the subway system.

5-5 Experiment 2 results

The experiment captures the basic dynamics of a network with an end result that is comparable to the Tokyo subway system. The plasmodium started to consume from its closest food source which is the Shibuya station and the protoplasmic network then started to propagate from Shibuya station towards the North and North-West. After monitoring the experiment for four days, it was observed that the protoplasmic network approximates the Tokyo subway system which validates our method (Fig. 5-5).

In general, it was observed that the structure of the protoplasmic network from the experiments does not depend significantly on the size and shape of the container, nor the amount of agar that is poured after the container is covered, but mainly on the configuration of sources of nutrients.4 Experiment 3 The final experiment was carried out using our site in Lagos, Nigeria as the template for placing the food sources. The test was done in a rectangular container 140 mm 100 mm, fully covered in agar. A schematic map of the previously defined 22 activity points within the urban boundary was produced in order to set up the experiment. Oat flakes were arranged on top of the agar surface in the pattern of the activity points and the plasmodium was inoculated in Lagos Island. This was

2. Adamatzky; Jones; 2010.

3. Adamatzky; Jones; 2010.

4. Adamatzky; Jones; 2010.

90

60

30 15 0 mm

Relative Neighborhood Graph Gabriel Graph

49

SLIME MOULD. PHYSICAL TESTING

5.1 5-6 Physical experiment within metropolitan Lagos.

Activity nodes

5-7 Network pattern from slime mould connections

50

EMERGENT TECHNOLOGIES & DESIGN

SLIME MOULD. PHYSICAL TESTING

ADAPTIVE FLUX MORPHOLOGIES

5.1 5-8 Network Connection Patterns connecting disconnected node

done because the island is where the urban growth started in 1900, and the urban boundary evolved from there. Even though this experiment was carried out using simple assumptions, the protoplasmic network is capable of reproducing the dynamics of network formation that we are interested in through foraging and adaptation.

Node and Line Abstraction The abstraction of the connections between the hubs was viewed as any line that the plasmodium created on the agar. There was visually a difference in the dominating connections between the hubs with thicker and stronger growth that we abstracted as the main paths (Fig. 5-6). After assessing these paths, the lines between the oats was shown and connected. From these results, the connections were abstracted to straight lines to show basic ideas of connection (Fig. 5-7). The resulting network had all

nodes connected except for one. This node was reconnected to the network manually (Fig. 5-8). This resulting network will be used in a later chapter for the purpose of evaluation.

Results This process gave us a method that would be effective for using the physical slime mould tests in order to compare them to later network designs. Through abstracting the connections shown in the network into straight lines, the networks can easily be compared to connectivity graphs and network design solutions.

Therefore using the physical experiments at the scale of the city will provide a powerful comparison and evaluation tool for our further studies.

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5.2 5-9 Test slime mould growth sample and the distribution of “food nodes”

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5.2 5-10 The sensing distance responds to the maximum reach to locate food of each cell that forms the slime mould.

SENSING DISTANCE

5-11 The sensing angle is the range of vision of each cell that forms the slme mould.

SENSING ANGLE < 360

Extracting Parameters During the physical slime mould experiments, we observed the growth and foraging behaviour of the mould in order to extract parameters that could be translated into algorithmic instructions (Fig. 5-9).

These parameters would later serve as the guiding forces for creating a bio-mimetic algorithm based on the behaviours of the slime mould for generating networks. The first important parameter that we observed was a sensing distance restriction. The indirect growth of the network branches suggests that there is a limit to the distance at which the mould can sense food sources (Fig. 5-10). If there were no restriction on the sensing distance, all of the network branches would be straight lines connecting food sources. The second parameter that we observed was

a restriction on the angle that the mould can sense. If the mould had a perfect 360 degree sensing angle, the growth of the network would be unidirectional towards the closest food source. However, the fact that the mould begins its growth radially suggests that there is a restriction on this angle which limits the sensing capability of the mould (Fig. 5-11).

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5.2

Computational Adaptation of Slime Mould Logic After extensive study, the slime mould foraging behaviour was abstracted into an agent-based computational simulation. The plasmodium was abstracted into a collection of individual “cells” (in this case a 3D point in Rhino). Food sources were marked as circles of varying radii which would determine the number of cells spawned from each node (Fig. 5-12).

The cells and food sources were both coded to have a primitive intelligence about their surroundings and how they should behave.

Cell Behaviour Each individual cell has a built-in intelligence. This can be broken down into two parts: a sensing behaviour and a movement behaviour. Each cell begins with an initial start vector from the centre of the node from which it is originating. A random percentage of the cells are assigned a vector towards a major “destination”. In the case of a city, this destination could represent the main direction of travel. The two most essential parameters which influence the sensing and movement behaviours are the sensing angle and the maximum sensing distance. The sensing angle (SA) defines a field of “vision” for each cell. If a food source is within this field of vision, the cell registers it as a “visible” food source. From the list of its visible food sources, the cell then selects the closest one and will then test it against the maximum sensing distance. The maximum sensing distance (SD) represents the proximity within which a food source must be in order to affect the cell’s motion.

What this means is that even if a food source is visible to a cell, the cell will continue along its initial vector until it is within the maximum sensing distance. Once this is the case, the cell begins to move towards the food source (Fig. 5-13). If there are no food sources within the sensing angle, the cell has two alternatives.

The first is that it will check to see if there are any other cells in its field of vision. If there are, it will create a

54

vector to the centre point of these neighbouring cells.

If there is absolutely nothing within its field of view, the cell will rotate at a random angle and continue its search (Fig. 5-14).

Food Source Behaviour Each food source also has a primitive intelligence, which is much less complex than that of the cells. The food sources simply are able to keep track of how many times a cell has passed through it. The purpose of this is so that when we analyze the network that results from the simulation, we are able to see which nodes were used the most, and therefore define those nodes as hubs of the network.

Digital Experiments The first experiments were done in a digital “petri dish” with food sources placed in the same pattern as was used in the physical experiments. The purpose of these initial experiments was not only to ensure that the cells were behaving properly, but also to find the parameter settings that gave the most well defined and well distributed networks. These networks were then compared to the following proximity graphs: Gabriel Graph, Relative Neighbourhood Graph, and the Minimum Spanning Tree (Fig. 5-15), which were described in an earlier section. The performance of the networks (P) were calculated as the length of the minimum spanning tree divided by the total length of the network.

The performance of the network is evaluated by comparing the total length of the network to the total length of the minimum spanning tree graph. The performance ratio is a number between 0 and 1(in a fully connected graph), 1 being the best possible performance. It is possible for this value to go above 1, but what this means is that not all of the nodes in the graph are connected. Graphs that result in a performance ratio that is greater than 1 either need

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SLIME MOULD. DIGITAL SIMULATION

5.2 5-12 Node division and Spawning of Cells

5-13 Behavior of a particle when a food source is present within the cell area of vision.

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Behavior of a particle when no food source is within the cell area of vision.

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5.2 5-15 Connection graphs

Gabriel graph

Relative neighborhood graph

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SD : 70 SA : 30 P : 0.513

SD : 70 SA : 45 P : 0.552

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SD : 70 SA : 170 P : 0.787

SD : 70 SA : 180 P : 1.335

SD : 35 SA : 45 P : 0.502

SD : 35 SA : 90 P : 0.561

SD : 35 SA : 150 P : 0.724

5-17 Second set of experiments

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5.2 5-18 Network connection methods

to be modified or disregarded. In order to modify the graphs, lines can be taken from the other connectivity graphs in order to make a complete network.

Results By varying the sensing angles and sensing distances, we were able to determine which changes in these parameters would lead to which changes in the overall pattern of the resulting networks. First, the sensing distance was kept constant at 70 units. The sensing angle was the only parameter that was varied. The results showed that smaller sensing angles result in networks that are difficult to discern or are overly connected. This means that points are connected to more nodes than are necessary, resulting in a redundant and wasteful network. A sensing angle of 30 degrees resulted in a network in which almost every node had at least 3 lines coming out of it, resulting in a low performance ratio. An angle of 45 began to refine some of the nodes, but was still not very coherent or performative. As the sensing angle is increased the graphs move closer and closer towards the previously mentioned proximity graphs. A sensing angle of 170 degrees led to a very close approximation of the minimum spanning tree and a high performance ratio.

Angles past 170 degrees lead to networks that were disconnected. For example, a sensing angle of 180 degrees produced a result that very well defined, but not fully connected which resulted in a performance ratio over 1 (Fig. 5-16).

In order to provide more direct pathways, the sensing angle was lowered to 150 degrees. This resulted in a network that closely approximated the relative neighbourhood graph which allows for more connections than the MST. This is advantageous because the MST is not always the most logical path for moving through a network. In Fig. 5-18, if a person wanted to go from point B to point D, he would want to go directly there, not from point B to point A to point C and then finally to point D. Allowing for more connections provides a more appropriate network for being applied to transportation.

In the next experiment, angles 45, 90, and 150 were tested with a sensing distance of 35 (half that of the previous experiment) (Fig. 5-17). This had a major effect on the coherence of the resulting networks as well. At 45 degrees, the network created a central, emergent node. This node was not one of the predefined nodes, but rather it was created by the agents of the system. The network showed a similar performance ratio to the previous test. At 90 degrees, the network also showed a similar performance. At 150 degrees, emergent nodes appeared along with a similar performance. In order to link this sensing distance to any network, the relationship we used is Ds = Davg/3 where Ds is the sensing distance and Davg is the average distance between points.

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Conclusions By varying the main behavioural parameters of the computational system, we were able to determine which settings would produce the most effective network solutions. A sensing angle of 150 degrees will be used in the further experiments as it produces networks that perform well, but also provides enough connection so as to provide sensible routes through the network. Also, the chosen sensing distance has great potential as it allows for emergence within the system. Even though the initial nodes are placed by a human user, the system itself creates its own new nodes based on the rules governing its behaviour.

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5-19 From top to bottom and left to right, diagrams of the evolution of London underground network with the redistribution of population density.

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5.3 5-20 Diagram of London tube network with stations in their real relative position

London and Tokyo were our two case studies of focus for existing networks. They are analyzed in terms of spatial implications of the city growth and connectivity within it. London was the first underground network in the world. Tokyo’s underground started with London as a reference but presents a different result. In both examples, the networks present high efficiency and deal with very large populations.

First it must be said that the underground as a transport system cannot be observed as independent from other means of transportation in the city. The presence of bus or railway modifies the flow of users within the system increasing the use of certain lines or hubs. Therefore, an efficient design of the network should have dealt with this dynamic.

London The London underground commonly known as “the tube” opened in 1863 becoming the first underground line built in the world. The initial construction was conceived to connect Euston, King’s Cross, Paddington, London Bridge, Bishopsgate, and Waterloo stations which were the major terminal railway stations within the city. This new means of transportation would relieve the intense traffic in the city from travelers coming to London. As soon as it was inaugurated, the underground was a complete success. As private companies owned the underground, competitiveness soon arose and the network lived through a big boom of development.

London’s underground transport system spreads over the territory in all directions as no geographical barriers are present. The technology to open tunnels under the river was researched in an early state of the network, so the river does not form a barrier for the network or the city layout.

In regards to the spatial evolution over time (Fig 5-19), it is seen that the initial linear construction between stations leads immediately to a spread over the territory. It is interesting that as branches spread outside the city centre an inner circle line is built. This loop line works as an interchangeable line for all the others, which converge in the city centre.

The links between lines happen with two different mechanisms: single point intersections and overlapping lines. With the former, some stations can have a degree of connectivity greater than two meaning that more than two neighbouring stations can be reached from that station. The latter happens both in the city centre, along the inner loop and along some branches. This could be a way to enhance connectivity in areas far away from the city centre where more lines converge, thus introducing redundancy in the service.

This redundancy is necessary to solve accidental breakdowns in the network or increasing the options to arrive at the destination.

The changes of population density with the spatial layout of the network over time show that the network develops and disperses the population throughout

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5.3 5-21 Evolution of Tokyo underground network

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5.3 the area (Fig 5-20), as travel through the territory becomes more accessible. Therefore, the city centre dissipates its density and thus increases the quality of life.

In comparison to London, Tokyoâ&#x20AC;&#x2122;s directional growth is constrained because of its proximity to the Tokyo bay, a fact which results in difference in connectivity regarding London (Fig. 5-21).

Tokyo

The most populated areas in the city form a ring surrounding the geometrical centre, along the bay waterfront and along lines that branch out from the city. On the one hand, the centre of the city contains most of the transport stations in the metropolitan area and, on the other hand, the denser areas that branch out from the city coincide with the underground and the train railway. This highlights a greater integration of areas involving the network due to the influence of this means of transportation.

Currently, Tokyo is considered one of the most efficient cities in terms of public transport with a population of 36.5 million inhabitants in 2009.1 The car was implemented quite late in the city and as a result, the street layout was not designed for them.

Therefore, crossing the city is easier and faster using the train or underground than car. An interesting fact is that the population in the city changes by almost 2.5 million people between day and night, and the average number of commuters per day is 8.7 million people. The transport system is achieving dangerous levels of congestion which in some stations goes up to 199 percent of their design capacity.

1. United Nations DESA Population division; 2009; p.6.

Connectivity between lines is different than the case of London. Comparing Fig 5-24 and Fig 5-25, it is seen that the denser areas present a higher number of stations. Most of the flow of users comes from the outskirts of the city meaning that branches play an essential role in the commuting to the city centre.

The connectivity between lines is not like it is in

5-22 Underground map of New York

5-23 Diagram of Tokyo tube network with stations in their real relative position

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5.3 5-24 Map of the metropolitan area of Tokyo showing the gradiation of densities. Maximum in the city centre (purple) and minimum white.

5-25 Diagram showing the location of stations in the area of the city of Tokyo.

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5.3

London, where lines overlap. In Tokyo, the response to connectivity is done by an increase of stations in the most trafficked areas such as the city centre. This effect is similar to New York (Fig 5-22). Both examples involve high densities and constrained space. In Tokyo, more loops are present in the network formed by different lines, a fact that increases redundancy within the system.

Conclusions In both networks present a high level of use despite a different arrangement in space and connection between lines. What makes a system good and successful in both examples lies in the connectivity with the hotspots and the ability to change overtime.

Changes may happen both in the use of a particular hub, mainly due to change of programs or attraction points in the city, and by extensions over time. The former is based on the ability to change service in the network and changes in the hubs to accommodate a bigger or smaller number of users. The latter is done by the extension of the branches. Redundancy allows different choices of paths which enhances possibilities of mobility.

Another issue shown within these two networks is the level of congestion. Usually, the network is designed expecting the increase of users for 20 or 30 years. In both examples the system runs at over 100% capacity.

This is translated into significant reduction of comfort in its use or delays in the system. The question is how can a network change its service to address major changes in use without compromising comfort?

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GENERATION OF THE NETWORK

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5.4 5-26 Minimum spanning tree graph

Minimum Spanning Tree Performance Factor: P = lenminspan / lennetwork 5-27 (left) Network 01

5-28 (right) Network 02

P = 0.853377968776 Disconnected Nodes

P = 0.735245379795 North Not Well Connected

P = 0.630844436567

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5-29 (left) Network 02-a North / south Option 1 5-30 (right) Network 02-b North / South Option 2

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5.4 Generating the Lagos Network In order to create a network for implementation in Lagos, it was first necessary to determine what the major nodes in the city are. Through our research we were able to designate 22 activity points within the city.

A circle was placed at each one of these points on the map. The radius of the circle was proportional to the density of the specific neighbourhood in which the node fell. This affected the number of cells that would be spawned at each node as was described before.

From these 22 nodes, we ran an initial simulation using the slime mould algorithm we had developed using the parameters that we had extracted from the generic digital experiments.

Emergence in the System This initial simulation led to a well-defined network, but also showed evidence of emergent nodes developing.

Despite the limited intelligence of the individual agents in the system, they occasionally create new and unexpected changes in the system. There was one node in which a clear branching structure had appeared creating a new node. Another anomaly was that one node was connected to the network but the path there was poorly defined. Our conclusion from this was that we should rerun the simulation but add in new nodes at the branching locations and at these areas of low resolution. We believed that this would produce a similar network, but would be cleaner and better defined. The result of this second run of the simulation confirmed our hypothesis. The resulting network was virtually identical to the previous one, but at the points where the first simulation had suggested new emergent nodes, the network was much cleaner and more legible. The new challenge was extracting an effective network to be evaluated for its performance factor (P) as was done in the earlier experiments (Fig. 5-26).

routes, and two North/South routes which spanned the whole extent of the bounding area. However, the third North/South route only went halfway up the coast line.

In order to address this, we produced two alternate networks with different lines added to complete the third North/South route. (Figs 5-29, 5-30) Each one of these was evaluated and compared. The resulting network (2b) had a P value of .670, all nodes were connected, and all directions in the network were well serviced.

Physical Simulation The last evaluation we ran in order to validate our system was to compare the digital simulation to a physical slime mould test. We prepared a slime mould culture overlaid on a map of Lagos with oat flakes placed at the same activity points as in the digital test.

We then brought the network that resulted from this test into the digital environment in order to evaluate it for its P value. Because of the complexity of the slime mould network, we extracted the most important lines from the network. The first evaluation returned a P value of 0.827, but there was one disconnected node.

After connecting this node to the network, the P value was 0.724 (Fig 5-31). These similar results validate our algorithmâ&#x20AC;&#x2122;s ability to create comparable solutions to real slime mould simulations.

5-31 Modified slime mould resulting network

Evaluating the results Two separate networks were extracted from the simulation. The first network was the network that resulted at the end of the simulation (Fig 5-27).

This network was very well defined but had more or less abandoned several nodes. As a result, when this network was evaluated for its P value, it had an extremely high number (.853). This number is misleading, however, because several nodes are not connected to the network. Because of this, we decided to extract a second network from midway through the simulation. In this network, all of the nodes were connected to the network (Fig 5-28). We then evaluated this network for its P value and got a result of .735. However, since this network was to be used as a transportation network, it was important that there be good access to all parts of the network. This network was effective in this respect except for in the North/South direction. It had three clear East/West

P = 0.72420912472

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NODE RANKING

EMERGENT TECHNOLOGIES & DESIGN

5.5 5-32 Simulated Slime Mould method the number represents the number of times the cells pass through the nodes.

Ranking Comparison As the simulation ran, it took into account every time a cell passed through a node (Fig. 5-32). At the end, the nodes were ranked based on this traffic count.

These ranks were then compared to the original ranking based on the geometry of the urban area and the neighbourhood densities (Fig. 5-33). The results showed that the rankings were similar uniformly, except for the nodes on Lagos Island. The digital simulation ranked them much higher than did the geometric/neighbourhood density method. However, these nodes were expected to be anomalies in the rankings because of their isolated location on the island. As the island cannot expand as the urban fabric grows, the island nodes will be further and further away from the geometric centre of the urban fabric. These nodes still remain extremely important to Lagos, however, because the central business district is still located here.

Conclusion By comparing these different methods of evaluation, we were able to validate the strength, accuracy, and potentials of our system. The performance evaluation of both the digital network and the physical slime mould network show that the digital system is able to produce similar network outputs to those from the physical tests. The ranking comparisons show that the agents within the digital system tend to use the various activity points in a fashion that is similar to the rankings of importance from the geometric density method. Based on these findings, the digital output of the system can be evaluated as part of the greater urban fabric of Lagos.

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NODE RANKING

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5.5 5-33 Rankings from the simulation compared to the geometric / density rankings over time

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5.6 5-34 (right)

R2 = 0.0233

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Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Intersection with transport network

Connectivity

Connection Line

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos 5-35 (left)

Integration [Hh]

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Linking transport network to street layout. In order to make a multi-modal network, the public transport network was linked to the Lagos axial map by finding the nearest axial line to the public transport node.

Integration [Hh]

Lagos is the former political and current commercial capital of Nigeria.1 Lagos’s start came with the boom with the oil industry, in the 1970’s so it has infrastructure that most big cities in Africa are lacking.

The contrast is visible in Lagos; it has elements of a modern city but also a very strong presence of the “informal”.2 Lagos is a coastal city with an estimated annual growth rate of 6 percent.3 In 2025, Lagos will be the eleventh largest city in the world.4 Some parts of the city act as self-organizing entities, like the Alaba International Electronics market. There are also some self-generated neighborhoods which are referred to as “white spaces”. These are places that are blank on the map, that appear to have no activities, but that are actually busy and productive.5 The combination of Lagos’s huge size, underperforming urban infrastructure, and high levels of poverty became a compelling reason for us to analyze and enhance the accessibility and integration of neighborhoods into the wider urban fabric and life of Lagos.

1. Immerwahr; 2007.

2. Felix, Wolting (Producers) & Van der Haak (Director); 2005.

3. World Bank, 2011.

Urban development of Lagos dramatically increased during the 1970’s due to the boom of the oil industry.

Three main bridges were built in order to connect the Lagos mainland to Lagos Island and Victoria Island.

The spatial structure of the city was changed after the development of these bridges became modernized according to a fairly conventional vision of what a modern city should look like in the 1970’s. However, after the capital of Nigeria was moved to Abuja in 1991 it was almost as if Lagos started developing in reverse of the direction it was intended.6 The focus of this analysis is to understand the movement and accessibility of the street network within the urban form of Lagos. In order to analyze the proposed network and to evaluate its effect on the urban fabric, a model had to be developed to reflect the effect of different degrees of public transportation networks integrated within the urban fabric. For this analysis, the theory of natural movement is applicable to the exploration of the spatial pattern which is analyzed through the use of Space Syntax using Depthmap software.

The theory of natural movement is the relationship

4. Siemens; p. 6.

5. Felix, Wolting (Producers) & Van der Haak (Director); 2005.

6. Immerwahr; 2007.

5-36 (Opposite page) Lagos existing urban fabric space syntax analysis.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

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Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 01.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3243

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between the structure of the urban grid and movement densities along lines.7 The structure of the city itself accounts for much of the variation in movement densities. The colors in the axial map of Lagos represent densities of moving people which is expressed with integration values. In other words, the theory suggests that movement is fundamentally a morphological issue in urbanism. In order to have sufficient and well used urban spaces, local properties of the space such as their form, size, and physical components are not as important as its configuration in relationship to the wider urban system. The configuration of the urban space and its relation to its integrated networks is the main generator of the movement patterns and not the local properties and attractions such as shops and offices.

These attractions are located to take advantage of the opportunities offered by the spatial configuration.8 Applying Space Syntax One of the most effective set of theories and associated tools currently used in a number of locations is Natural Movement Theory and Space Syntax, which 7. Hillier; 1996; p.120.

8. Hillier; 1996.

are applicable to investigate the relationship between spatial configuration and movement.

In order to analyze the Lagos urban structure, an axial map of the city was produced. In Depthmap, the interaction of the different mobility networks and their effect on the urban configuration was investigated.

Several measures such as connectivity and integration (both global and local) were investigated and the correlation of these measures was compared for different networks to see which network gives a better correlation value and is therefore more intelligible.

The spatial agglomeration of the urban form was evaluated at different scales by calculating integration according to different radii. For analyzing the global integration the radius was equal to n and for the local integration the radius was equal to three. A radius of n means analyzing the whole system where as a radius of three takes everything up to and including three turns from the base line.

Lagos Urban Fabric The distribution of global and local integration in the Lagos axial map is shown in Fig 5-36. It shows that the city expands from the south to the north and then spreads into the self-generated neighborhoods

5-38 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 01.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

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Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3345

Integration [Hh]

towards the east and west of the city with lower integration and accessibility values. The white spaces and the self-generated neighborhoods are the most segregated areas. One of the problems that led to this is their isolation as a result of poor street connectivity, which makes these areas not function properly within the urban system. The map also confirms that Lagos has a centralized Business District which is in Lagos Island. There is high integration from Lagos mainland towards the island which shows where the highest flow of movement is concentrated.

The spaces coloured red are the ones that benefit most from spatial agglomeration. The disadvantage of spatial agglomeration is congestion. One factor that causes congestion is the lack of spatial connectivity which concentrates all the traffic into a few routes.

The difference in spatial measures of different urban environments is linked to their multi-scale transport systems. For example, connectivity has a high level of correlation at low radius with pedestrian activity and it gets higher as the degree of transportation gets higher, going from cycling to vehicle, to bus, and to tram and metro.

The spatial and functional characteristics of Lagos were also considered. Some parts of Lagos suffer

from spatial isolation and accessibility problems and lack the infrastructure to support the rate of population growth. Therefore, we want to propose a new rail network within the urban fabric that will take into account the inner-structure of areas suffering from spatial isolation and integrate them to the surrounding urban fabric. By increasing the connectivity of these areas, it will inherently attract more people and stimulate regeneration as well as easing the stress on the existing transportation infrastructure.

The axial line map reveals that urban street networks usually consist of a very small number of long lines and a very large number of small lines. In the axial line map of Lagos, 33% of the lines are above the average line length and 67% are below the average line length.

The most integrated routes are among the 33% and are located in or near the geographical center of the city. The distribution is not homogeneous and shows the strongest and most dense agglomerations in red.

Comparing these results to the literature of Lagos confirms the reliability of this approach.

When adding a rail network to an existing urban fabric, the new network will tend to become the most integrated route. If this network were a road system, this could lead to congestion of traffic, but since it is

5-40 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

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5.6 Integration

0

1

Network 02-a layout

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5.6 5-41

R2 = 0.0824

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-a.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3468

Integration [Hh]

a mass transit system, this is not a problem because it is designed to attract high usage. As a result, areas around the network and the hubs have more commercial opportunities that can be created and as a consequence, more movement can be attracted to these areas. Integration is the predictor of movement and an effective criterion in studying the notion of accessibility and spatial isolation. Integrating the network within the urban fabric distributes the integration and accessibility over the whole area and makes connections with other integrated streets.

To integrate different layers of transport networks to the urban fabric, an additional layer was added to each transport network which is referred to as the “modal interface” connecting the different modes.9 The “modal interface” is a link between the stop/station of different transportation networks and the axial map.

It links the additional networks to the axial line map by finding the nearest axial line (Fig. 5-35). For the BRT network the links are added at the locations of the 26 bus shelters and for the rail networks the links are added at the location of activity points.

Construction of the multi-modal transportation model

Once the multi-modal network has been modeled it can be translated into a graph and be further analyzed. The network was then imported into Depthmap as an axial line map and evaluated according to the measures. The connectivity, global and local integration measures were evaluated for the proposed rail networks for comparison.

The different networks were produced as individual models, each including the BRT network. The configuration of these networks was then analyzed, using bus stops and the main stations of the rail networks in order to link them accordingly to the urban fabric.

The network models presented can be combined in an integrated multi-modal network model in order to study the interactions of the different mobility networks and their effect on urban configuration and movement levels.

Comparing the proposed rail networks From the results it is obvious that by adding another level of transportation to the existing layout of the city the distances, connectivity and integration measures

5-42 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-a.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

9. Gil; 2012.

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5.6 Integration

0

1

Network 02-b layout

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5.6 5-43

R2 = 0.0848

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3521

Integration [Hh]

in Lagos would all change (Figs. 5-38, 5-40, 5-42 and 5-44). Intelligibility (R2 for global integration vs local integration) measures can be obtained from the addition of different networks (Figs. 5-34, 5-37, 5-39, 5-41 and 5-43).From the graphs it can be seen that the correlation between local and global integration of the Lagos urban fabric is 0.057 and with the integration of the BRT network together with networks 01,02, 02-a and 02-b it increases to 0.3243, 0.3345, 0.3468 and 0.3926 respectively. Also, the correlation between the degree of connectivity and global integration gives similar results. It increases from 0.0233 from the Lagos urban fabric to 0.0803, 0.0815, 0.0824 and 0.0848 for the integration of the BRT network together with network 01, 02, 02-a and 02-b respectively.

The results confirm that network 02-b is the more intelligible among the other networks.

Impact Maps In order to further analyze the impact of the different networks on the urban fabric, the difference in integration (impact) of every axial line of the two models was calculated and impact maps were produced. The impact values are scaled between 0-1 for comparison purposes and the differential represents a change in integration ranking with the introduction of the public transport networks.10 Fig 5-45 shows how the integration impact increases with the addition of the networks starting with the addition of the BRT network, then the BRT network with networks 01, 02, 02-a and 02-b. Fig 5-46 is a chart which shows the percentage of the urban fabric that

5-44 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

10. Gil; 2012.

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5.6 5-45

BRT network

BRT + network 01

BRT + network 02

BRT + network 02-a

Impact maps of all the different case scenarios analysed.

The integration impact shows the difference of integration regarding the existing situation in Lagos.

BRT + network 02-b

Integration Impact

0

78

1

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5.6 100

5-46 Chart showing the improvement of the overall integration in the different scenarios tested.

50

La go s

s

s

go

go

La

La

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go

go

La

La

+

+

+

+

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100

5-47 Chart where the integration impact percentage in the different scenarios is shown for every street group.

50 BRT BRT+ 01 BRT+ 02 BRT+ 02a BRT+ 02b

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was improved. Fig 5-47 is another chart which shows the distribution of this change broken into different ranges of percent change. The final network (network 02-b) has improved the integration of 41% of the urban fabric and in comparison to the other networks shows a better impact. Therefore network 02-b was chosen for further analysis.

After the implementation of the rail network, the isolated neighborhoods become more integrated within the urban fabric and become more accessible allowing for the integration to be distributed within these neighborhoods.

Conclusion By comparing this method of evaluation with the results of the performance evaluation that was done against the minimum spanning tree, similar results can be observed. Network 02-b in the performance evaluation gave the best result. In the DepthMap evaluation, network 02-b gave a better correlation in terms of integration and connectivity and resulted in the most intelligible network. It also proved to have the highest impact on the urban fabric improving its integration by 41%.

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5.7 a

5-48 Test 1 using simple lines

b

c

j

a b c d e f g h i j

i

a b c d e f g h i j

d

g

h

e

2 1 3 3 2 3 3 2 2

2 2 2 3 2 2 1 1

3 3 2 2 3 2 2

3 2 2 3 1 2

2 1 1 2 2

2 2 1 2 2 2 3 1 2 1

f a

5-49 Test 2. Line overlapping increases connectivity in the network

b

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1 1 2 2 2 2 2 1 1

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2 1 1 2 2

2 2 1 2 2 2 2 1 2 1

Network Division A basic network morphology must be broken down into individual lines in order to develop it fully into an urban transportation network.

Case Study References First, the case studies offer insight into how the shape and layout of the different lines affect the connectivity between them. As observed in the evolution of the London network, or any other underground network, lines are always subject to the cityâ&#x20AC;&#x2122;s unpredictable evolution with no fixed rules determining its growth.

Nevertheless different mechanisms can be observed guiding the growth of the network.

In London, tube lines were first built to link stations and to diminish the road traffic of travelers inside the city. It is observed that in the first section that was built, many lines overlap due to the increase of

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5.7 5-50 Line interconnection mechanisms found as a result of the analysis of London tube's network evolution

Connection of main nodes

Line overlapping and branching

New lines connected to destination stations

If branches go close, overlap lines

Line-ends overlap

Create lines through areas with no connections

Add lines to station with train connectivity

Temporary connections

Lines that run in parallel stop in different stations

travelersâ&#x20AC;&#x2122; demand and business opportunities. This initial line layout was extended and modified rather than adding new lines. Lines eventually branch out and connect more areas of the city. Interestingly enough, the branches first spread out from the city rather than inside the city. A loop surrounding the city centre was very soon finished. After the inner circle is built, new lines intersect the inner circle increasing interchangeability in the centre part of the network.

Remarkably, the line overlapping is also present away from the city centre. When branches of different lines grow and get closer they share the same pathway.

Division of Lines Based on the London case study, we used similar strategies to divide the network into individual lines (Fig. 5-48). Instead of a single circle line as seen in London, our network presents three different independent cycles. This functions in a similar fashion as a circle loop. The criteria chosen to evaluate the

line layout are based on the number of lines used to go from every terminus station to every other. A limit of two is imposed as observed in mature underground networks such as London and Tokyo.

Modifying The Lines The first analysis reveals a required number of trips above the maximum (Fig. 5-48). Thus, the network connectivity must increase in order to improve. The way of doing this consists of extending branches of existing lines and overlapping them where connectivity is weak (Fig. 5-50). These extensions are placed in the destination lines which serve areas whose expected future density is increasing considerably. The second iteration shows successful results and this line layout is selected for further development (Fig. 5-49).

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5.8 5-51 Method 01 District Density Method

District

5-52 Method 02 Equal Distribution Method

District

23%

5-53 Method 03 Combination Density + Equal Methods

District

Station Placement Methods

23%

+

Our aim was to define methods of spatial organization based on networks. By allocating stations we wanted to develop a relationship between the network and an achievable passenger rate of the population in each district. With these relationships, we could then focus on how to address the integration of the system into the urban fabric in terms of time relative to how people might use the network.

The initial study of station placement methods provided design methods that held to set relationships within a network. In both methods used below, the assumptions are from existing charts that relate total train length, line capacity and speed. The first method placed the stations based on the densities of the local government districts considered to be the Metropolitan area. This method was used to test the amount of stations that would be necessary to move the populations commuting daily.

The second method was based on an equal distribution of stations running at a maximum capacity on a single line. From the guidelines in the Rail Transit Capacity Guidelines, Part 3, a basic spread sheet

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5.8 5-54 District density method

1 100%

100%

80 %

30 %

2

2 Achievable line capacity. The train length is determined by total passenger demand and capacity.

3 Station spacing. Assumes stations needed from density projection. Provides speed to re-evaluate the spacing.

3

Total length of train

1 The estimated total population in 2020 is at 25 million.

The a percentage of this population to use transit is assumed to be 80 percent.

Of this 80%, 30% is assumed to use the rail network.

Total track length

was provided that allowed for easy calculation of both methods. In both methods, the total track length is not considered as it is not a factor in the relationship of capacity at this stage. Track length was only considered in the distance between a single track lengths between stations. Both methods design for a fully mature system that is expected to be reached 10-15 years after completion of construction.

The district Density Method -Population Capacity Estimation The first step in this method was to calculate the amount of population that would be commuting by rail. This was assumed by taking the populations of each d and relating it proportionally to the total population that was assumed to commute. From other case studied systems, 30% appeared to be an informed estimation (see appendix 8-4). This percentage also worked well with the projected numbers from the LUTP (Lagos Urban Transit Project) which assumed 27% of the population would use the new bus rapid transit after initiation. This 30% of the population assumed to be taking the rail, was from the 80% of the total commuting population that would use public transportation (Fig. 5-54).

Agege Pop: 28,464

8 stations to accommodate 28,464 people

5-55 Example of the district density method applied in Agege district.

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5.8 5-56 Station placement of district density method estimated a total of 58 stations to accommodate 5,290,815 passengers daily.

Ifako-Ijayi 32,468 +1 Agege 28,464 +8

Ikorodu 34,994 +1 Kosofe 57,206 +10

Ikeja 23,766 +4 Alimosho 133,377 +8

Shomolu 28,358 Oshodi Mushin 38,866 Isolo 39,076 +4 +1

Ojo 45,383 +0

Surulere 34,013 +1

Amuwo Odofin 24,143 +5

Lagos Mainland 27,678 +2

Lagos Lagoon

Lagos Island 15,892 +2

Apapa

Ajeromi

42,625

+3

84

+4

16,494 +2

Eti-Osa Eti-Osa

38,550

Victoria Island +3

STATION PLACEMENT

ADAPTIVE FLUX MORPHOLOGIES

5.8 5-57 Achievable design capacity equation.

This equation determines the design capacity based off the trains per hour and the passengers per train.

Design Capacity C D = C L + CT where: CD CL CT

RESULTS: =design capacity (p/h); =line capacity (trains/h); and =train capacity (p/train).

-Vehicle length related to capacity With this estimated amount, the total vehicle length was calculated (see appendix 8-5). The track lengths were then measured based on the intersection they held with each respective district. In some cases, there was more than one track within one district, the total length of all the track was calculated in the division for stations.

-Station allotment per District density To calculate the number of stations, the guideline of a station range was used in relation to the density of a district. The suggested range was from 1/2-3 km as a minimum and maximum. By breaking this range into three categories, and then taking the district densities from least to greatest, the two were set in relation to each other (see appendix 8-6). The lower third of the district densities would receive the largest spacing distance in the range of station placement (Fig. 5-55). The expectation of this method was that if this step of density allocation worked then the next application would consider further refinement based on placement to density at a more detailed range.

-Speed of flow / Re-evaluation As the distance between stations and the train length was known, the speed was then referenced against a chart that provided an estimation of the station placement. Generally, the results came within a few 100 metres of the chart which meant the estimation was correct and the method chosen worked within the relationships set in the spreadsheet.

Conclusion of District Density Method The benefit of the district density method was that it became clear which areas would need more transportation and the frequency of stations necessary per line (Fig. 5-56). However, issues arose in estimating sizes for the platforms in relation to the

3,371,200 PASSENGERS DAILY 27.49% OF 80% USING PUBLIC TRANSIT USE THIS NETWORK

flow of passengers. Using the methods for platform design from the London underground guidelines, the space required for the platforms was in relation to the total train length and peak one minute platform load, a ratio of the total percentage of people per hour load. Using these estimations for the platforms, the dimensions were largely out of scale in terms of typical platform design. For example, one of the stations measured 229 metres in length and 13 metres in width to accommodate a peak one minute load of 1,080 passengers.

This result was an extension of the first assumption in the method; that entering the total passenger count would result in a realistic train length. However, the peak minute passenger count should have been used for the design as in the guidelines. The other factor that could have contributed to this was the assumed percentage of passengers that would take the rail network. Our assumption was based off projections from the proposed study for the Lagos BRT, which could conceivably handle a larger percentage of the population by adding more vehicles. Because we decided the total amount of passengers without a guide for what the network could conceivable carry, the train lengths were incorrect and therefore caused the platform design to be disproportionate to what would normally be expected.

Another issue that arose was the intersection of hubs and stations. As the activity hubs were set from the provided map of the BRT, they were used in the initial steps for developing the network lines. Therefore their placement was critical to the location of optimised network. When dividing the lines for station placement, the location of the hubs was not considered and sometimes a station and hub would overlap or be adjacent by a few 100 metres. In these conditions, we assumed that the spatial pull of the hubs would remove need for two stops, and condensed them into one.

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5.8 5-58 Station placement of Equal Distribution Method estimates 59 stations for 3,371,200 passengers.

Ifako-Ijayi 32,468 +0 Agege 28,464 +5

Ikorodu 34,994 +1 Kosofe 57,206 +10

Ikeja 23,766 +8 Alimosho 133,377 +9

Shomolu 28,358 Oshodi Mushin 38,866 Isolo 39,076 +2 +3

Ojo 45,383 +0

Surulere 34,013 +0

Amuwo Odofin 24,143 +6

Lagos Mainland 27,678 +3

Lagos Lagoon

Lagos Island 15,892 +2

Apapa

Ajeromi

42,625

+2

86

+3

16,494 +2

Eti-Osa Eti-Osa

38,550

Victoria Island +3

STATION PLACEMENT

ADAPTIVE FLUX MORPHOLOGIES

5.8 5-59 Equal distribution method.

1 1 The estimated maximum length vehicle length was for four rail vehicles and at 400 feet.

Maximum length of train

2 This steps estimates the total trains per hour based off the length. A total of 47 trains was assumed for vehicles at 400 feet.

52 51 50 49 48 47 46 45 44 43 42 41 40 39 38 37 36 35 34 180 m 600 ft

54,000

Capacity (Passengers / Hr / Track)

Trains / H

3 Capacity of passengers per track. From the train length the maximum number of passengers is assumed at 28,000 people per hour per track line.

47 TRAINS / H

150 m 500 ft

120 m 400 ft

90 m 300 ft

60 m 200 ft

50,000 46,000 42,000 38,000 34,000 30,000

LIGHT RAIL

26,000 22,000 18,000

HEAVY RAIL

14,000 10,000 180 m 600 ft

150 m 500 ft

Train Length

Equal Distribution Method The equal distribution method uses the design capacity formula from the Rail Transit Capacity Manual (Fig. 5-57). This equation determines the design capacity based off the maximum trains per hour and the respective passenger capacity of these trains.

-Passenger Capacity For this process, the first step was to assume the maximum length of a rail vehicle and then find the total capacity of passengers based on this length.

This design method was used twice, the first attempt assumed a train length with light rail restrictions and the second with heavy rail. This decision was based on the assumption that a heavy rail system would need to be design below grade. However, after reviewing the case studies and examples of locations of their rail tracks, it was decided that both heavy and light rail could be used above or below ground, and the

28,000 PASSENGERS / HR / TRACK 120 m 400 ft

90 m 300 ft

60 m 200 ft

Train Length

deciding factor should have been the percentage of the population that is expected to use the rail system.

Therefore the second attempt assumed capacities of a heavy rail system.

-Maximum train length The first step in this method was to assume the maximum train length (Fig. 5-59). In the first attempt, we assumed the maximum length of four light rail vehicles at 150 metres as suggested in the guidelines. This could accommodate about 27% of the commuting population, which is about 3.3 million people daily. In the second attempt with heavy rail restrictions, the assumed length of the train was 200 metres at eight vehicle lengths. This accommodated 31% of the population at around 3.8 million people daily. The change in the train vehicle length affected the maximum number of trains per hour, which resulted in a slightly different waiting time between each station.

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5.8 5-60 Station Placement of Combination Method

Ifako-Ijayi 32,468

+1

Agege 28,464

Ikorodu 34,994

+8

+1 Kosofe 57,206

Ikeja 23,766

+10

+4 Alimosho 133,377

Shomolu 28,358

+8 Oshodi Mushin 38,866 Isolo 39,076 +4

+4

+1 Surulere 34,013 Ojo 45,383

+0

Amuwo Odofin

+1 16,494 Ajeromi

42,625 +3

5-61 Expected daily ridership of each designed line in a day 329,362 users / day 645,722 users / day 302,560 users / day 635,426 users / day 222,277 users / day 571,811 users / day

88

+2 Lagos Island 15,892 +2

Apapa

24,143

+5

Lagos Mainland 27,678

+2

Eti-Osa

38,550 +3

STATION PLACEMENT

ADAPTIVE FLUX MORPHOLOGIES

5.8 -Rate of trains The second step was to determine how many trains could run based on the total vehicle length. In the first attempt the estimation was at 47 trains per hour. The second trial estimated about 45 trains per hour. The change to the lines was represented as a few seconds between the stops. For the 150 metre length the time between stations was 280 seconds, with a top speed at 80 kilometres per hour. For the 200 metre length trains, the time between stations was 282 seconds, with a top speed of 120 kilometres per hour.

-Station Placement The third step assumed the maximum distance without delay on a line, of 2.4 kilometres per hour. This distance was then evenly distributed over all the lines.

In the case where hub and station collided, the station was removed as in the previous method.

-Conclusion Equal Distribution Method The benefit of the equal distribution method was that the realistic capacity of the network was determined as a ratio of the equation and not as an assumption as in the previous method. From the first trial, the population that could be estimated to use the system in a light rail capacity was around 27%; 3% less than what we had assumed. This 3% however, was enough to cause massive proportion issues in areas of high density and flow. The other benefit in using this method was to see the comparison between a light rail and heavy rail system. The shortcoming to this method was that the frequency of stations was not distributed in areas where there was more of a need for stops. In terms of platform design the ratios of this method were still off in width, but the lengths were now all set the same as a default value which reflected the capacity being the first decision in the process.

Combination method: Equal Distribution and Density Methods After considering the outputs and benefits of both methods we decided to revise the method and design for a network that was based off neighbourhood density and train capacity. This would allow for a reasonable flow at stations while still dispersing different concentrations of stations throughout Lagos.

-Achievable Capacity The inputs for this method used the maximum train length, which we calculated from the rail capacity manual as 200 metres. We used the default loading level of 8 square metres per passenger. These relate to the Fruin levels which are a measure for pedestrian spatial requirements that measure the relationship between the density of groups of people and the

speed with which they can move or circulate.1 This is also the typical assumption used when designing for a rail network.

-Passenger Capacity per line From this we set the operating margin, dwell time and signalling headway at the minimum defaults to get the maximum capacity of the network. These inputs provided a total of 59,800 passengers per peak hour direction and a total of 46.8 trains per hour. The total headway between trains at peak hours was 77 seconds. This relationship provided us with the total people and trains per line which we then used to determine the station spacing.

-System Capacity We then applied the total passengers to each of our six lines to get a total population that could be moved of 2.8 million for the whole system. This was calculated by taking the projected population for 2025 and then finding the 80% that will be taking public transport.

From this 80% we then apply the number the system can handle and get the percentage of people the rail system could accommodate at full maturity. We found this number to be 23% of the projected population.

-Station Placement Using the single line capacity relationships, we assumed a maximum distance between the stations as 2.4 kilometres. With this we were then able to assume the speeds and time between trains at the peak hour.

Then to relate the stations to the density of the areas in which they were located, we categorized the stations based on the populations of the neighbourhoods.

For example, we took the red line and measured the segments of each portion that fell into each of the districts. We then counted the number of stations that were in each area. Using the same ratio as before to assume the projected population that would be using the rail, we divided this amount into each station. This was then reduced down to the rush hour count which we assumed to be eight hours a day from 6am-10am and 4pm â&#x20AC;&#x201C; 8pm. These numbers were necessary so that we could begin to design the proportions of the platforms to accommodate daily flows.

Conclusion of Combination method We found this combination method to provide results for station platforms and flows that fell within the guidelines of standard practice. Our aim to reach an achievable capacity to use for design that would compensate for speed and frequency, with which then could be understood at a detailed level in relation to the district densities.

The areas for further refinement are the levels of 1. Network Rail; 2011; p. 12.

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5.8 5-62 Comparison of the Northern Line of London with the proposed yellow line for Lagos

London Underground Northern Line

Lagos Network Yellow Line

+ 58 km line length + 50 stations + 567,950 daily passengers

+ 28 km line length + 27 stations + 645,722 daily passengers

5-63 Busiest stations from daily passengers

4,000,000 3,640,000

3,500,000 3,000,000 2,500,000 189,4256

2,000,000 1,500,000

1,130,567

1,000,000 500,000

90

1,800,000

79,894

57,000

Busiest Lagos

Waterloo London

125,810 Cuatro Caminos Mexico City

42 Street Grand Central New York

Gare Du Nord Paris

Gangnam Seoul

Shinjuku Tokyo

STATION PLACEMENT

ADAPTIVE FLUX MORPHOLOGIES

5.8 5-64

Charact erist ic

Examples

Walking speed

In larger cities of East Asia and Europe (e.g. Singapore, Copenhagen, Madrid), average walking speed can reach 1.7 m/s, whereas in cities in the Middle East (e.g. Manama, Amman, Damascus, Dubai), people walk more slowly, at 1.0 - 1.2 m/s

Walking side

People tend to walk on the same side of a passageway as their traffic flow. Left-hand traffic in 75 countries, including U.K., Australia, Thailand, India, Japan, South Africa; right-hand traffic in 164 countries.

Social distance

Asian and Latin people accept closer distance than western cultures. On the other hand, in Arabic countries social distance seems to be greater.

Age distribution

Western countries have a larger percentage of elderly people.

Prevalence of disabilities

In high-income countries older people make up a greater proportion of the population but have lower levels of disability than their counterparts in low- and middle-income countries. Moderate disability rates are similar for males and females in high-income countries, but females have somewhat higher rates of severe disability.

N ew user s

In so m e count r ies t h ere are peo ple w h o h ave n ever used an escalat o r or elevat o r.

Moving aids

In some countries, disabled people rarely use wheelchairs, which are commonly used in western countries.

Pr am s

In so m e cult ures pr am s are n ot used at all.

Bicycles

In som e count ries, bicycles are used an d t ranspor t ed in t h e subw ays m ore t han elsew h ere

Waiting time

In East Asia people do not mind waiting as much as in western countries.

Ride comfort

In East Asia people prefer ride comfort inside the elevator whereas in North America elevator efficiency (e.g. higher elevator acceleration) is preferred.

Walking on escalators

In many countries, one side is for standing, and the other for walking. However, in some countries (e.g. India), people stand on both sides, and only the first few people walk up the escalator.

spatial congestion as suggested by the Fruin levels.

Applying the default criteria as an assumption works based on Western standards of congestion and the site conditions of passengers in Lagos was not considered as it is not known (Fig 5-64). This relationship could be explored with further information regarding the type of users, which transportation mode they are connecting to, and the acceptable cultural proximities of passengers.

Station Placement Summary Conclusion The methods applied assumed relationships currently used by engineers in the infrastructure design of stations. By applying these methods to develop a network we found that our yellow line with a capacity of 645,722 passengers daily would have the same flow as of the northern line in London with 567,950 passengers daily. Within the network we also found that our busiest station with 79,894 passengers daily had similar flows as Waterloo station in London with

Passenger characteristics per culture from the Planning Guide for People Flow in Transit Stations. 2009. p 12.

57,000 passengers daily. These results validated that our methods predicted for a rate of passengers that is attainable in similar megacity networks.

We found the application of these techniques a success as they could feasibly accommodate for flow in a successful way in order to design for a network.

The achievable capacity method in conjunction with the district densities provided the desired output, which was an estimation of the projected population within the capacity of current transportation means.

We see the potential of this method to explore the conditions at each node within the system and its affected urban tissue based on passenger flows.

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5.8 Integration

0

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Network 02-b layout plus all stations

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5.8 5-65

R2 = 0.0853

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b plus all stations.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh] R3

R2 = 0.3926

Integration [Hh]

5-66 (Opposite page)

The network was again analysed for integration including the 79 stations (Fig. 5-66). Intelligibility measures can be obtained from the graphs in Fig 5-65. It can be observed that the correlation between local and global integration of Lagos urban fabric coupled with the network and its 79 stations is 0.3926.

From Fig 5-65, the correlation between the degree of connectivity and global integration is 0.0853. These values confirm that the intelligibility of the network increases with the placement of the 79 stations.

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b plus all the stations.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

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STATION PLACEMENT

5.8 5-67 Impact map for BRT + network 02-b + all stations

Integration Impact

0 94

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5.8 100

5-68 Chart showing the improvement of the overall integration in the different scenarios tested.

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5-69 Chart where the integration impact percentage in the different scenarios is shown for every street group.

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BRT BRT+ 01 BRT+ 02 BRT+ 02a BRT+ 02b BRT+ 02b + All stations 040 0 45

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With the addition of the 79 stations, the network was again analysed for its impact (Fig. 5-67). The impact is very similar with the addition of the stations, improving the integration of 41% of the urban fabric (Fig. 5-68).

However, the distribution of this change increases compared to the network with just the activity points as seen in Fig. 5-69. In other words, in the case of the 79 stations, the integration of a small percentage of the urban fabric improves by 400%, whereas in the case of only using the activity points as links, the highest improvement was by 350%.

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LOCAL IMPLICATIONS

6.0

6.1 Area of influence 6.2 Station categories 6.3 Intermodal station case studies 6.4 Pedestrian node generation 6.5 Category A station design 6.6 Evaluation

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6.1 6-1 Process to calculate area of influence of each station in a sample node of the network

n = Average number of users / day d = Neighbourhood density

A = Area Affected = n / d

A

Area of Influence In order to examine the effects that each individual station has on the urban fabric, we need to first determine the reach that each station has into its surroundings. To determine this area of influence, we first find the average amount of passengers that will be using the station in a day (n). Next, we find the population density of the neighbourhood in which the station is located (d). Based on this information, we can then calculate the area (a) that is affected by this particular station using the equation: a = n/d (Fig. 6-1). This area is taken to be the area that will be affected by the presence of the station. In order to quantify this effect, we ran new Space Syntax analysis on only the affected area for each station.

These areas fluctuate substantially (Fig 6-2). The total affected area is summed and compared to the total urban area of Lagos. This comparison showed that the network is currently affecting 44% of the urban fabric. This information was then used to produce pedestrian nodes in the surrounding area.

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6.1 6-2 Overall network influence in Lagos metropolitan area

Total Urban Area : 675 km2

Areas of influence of each station

Affected Area

Total Affected Area : 296 km2 44 % of the urban fabric is affected

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6.2 6-3 Parameters for defining station categories

Passenger Operation [km]

Number of stations

Daily ridership / day

Pink Line

28

18

329,362

Yellow Line

38

14

302,560

Green Line

24

23

645,722

Blue Line

35

20

635,426

Cyan Line

15

10

222,277

Red Line

34

21

571,811

79 Stations MIN avg passenger / day = 8,200 MAX avg passenger / day = 80,000

Average passenger / day

Number of lines

Category A: National hubs

62,001 - 80,000

3

Category B: Regional hubs

21,801 - 80,000

2

Category C: Medium

21,801 - 80,000

1

Category D: Small

8,200 - 21,800

Station Categories Our previous analysis resulted in a network consisting of 6 lines and 79 stations. For categorizing the stations four categories were defined. The average number of passengers per day for each station was calculated according to each stationâ&#x20AC;&#x2122;s associated neighborhood density. From this information the minimum and maximum average passengers per day for the 79 stations were extracted and ranges of passenger flow were assigned for each of the four categories. Fig. 6-3 shows how the categories are divided according to a daily passenger range and the number of lines that go through each station. 1 Implementation Having defined the four categories, the stations were classified accordingly. Fig. 6-4 shows the distribution of station categories across the network. (Appendix 8.2, Chart 8-9) 1. Network Rail; 2011.

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STATION CATEGORIES

6.2 6-4 Distribution of station categories across the network

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6.2 6-5 5 selected test case stations

B-1

D

A

B-2 C

Category Test Cases From each category a sample was taken for further analysis. The samples were chosen according to their proximity to either a second mode of transportation or a commercial or cultural center. Category A has only one option which is station [y-03, C-07, B-20] (Appendix 8.2, Chart 8-10). This station has three lines going through it and has a high flow of daily passengers. The Nigerian Railway Corporation (NRC) also stops at this location and the Oshodi market is 3 km north from the station. From Category B, stations [B-19, R-20] and [G-02, P-02] were chosen.

The first station has two lines going through it and is in close proximity to ferry services being operated in Lagos Lagoon. The second station is within the Alaba International Market. The Alaba International Market is the largest importer of electronics in Africa and it is estimated to have an annual turnover of two

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billion dollars.2 Station [B-03, P-15] was chosen from Category C and it is located in the Central Business District on Lagos Island. Lastly, from Category D, station [R-09] was chosen since it is the closest station to the Murtala Muhammed International Airport (MMIA) (Figs. 6-5 and 6-6).

2. Felix,Wolting (Producers), & van der Haak, Bregtje (Director);2005.

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6.2 6-6 5 test stations and their urban context Y 03

C 07

B 10

National rail A

B 20 R 21

Ferry B-1

G 02 P 02

Alaba market

B-2

B 03 P 15

Business district

C

R 09

Airport

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6.3 6-7 Amsterdam Central Station

To understand the parameters of inter-modal transportation hubs, we examined several case studies. We focused on stations in Hong Kong, Japan, France, The Netherlands, and Italy. These stations represent a variety of design methods from history and different cultures. However, they share a similar trait in that they are all major multi-nodal connections. At these stations passengers traveling by one mode will be immediately transferring to another mode (metro, bus, taxi, and bicycle) and the connection between this transfer is critical for the network to be efficient.

From these studies we abstracted main concepts of connections to the urban fabric and station design that we could use within our network. An area of particular interest was how these stations integrate into their surrounding urban fabric and how they allow for passengers to transition between the station and the urban environment. Stations function better when there is a buffer between the city and the station rather than throwing passengers directly into the city. This is shown in different strategies such as the creation of public â&#x20AC;&#x153;piazzasâ&#x20AC;? common in Italy, or by integrating public green space into the surrounding area.

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Stations that were successful in their designs had methods of connecting to pedestrian paths. These paths can act as an infrastructural axis within the city, and as a result this connection can be more accessible to passengers. Coupling our network lines with paths to transfer to other modes of transportation provides a high degree of connectivity and therefore increases the flow of the station. At these connections it is critical to determine a correct distance for transfer, as too much distance can cause confusion and limit navigation.

INTERMODAL STATION CASE STUDIES

ADAPTIVE FLUX MORPHOLOGIES

6.3 6-8 Plan of the Amsterdam Central Station. Coloured lines represent different means converging in the transport node.

Dutch Renovations Approach We also examined how the Dutch treat their stations and how the new designs were dealing with connecting to the urban fabric. For Amsterdam central station (Fig. 6-7), the placement of the station was an important strategic move in the development of the city (Fig. 6-8). The objective of this design was to â&#x20AC;&#x153;maintain the centre of the country as the green heart of the Netherlands.â&#x20AC;?1 In Rotterdam central station, the station was renovated to have a public square at the front of the station to connect to the city.

The station also developed connections with bridges surrounding the station. At Utrecht, the master plan proposed buildings designed to accommodate the speed of uses in program as well as pedestrian path connections. The conclusion from these studies was that all of the stations developed connections to the existing paths for biking and walking and ample room for bike storage.

1. Amsterdam, NL.p 2.

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6.3 6-9 Shibuya Station

6-10 Section of the different buildings that form Shibuya station.

Keio Railway

Ginza metro line Toyoko Railway

Railway

Metro

Metro

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6.3 6-11 Plan diagram of Shibuya train station with the immediate surroundings. Coloured lines represent different means converging in the transport node.

Bus stop

Shibuya station Shibuya station is located in the city of Tokyo. It is the fourth busiest station in Japan. In 2004, it held over 2.4 million passengers per week day (Fig. 6-9). It has terminal stations of 3 metro lines and 5 railways apart from other road transports. Shibuya station is named after the area in which it is built: a ward which is a major entertainment and shopping centre in Tokyo.

The massive use of the station is not only due to the population living in the area, but also people that commute from the city centre to southern and western suburbs.

reduce its footprint in an already saturated area such as Shibuya. The footprint of the station does not affect the intense traffic that flows in the area consisting of both private and public road transport.

Regarding the station morphology, it consists of a main building and a western building which are connected above ground level (Fig. 6-10). The transport lines that converge at the station do not meet in one point but stop on different levels within the station (Fig. 6-11).

This fact considerably reduces the area needed for the station compared to other cases. Also, it helps

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6.3 6-12 Rendering of Proposed Design for West Kowloon Terminus

6-13 Landscape paths on station roof structure.

6-14 Section diagram showing the location of platforms in the building.

Regional Shuttle Metro Lines

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Long-Haul High speed Lines

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ADAPTIVE FLUX MORPHOLOGIES

6.3 6-15 Plan diagram of West Kowloon project with the immediate surroundings. Coloured lines represent different means converging in the transport node.

Bus stop

West Kowloon Terminus The new West Kowloon Terminus proposal has been praised for its integration into the urban fabric and creation of public space (Fig. 6-12). The station has an expected date of completion of 2015. The high speed rail terminus will consist of 15 tracks that connect Hong Kong to major mainland cities in China. The 15 tracks consist of regional shuttle trains and longhaul high speed trains (Fig. 6-15). It is to become the largest multi-level underground high speed rail station in the world with a footprint of over 10 hectares.2 After completion, the station is to reduce travel times by 50 percent.3 The site is in close proximity to West Kowloon Cultural District and to Victoria Harbor.

Therefore, it requires a design that will integrate the surrounding urban context to the station. The landscaped paths which make up the roof of the rail station emerge from the surroundings. These paths act as the connective fabric between the station, the

adjacent public transportation, the surrounding urban district, and the waterfront. The landscaped paths together with the central plaza will act as green, open spaces and allow for pedestrian circulation.4 The vision for this station is to develop a green landscape within the densely populated urban context. The landscaped paths weave together with the interior spaces of the station, the adjacent urban surroundings, the West Kowloon Cultural District, and Victoria Harbor and together they become the new urban fabric (Fig. 6-13). The layered structure of the station allows for uninterrupted movement of the massive flows and provides a new destination for public use.

2. http://www.aedas.com/Express-Rail-Link-West-Kowloon-Terminus-Hong-Kong 3. SPECTRUMAsia. 2011

4. SPECTRUMAsia. 2011

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6.3 6-16 Perafort station. Design guidelines

6-17 Pusan station. Design guidelines

FOA Case Studies

-Tenerife 2003

Another case study was a series of station designs done by the firm FOA. We chose studies that dealt with multimodal means of transportation and that integrated into different fabrics of the city and infrastructure. For the proposals of Terrife and Pusan, the terminals were end nodes of the system in the form of a pier and waterfront station. For the proposals of Perafort and Florence, the interventions were in the middle of an existing urban fabric.

This project was a proposal for a pier in Santa Cruz de Tenerife off of Africa. The pier was to serve as a connection between the port frontage and the urban grid which was to reuse the existing transport infrastructure. The proposal was for an extension of the paths of the infrastructure along the pier and to provide stops for each mode of transportation connected. (Fig. 6-18).This solution addresses the need to solve the conflict between pedestrian traffic and the roadway infrastructure that passes along the length of the port required. This required the extension of the urban fabric into the port precinct.6

- Pusan 1996 This project was for a high speed rail station terminal in Pusan. The context of the project served as a main connection between Chungjang Road and the waterfront. The proposal was to use the terminal as a connection rather than an end point of axis in the city.5 The method to address this was to provide programs on top of structure to give perspective back to those around it and connection to the sea front area (Fig. 6-17).

We saw this project as a successful demonstration of how to elongate program of paths and modes of transportation as connection to the existing fabric.

Even though the project was to be considered an end point by means of a pier program, the connections allowed for the modes of transportation to be continuous.

We saw this project as a success in connection of programs to an existing area and as a successful method of connecting the users to the surrounding by creating a visual connection. The height of the station allowed it to be used as a visual reference from within the fabric as much as the users at the high program area.

The Perafort project was for a High Speed Railway station in Spain. This site had a context of a concentrated population in a dense urban centre7.

The project addressed the contextual issue that

-Perafort 2002

6. Kwinter, Wigley, Mertins, Kipnis; 2004; p. 115.

5. Kwinter, Wigley, Mertins, Kipnis; 2004; p. 111.

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7. Kwinter, Wigley, Mertins, Kipnis; 2004; p. 86.

ADAPTIVE FLUX MORPHOLOGIES

INTERMODAL STATION CASE STUDIES

6.3 6-18 Tenerife station. Design guidelines

6-19 Florence station. Design guidelines

occurs as a station and line are added into an existing fabric. “When implementing this kind of infrastructure there is automatically a disconnection of the territories on both sides of the station that decreases the critical mass of the local developments”1 meaning that the station can sometimes actually divide an urban area rather than integrating itself into the fabric. The solution proposed was to implement a series of cross connections of paths at a consistent frequency that was accessible for both pedestrian and vehicle traffic (Fig. 6-16).

From this case study we considered the station as a successful example of methods of connecting to an existing urban fabric. Cross connections need to be accessible on two scales: one that relates to the existing territory and the other on a pedestrian pace.

Also, the idea of cross connections to continue the mass of the two fabrics as much as possible was an aspect that we should consider when developing our stations.

8 and presents a major challenge for successfully merging the two. The proposal was to change the idea of the railway station as the end point, main axis, or exit of the city into a seamless part of the fabric. A park was designed so that the people arriving would appear in the middle of it instead of at a main point of the city. The aim was to have the “transportation interface become the contemporary equivalent of the plaza as public space. 4

The program placed the people using the station at differing heights within the stations and had them emerge onto the top in a park. The benefit of having to sink the station below grade was that it erased the disconnection to the fabric (Fig. 6-19).

-Florence 2002

We viewed this project as a successful example of connection of a complex of multimodal transportations.

But the conditions of this project, as with many others, led to the grade condition being manipulated in order to alter the interactions with the programs. The height changes are used as principles of organisation for the programs of the station.

This project was to redevelop the Macelli area as a new cultural metropolitan centre with a high speed railway complex. The site was in the historical centre in the Novoli and the proposed transportation system had to be located 25 m below grade to avoid the existing conditions of the site. “The hybridisation of the public space with the transportation infrastructure is one of the most distinctive features of the contemporary city”

8. Kwinter, Wigley, Mertins, Kipnis; 2004; p.186.

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6.3 6-20 Gare Montparnasse aerial view

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6.3 6-21 Plan of Montparnasse train station showing the different buildings (grey) Coloured lines represent different means converging in the transport node.

Gare Montparnasse (Paris, France) While studying case studies of major transport hubs, it was important to examine a “bad” example alongside the good ones. The station that we studied for this purpose was Gare Montparnasse in Paris (Fig. 6-20).

Gare Montparnasse is one of the six main stations of Paris. This station has a reputation of being highly confusing and difficult to navigate. It owes this to the fact that the station is actually made up of three smaller stations (Montparnasse Maine, Montparnasse Pasteur, and Montparnasse Vaugirard) spread out over two blocks. Each part of the station serves different routes and train services. Only when you have your ticket are you able to know which station you are supposed to go to. Along with the three main parts of the station, there is also a metro station associated with the station. However this metro station is another block away on the other side of the famous Tour Montparnasse. In order to reach the metro station from the main station, travellers must

traverse an extremely long underground tunnel. This tunnel is so long that the station is experimenting with special high-speed moving walkways that move three times as fast as typical moving walkways. A journey from the entrance of Maine to the end of the platform in Vaugirard is almost .6 km. A journey from the end of the platform in Vaugirard to the metro station is almost 1 km (Fig. 6-21). The station’s enormous spread makes it incredibly difficult for both departing and arriving passengers to navigate and get their bearings in the city.

The station also has problems with integration into the surrounding urban fabric. There is a small public plaza in front of Montparnasse Maine, but in the other stations, there is none. They are directly on the street.

This provides no diffusion zone between the station and the street, thrusting travellers directly into the urban fabric with no chance to adjust.

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PEDESTRIAN NODE GENERATION

6.4 6-22 Axial map of urban context with 500 m radius around the station

6-23 Depthmap space syntax analysis for integration

6-24 Pedestrian nodes placed on streets in the top 35% of integration levels

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6.4

Integrating to the Urban Context After laying out the locations of all 79 stations and breaking them into their 4 separate categories, we developed a system for integrating an individual station into its surroundings. A more localised Space Syntax analysis was carried out on a case study area for each category. From this information we placed nodes within a 500 meter radius of the station. These nodes would be placed at the most integrated streets in the area. This is because these streets are used more frequently, so people both arriving at and leaving the stations will most likely be coming from or going to these areas. These nodes will serve as transition points in order to create a spatial diffuser between the station and the urban fabric surrounding it.

where there were no nodes in the immediate area of the station. The value of 0.75 resulted in all stations having at least one node, while not creating large amounts of overlapping nodes.

Generating Pedestrian Networks After defining these nodes, they were fed back into the network generation algorithm. On this more localized scale, the aim was to create a network of pedestrian paths connecting the station with its surroundings.

This network will be used primarily as pedestrian routes from major streets in the area to the station.

It will serve to both better embed the station into its surrounding context and to increase the integration values of the surrounding areas.

Generating Pedestrian Nodes

Implementation

In order to place nodes, we developed an algorithm that would examine Space Syntax data for a given area and assign nodes to streets based on their integration values (Fig. 6-23). The integration values are first scaled between 0 and 1. Then, nodes are placed at the midpoints of each street as a circle with a radius that is proportional to its integration. An important parameter of the algorithm is the minimum value (minVal) that will be considered as meriting the placement of a node. For example a minVal of 0.5 will mean that any street whose scaled integration value is between 0.5 and 1 will be considered a node. This gives the user control over how many nodes will be placed in the system. A smaller minVal results in a larger number of nodes. In the end we used a value of 0.65 as the minVal meaning that only the top 35% of the streets were considered nodes (Fig. 6-24). This was because in the case of a minVal of 0.5, many of the nodes were overlapping and were very jumbled, and with a minVal of 0.9, there were many cases

The following pages show this process for every station within the network. Each particular pedestrian network is evaluated for its P value in the same manner as the global network (length of the minimum span tree/total network length): P = lenminspan/lennetwork The other value that is associated with each station is the Radius of Infulence (ROI). This is the value that was calculated in order to determine how far into the urban fabric the influence of each station reaches.

It is calculated based on the average number of passengers per day (n) and the neighbourhood density(d): ROI =

n d*Ď&#x20AC;

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6.4

R-07, G-13

R-12

P = 0.847663547891 ROI = 1.126642

R-08

R-13

R-09

C-04, Y-05

P = 0.640448454447 ROI = 0.956463

R-14

P = 0.681712850961 ROI = 0.956463

R-10

R-11

P = 0.725040789842 ROI = 1.126642

P = 0.511840566716 ROI = 0.731118

P = 0.720590204284 ROI = 0.455894

B-01

P = 0.588418383774 ROI = 2.637425

P = 0.840823243279 ROI = 0.448948

C-07, y-03, B-10

P = 0.699579157324 ROI = 0.695833

P = 0.964214738936 ROI = 0.455894

C-10, y-06

C-06, y-02

C-02

P = 0.703278751894 ROI = 0.956463

C-09, y-05

C-05, y-01

C-01, G-08

P = 0.681699600448 ROI = 0.448948

P = 0.658047466492 ROI = 0.731118

P = 0.96213820137 ROI = 0.998674

P = 0.722754479563 ROI = 0.956463

C-08, y-04

P = 0.768423948316 ROI = 0.731118

P = 0.747089571539 ROI = 0.956463

P = 0.771529006222 ROI = 0.956463

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C-03

P = 0.657384970259 ROI = 0.448948

B-02

P = 0.50401282519 ROI = 2.637425

PEDESTRIAN NODE GENERATION

ADAPTIVE FLUX MORPHOLOGIES

6.4 6-25 (Opposite Page) Pedestrian network generation for individual stations. Partially shown, remainder in Appendix section 8.3 table 8-11

P - Performance Factor ROI- Radius of Influence 500 m radius Radius of influence

Observations Looking at the evaluations of each station and its newly generated pedestrian network, differences in the values for each station can be seen (Fig. 6-25).

In terms of the performance value of each network (P), the majority of the values fall somewhere between 0.6 and 0.75. There are values in the extremes of the range of 0-1, but only a small amount. This is because the algorithm that generates the networks has been designed to produce networks with a high performance. Therefore, the majority of the solutions will have a relatively similar P value. The other value extracted from each station is the Radius of Influence (ROI). This value fluctuates much more from station to station. It is as small as 0.3 km in some stations and as big as 2 km in others. This is because this value is based off of information that is station specific (expected average passengers and neighbourhood density), not generated by an algorithm. Each station has a different number of expected passengers and there are many different neighbourhood densities across Lagos. Because of this heterogeneous distribution of information, the ROI value is expected to fluctuate more than the P value.

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6.4 Integration

0

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Network 02-b layout

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6.4 6-26

R2 = 0.1516

Connectivity

Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network, the network 02-b, the connection to all stations and the local pedestrian pattern in every station.

Top Dispersion graph showing connectivity vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

Integration [Hh] R3

R2 = 0.4822

Bottom Dispersion graph showing local integration vs Global Integration for each street in the urban fabric of Lagos

Integration [Hh]

The network was evaluated again for its integration, this time with the pedestrian networks (Fig.6-27). Intelligibility measures can be obtained from the graphs in Fig. 6-26. From Fig. 6-26 it can be observed that the correlation between local and global integration of Lagos urban fabric integrated with the network and the pedestrian networks is 0.4822. From Fig. 6-26 the correlation between the degree of connectivity and global integration is 0.1516. These values confirm that the intelligibility of the network increases with the addition of the pedestrian networks.

6-27 (Opposite page) Space syntax analysis evaluation of Lagos urban fabric with the addition of the BRT network and the network 02-b, the connection to all stations and the local pedestrian pattern in every station.

Top Global Integration Map (r = n) Bottom Local Integration Map (r = 3)

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6.4 6-28 Integration impact map of all the local modifications of the stations compared with the preexisting situation.

Integration Impact

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6.4 100

6-29 Chart showing the improvement of the overall integration in the different scenarios tested.

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6-30 Chart where the integration impact percentage in the different scenarios is shown for every street group.

BRT

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The newly generated pedestrian networks were inserted into the model and the network was again analysed for its impact, which showed a marked improvement (Fig. 6-28). This network improves the integration of 56% of the urban fabric (Fig. 6-29). Also, the distribution of this change increases, which means that the integration of a small percentage of the urban fabric improves by 450% (Fig. 6-30). Currently, our system is only affecting 44% of the urban fabric. If this number could be increased, the integration of the urban fabric could be further improved.

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6.5

EMERGENT TECHNOLOGIES & DESIGN 500 m 10 min walking

6-31 (Top Left)

B 10

The area to intervene covers a circle whose radius is 500m.

B 10

The category A node represents the intersection between the blue, cyan and yellow lines.

6-32 (Top Right)

Y 03

Step 1 The street pattern within the range of intervention is analysed with space syntax.

Y 03

C 07

C 07

Integration

0

1

6-33 (Bottom Left)

B 10

Step 2 Placement of nodes based on integration values.

B 10

6 22

5

7

3 10

13

4 11 8

15

Y 03

6-34 (Bottom Right) Step 3 Ranking of nodes based on digital simulation.

C 07

1

9

21 23

12 C

19

07

20

First connection attempt to nodes following existing street pattern.

Integration of the axial map In the first step we addressed the nodes placed from the Space Syntax analysis of the local area (Fig. 6-32).

While running the network generation algorithm on these nodes, the nodes were ranked based on how many cells passed through each node (Figs. 6-33, 6-34). This ranking suggested which paths emanating

122

17 18

For further refinement of our system into the urban fabric we focused on one of the five selected stations to connect to the urban tissue and allocate space for the flow from the network. The selected node was from Category A as this is the busiest station in the network. This node is the connection point of the Yellow, Cyan, and Blue lines as well as the national rail service. We addressed the design of this node with the same rule set as used in our global network design. Therefore, we have used a fractal approach of network design for our local development.

Y 03

16

14

2

from the station would have the highest pedestrian traffic.

Existing Urban Territory With the station and paths of travel, the integration into the existing fabric was then considered. Our first attempt used the roads as they were and adjusted the connections of the simulation to conform to these streets (Fig. 6-34). However, after reviewing this step, it became evident that this method was not suggesting any new network, but actually simply directing people through the existing network. Therefore, areas intersecting the generated networks must be modified in order to adjust to the presence of a new pedestrian network (Fig. 6-35).

Platform and Path Development To develop the paths and platforms for the flow we

CATEGORY A STATION DESIGN

ADAPTIVE FLUX MORPHOLOGIES

6.5 6-35

5

7

22

3 13

10 13

Step 4 Network lines connected to ranked nodes directly following path of simulation regardless of street pattern (shown as blue line)

B 10

6

4

4 11 8

11

1

9

9

8

15

Y 03

16

14

15

Method to address overlapping pedestrian lines offset to accommodate multiple flows.

21 23

12 C

19

07

20

17 18

2

6-36

B 10

Step 5 The diameter of the circle that generates the platforms is the same as the length of the expected trains

Y 03 C 07

used the same criterion as used in designing for railway station platforms (Fig. 6-41). This method designs for the maximum count of people flowing through the highest congested section of the platform during peak rush hours. The tools used in this method assume a number for the design density that addresses the platform width and a maximum length that accommodates the train vehicles (Fig 6-38). Previously we had determined the platform sizes of the three lines, we then applied this method to the pedestrian and connecting paths. In order to determine the length of the paths, we defined a radius around the station equal to the length of a train (Fig. 6-36). Where the paths projected past this radius to connect to the fabric, the path would no longer conform to the required widths (Fig. 6-37).

6-37 Step 6 Pedestrian paths

Area removed by projection of paths into urban fabric.

The paths were designed widths based on their amount of expected passenger flows. We assumed a minimum path width of three metres for each line.

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CATEGORY A STATION DESIGN

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6.5 6-38 Fruin levels for depicting passenger congestion levels.

Level C is the standard used when designing platforms.

2

2 0.93 – 1.21 m

2 0.65 – 0.93 m

A Free Circulation

B Restricted Circulation

C Personal Comfort

+walking speeds freely selected; conflicts with other pedestrians unlikely.

+walking speeds free selected; pedestrians respond to presence of others.

+walking speeds freely selected; passing is possible in unidirectional streams, minor conflicts for reverse or cross movement.

1.21 m

2

0.28 – 0.65 m

2 0.19 – 0.28 m

D No Touch Zone

E Touch Zone

F The Body Ellipse

+freedom to select walking speed and pass others is restricted; high probability of conflicts for reverse or cross movement

+walking speeds and passing ability are restricted for all pedestrians; forward movement is possible only by shuffling; reverse or cross movements are possible only with extreme difficulty; volumes approach limit of walking capacity.

+walking speeds are severely restricted; frequent, unavoidable contact with others; reverse or cross movements are virtually impossible; flow is sporadic and unstable.

However, based on the connections to the nodes, an overlap in the paths was required (Fig. 6-35). To address this, additional width was provided by an offset from the initial three metres for each overlap.

Therefore, the density of flow would have more space to move through.

Platform Conclusions Based on our calculations, the category A node could accommodate a peak flow of 56,354 people, the same amount as Waterloo Station in London. This flow requires an area of 0.186 square kilometres to adapt to the pedestrian paths. The radius of influence at this node was of 466 meters. In the development

124

< 0.19 m2

of the lines, the simulation counts there provided different densities per line (Fig. 6-40). The densities were determined from the peak flow in a 38m by 5m area which represents approximately a one minute walk. For paths 03 and 06 the densities were such that there was a surplus of 15 metres per passenger.

Therefore, the flow of these paths was not calculated as it was minimal and the defaults for the design were applied. For path 01, the flow was of 68 people per rush hour minute with approximately 2.14 square metres per passenger. For path 02, approximately 14 people for every rush hour minute and 5.05 square metres per passenger. For path 04, approximately 18 people per rush hour minute and 4.14 square metres per passenger. And for path 05, 25 people

CATEGORY A STATION DESIGN

ADAPTIVE FLUX MORPHOLOGIES

6.5 6-39 Detail view of platform sizes on site and affect area of urban fabric.

6

Paths numbers are shown.

1

5

2 4

3

Pa

th

Pa 01

th

Pa 02

th

Pa 04

th

05

68 ppl

14 ppl

18 ppl

35 ppl

2.14 m2/person

5.05 m2/person

4.14 m2/person

3.08 m2/person

6-40 Passenger density flow of each pedestrian pathway in the station study selected (Category A) One dot represents 1 person per minute

38 m represents 1 minute walking pace

25% of platform length 0,8 m2 / person

37,5 m 153 m

5m

35% of total combined boarding and alighting load

6-41

3m

Platform design constraints accommodate for the busiest section of passenger flow and congestion.

125

CATEGORY A STATION DESIGN

EMERGENT TECHNOLOGIES & DESIGN

6.5 6-42 Path 01 Showing the flow of 68 people per rush hour minute with approximately 2.14 square metres per passenger.

38 m (1 minute walking pace) 1,5 1,5 3

6-43 Path 02 Showing approximately 14 people for every rush hour minute and 5.05 square metres per passenger.

The yellow and cyan rail lines ran along these paths.

2

2,5

9 28

6-44 Path 03 The densities of this path such that the flow per passenger was greater than 15 metres per person. Therefore the design held to the minimum defaults for a platform.

5,2

The blue rail line ran along this path.

3

4,6 15,2

6-45 Path 04 Approximately 18 people per rush hour minute and 4.14 square metres per passenger.

The yellow and cyan rail lines ran along these paths.

2

2

2

8 24

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CATEGORY A STATION DESIGN

ADAPTIVE FLUX MORPHOLOGIES

6.5 6-46 Path 05 Showing the flow of 25 people for every rush hour minute and 3.08 square metres per person.

1,6

At this line the overlapping path method was applied.

1,5 3 15

6-47 Path 06 The densities of this path such that the flow per passenger was greater than 15 metres per person. Therefore the design held to the minimum defaults for a platform.

4,3

The blue rail line ran along this path.

3

4,5

for every rush hour minute and 3.08 square metres per person. All of these densities of flows suggest that a Fruin level A would be achieved which means free circulation; walking speeds freely selected and conflicts with other pedestrians unlikely.

We found these results acceptable as they accommodated for the flow successfully and did not produce a rate which would cause congestion.

However, the amount of square metres per passenger seems excessive and could be refined further. The space per passenger could be refined by defining a relationship of the length to the pedestrian paths and a width that uses a level of congestion relative to the cultural norms. This could also be addressed by accounting for directions of flows and other connections with networks. Further research into the guidelines and passenger capacities of Lagos could inform the path refinement.

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EVALUATION

EMERGENT TECHNOLOGIES & DESIGN

6.6 6-48

BEFORE

81%

13%

5%

1%

Buses and mini-buses (Danfos)

Taxi, private cars

Motorcycles (okada)

Railway

51%

13%

5%

Buses and mini-buses (Danfos)

Taxi, private cars

Motorcycles (okada)

Redistribution of usage of different means of transportation before and after the implementation of the network.

AFTER

31%

Railway

6-49 Reduction of Road Usage

# of People On Roads Before Implementation % Reduction In Road Usage

Urban Population # of People On Roads After Implementation

6-50 Travel time comparison Ifako-Ijayi

Personal Vehicle Travel Time : 2 Hours BRT Travel Time : 40-70 min

Kosofe

Agege

Ikorodu Ikeja

Proposed Network Travel Time : 25-32 min

Mile 12

Alimosho

Oshodi Isolo

Mushin Lagos Mainland Surulere

Ojo

Lagos Island

Amuwo Odofin

Ajeromi

Eti-Osa Eti-Osa

Apapa Victoria Island

128

27%

EVALUATION

ADAPTIVE FLUX MORPHOLOGIES

6.6 6-51

Length of mass transport network in km per km2 of city area [km/km2] 0.35

Comparison of mass transit across Africa

Tunis Casablanca

0.3

Cairo

Lagos

0.25

Nairobi

0.2 0.15

0.05 BRT

Cairo

Casablanca

Nairobi

Tunis

Post Implementation

0.1

Average of African cities Existing Post Implementation

Lagos

Redistribution of Transport Usage

Public Transport Comparison

In evaluation of the effect of our system we found that 30% of public transport users will use rail. The use of buses and miniâ&#x20AC;&#x201C;buses (Danfos) currently in Lagos takes 81% of public transport usage. A large portion of the BRT users take danfo for some parts of their journey as a means of access to the BRT network.1 However after the implementation of the our system, the rail network will be well integrated within the urban fabric and can be used as access points to the BRT network and as a result will decrease the use of mini buses to 50 percent (Fig. 6-48).

A typical measure for ranking public transportation network in different cities is the ratio of the lengths of the network to the urban area. The average of this ratio in Africa is 0.07 with Tunis having the highest rank of 0.27.2 Compared to other African cities Lagos is currently below average having a rank of 0.03 but after the implementation of our network it will be one of the highest ranked in Africa with a rank of 0.29 (Fig. 6-51).

Reduction in Congestion Another way to evaluate our system was to compare journey times for a typical commute in Lagos. The results indicated that the proposed rail network can decrease journey time by 75% for a personal vehicle and 40% for the BRT network (Fig. 6-50). This shows that the rail network will improve the current congestion in Lagos. Another measure that can be used to identify the improvements of the system in terms of congestion is to see what percentage of road usage will be reduced by the rail network. Since currently all public transportation is on the roads the difference between the number of people on the roads before and after the implementation of the network will give that percentage. According to our design outputs and flow analysis the proposed rail network will reduce road usage by 27percent (Fig. 6-49).

Despite these positive results there are remaining aspects with areas for improvement. First, the system reduces the use of informal modes of transportation such as danfos, but even with this reduction, 50 % of the population is estimated to continue using them as per transportation reports. Further reduction in this number would continue to improve the transportation in Lagos as it would alleviate the overload on the existing infrastructure. Second, the journey times that showed the highest reduction in travel were mainly along major commute routes. Further evaluation to ensure that all routes are reduced would increase the effectiveness of the network. Finally, the road usage was reduced by 27%, but this will most likely only be along heavy commute routes. We see the area for improvement in continuing to reduce usage of the roads across the entire city in order to improve traffic conditions.

1. Lamata; 2009.

2. Siemens; p. 31.

Conclusion

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EMERGENT TECHNOLOGIES & DESIGN

130

ADAPTIVE FLUX MORPHOLOGIES

CONCLUSIONS

7.0

7.1 Conclusions 7.2 Further development

131

CONCLUSIONS

EMERGENT TECHNOLOGIES & DESIGN

7.1 7-1 World Map showing studied cities regarding length of mass transport networks in km / km2.

London New York Mexico City

Tokyo Lagos

Mexico City

New York

London

Tokyo After Implementation Before Implementation

Lagos

0

6

Urban Networks The challenge of accommodating urban inhabitants has become increasingly complex as more people are living in cities. In turn, these cities are developing at intense rates of urbanization and becoming mega cities. Providing sufficient infrastructural networks for these populations is vital to ensuring the survival and continued growth of a city (Fig. 7-1).

Typically, a transportation network such as a public rail system starts small and expands as the city expands.

However, in some cases, cities expand so fast and in such an uncontrolled fashion that a transportation system never has time to develop. Implementing a transportation network into this type of city presents a unique challenge and a unique opportunity. In the case of a city in its infancy beginning to grow, it is difficult to make decisions about where major nodes should go, and how people will use the newly developing areas.

With an established city, much of this information is already known. Therefore, more informed decisions

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CONCLUSIONS

ADAPTIVE FLUX MORPHOLOGIES

7.1 7-2 Slime mould simulation with emergent nodes and connection graphs

Gabriel Graph

Relative Neighbourhood Graph

Minimum Spanning Tree

Slime Mould Simulation

can be made about what parts of the city should serve as major nodes for the implementation of a network.

System Development In order to implement a network in a city, our proposal functions as an adaptive computational system rather than a top-down implementation of a network. Through the power of agent-based computing, we are able to create intelligent network solutions that go beyond a simple â&#x20AC;&#x153;connect the dotâ&#x20AC;? approach. The system utilises simple rules of interaction between individual agents and their environment which lead to emergent behaviours which create robust and efficient solutions which could not be predicted beforehand (Fig. 7-2).

This emergence leads to more possibilities than are possible from traditional methods for generating networks such as connection schemes like the Gabriel Graph, Relative Neighbourhood Graph, or Minimum Spanning tree, which are computed solely based on proximity of points.

Implementation In order to test the system, we decided to first apply it to the city of Lagos in Nigeria. The reason for this was that Lagos is projected to become the 11th largest city in the world by the year 2025. Despite its already large population and its projected growth, the city still has no major public transportation in place. There is a Bus Rapid Transit system in place, as well as private bus companies, but the traffic in the city is still a serious problem. Many residents start their morning commutes as early as 4 am in order to get to work on time. After designating 22 activity points within the city to serve as nodes, we applied the algorithm.

We analysed several variants of the resulting network using Depth Map Space Syntax software in order to evaluate their effects on the urban fabric. The most effective of the solutions was developed further into a fully developed public rail network. Final evaluations of the network showed an improvement in integration in 56% of the affected urban area (Fig. 7-3).

133

CONCLUSIONS

7.1 7-3 Space Syntax analysis before (top) and after (bottom) the implementation of the network

134

EMERGENT TECHNOLOGIES & DESIGN

CONCLUSIONS

ADAPTIVE FLUX MORPHOLOGIES

7.1

Potentials of the System Our experiment in Lagos shows the enormous impact that a network developed by our system can have on a city. Even though many of the inputs to the system were specific to Lagos, this system can be applied to any other city. Lagos was simply the first testing ground for the system. Every city has important areas which could be translated into nodes to be input into the system. The system could then be used to generate possible solutions for the selected city. The parametric nature of the system would also allow for adjustments to be made in order to create different outcomes based on the desired effect for the city.

Limitations of the System Even though the system has been successful to this point of producing effective solutions for network design, it is not without its limitations. The first and most obvious limitation is computing power. The Rhino/Grasshopper/Python hybrid is not the most effective environment for agent-based modelling and is therefore extremely computationally heavy. One run of the simulation can take upwards of 4 hours to complete. Other scripting environments such as Processing are capable of producing results much faster because they are much more streamlined.

Rhino/Grasshopper/Python has its advantages as well as it is very accessible and there is a large support community in place.

which to run and the results are extracted from a frozen state. This was apparent in some of our simulations where nodes would become abandoned because all of the individuals in the system had gotten stuck into certain paths. Something that could be added in order to deal with this problem could be to build in a method of self-evaluation into the system. This could be in the form of an “off switch” for the system so that when it reaches a certain state, it stops. This could be coupled with an evaluation that will add new agents to the system after a certain amount of time if the state has not been reached.

Agent-Based Computing for Adaptive Network Generation Our system shows the potential that agent-based computing has for the generation of adaptive networks. Traditional methods for generating networks result in static representations of a network. Our system, however, is one that is constantly changing and can be adjusted easily to change the course of the simulation. Our experiments have shown that the networks resulting from the simulation are not only effective in the sense of material used (network length), but they are also highly effective in terms of their impact on the integration of the urban fabric in which they are implemented. We believe that the system is a highly powerful tool, and a clear demonstration of the potentials of agent-based computing for generating network solutions for large urban areas.

Also, the idea of “complete” is quite ambiguous in this system. The system is simply given a time frame in

135

FURTHER DEVELOPMENT

7.2 7-4 Potential expansion of network as urban area grows

7-5 Potential development of node implications.

6

Space Syntax depth diagram.

Street shown in blue is depth 0 of urban fabric at node.

3

5 4 2 1 0 Depth 1 2 3 4 5 6 7

136

EMERGENT TECHNOLOGIES & DESIGN

FURTHER DEVELOPMENT

ADAPTIVE FLUX MORPHOLOGIES

7.2

Further Development Throughout the design of this system, two distinct potentials emerged as the possible directions in which the project could continue to develop: We could continue to develop the network system itself so that it is able to continually adapt over time as the city grows, or we could develop the system at a more local level and design the individual stations architecturally.

Network Development Our initial interests laid in the concept of time and how the built environment can adjust to change over time.

Transportation networks feel this effect constantly, not only geometrically, but also in terms of changes in flow. As a city grows, its network must adapt and grow in order to continue connecting all parts of the city. As this change happens, certain parts of the network may become more or less important based on their location. Some areas may become isolated or, in extreme cases, abandoned while others may become more vital to the city. While this change happens on the scale of years, there are also changes to the network that happen on the scale of days, and even hours. Throughout any given day in the city, the flows across different parts of the network change several times. Morning and evening rush hours are an example of this change, as transportation networks in many cities become saturated with traffic.

A possible direction for the continuing development of our system would be to attempt to address these changing conditions. This could be done in different ways. One method would be to introduce new nodes in phases in order to see how the network reconfigures itself to adapt to the presence of new activity points in the city. A second method would be to study more closely the potential of introducing secondary networks. It can be difficult to adjust a primary network after its implementation, but one way of addressing the shifting importance of nodes, would be to look at the potentials of adding more flexible

networks for other means of transportation which could adapt to increase connection of nodes that had become more important.

Station Development The other option lies in a more localised and architectural scale. The work until this point has begun to touch on this subject slightly, but in a highly diagrammatic sense. In this approach, we pursued a fractal design method wherein the logic that was used to design the network system was scaled down to a local level at each station in order to design local networks. From here it could be possible to use these networks and station platform information as inputs into a generative system for generating station typologies.

Depending on the level of specificity, this could be developed on one station in order to advance it to a highly articulated architectural solution, or it could be developed more on the scale of a system, showing less refined solutions for multiple stations.

It would be important to also develop the local networks and continue to evaluate their effect on the surrounding urban fabric. The information regarding the expected flows through the stations could also serve as inputs to a system that could begin to make predictions about how the area will develop. As nodes become more and more important in a network, they will begin to stimulate the growth of an area leading to increased population densities and architectural challenges centred around how to accommodate this influx of people.

137

EMERGENT TECHNOLOGIES & DESIGN

138

ADAPTIVE FLUX MORPHOLOGIES

APPENDIX

8.0

8.1 Scripts 8.2 Charts and tables 8.3 Station analysis

139

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 8-1 Slime Mould Grasshopper Simulation Interface

###SLIME MOULD SIMULATION#####################

Zval = ptL[i][2] zL.append(Zval)

###CELL CLA SS################################# import rhinoscriptsyntax as rs import scriptcontext as sc

cenPt = rs.AddPoint([math.fsum(xL)/ len(xL),math.fsum(yL)/len(yL),math. fsum(zL)/len(zL)]) return cenPt

from operator import itemgetter import random import math

#Define Class of a Cell

###################################### ########

###################################### ######################## ###################################### ########################

def cenPt(_ptL): class cell: ptL = _ptL def __init__(self): xL = [] yL = [] zL = [] for i in range (0,len(ptL)):

###You can kind add to this as you go along and realize you need more stuff

Xval = ptL[i][0]

self.name = None

xL.append(Xval)

self.start = None

Yval = ptL[i][1]

self.cent = None

yL.append(Yval)

140

####All of the information you will need from the Class

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 self.coord = None self.dest = None self.neighbors = [] self.visibleNeighbors = []

self.neighbors = temp def seeNeighbors(self,_angle): angle = _angle for i in range(0,len(self. neighbors)):

self.closestNeighbor = None self.startVect = None

vect = rs.VectorCreate(self.neighbors[i]. coord,self.coord)

self.senseAng = None self.foodSources = [] self.pullFood = None self.closestFood = None self.closeIndex = None self.visibleFood = [] self.moveVect = None self.line = None self.eating = 0 self.steps = 0 def getStart(self,_start): self.start = _start cent = rs.CurveAreaCentroid(self.start) self.cent = cent[0] def getCoord(self,_pt):#This is the XYZ coordinate of the Cell Class self.coord = _pt def getName(self):#get the GUID of the class self.name = rs.AddPoint(self. coord) def getNeighbors(self,_list,_index): list = _list index = _index temp = list[:] temp.pop(index)

vect2 = rs.PointAdd(self. coord,self.startVect) ang = rs.VectorAngle(vect,vect2) if ang > float(angle)/2: pass else: self.visibleNeighbors. append(self.neighbors[i]) def getClosestNeighbor(self): distL = [] for i in range(0,len(self. neighbors)): tmpL = [] tmpL.append(i) dist = rs.Distance(self. coord,self.neighbors[i].coord) tmpL.append(dist) distL.append(tmpL) distL.sort(key = itemgetter(1,0)) self.closestNeighbor = self. neighbors[distL[0][0]] def getDest(self,_Boolean): self.dest = _Boolean def getVect(self,_ destination):#Initial Vector of movement outward from center Destination1 = _destination if self.startVect == None:

141

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 if self.dest == 0: self.startVect = rs.VectorCreate(self.coord,self.cent) elif self.dest ==1:

if rs.PointInPlanarClosedCurve (self.coord,circ) == 1: pullVect = rs.VectorCreate(self.pullFood. coord,self.coord)

cent = rs.CircleCenter Point(Destination1)

pullVect = rs.VectorUnitize(pullVect)

self.startVect = rs.VectorCreate(cent,self.coord)

pullVect = rs.VectorScale( pullVect,factor*(self.pullFood.radius/ maxRad))

else: self.startVect = self. startVect def newVect(self,_pt):

self.startVect = rs.VectorAdd(self.startVect,pullVect) else: pass

pt = _pt self.startVect = rs.VectorCreate(self.coord,pt) def getFood(self,_foods):#Get All of the Food sources self.foodSources = _foods def getPullFood(self): distL = [] for i in range(0,len(self.foodSources)): tempL = [] dist = rs.Distance(self. coord,self.foodSources[i].coord) tempL.append(i) tempL.append(dist) distL.append(tempL) distL.sort(key = itemgetter(1)) self.pullFood = self. foodSources[distL[0][0]] def getPulled(self,_factor,_ maxRad): factor = _factor maxRad = _maxRad circ = rs.AddCircle(self.pullFood.coord,self.pullFood.pullRadius)

142

def seeFood(self,_angle):#Break out all the Food Sources the cell can “see” angle = _angle self.senseAng = angle self.visibleFood = [] for i in range(0,len(self.foodSources)): foodVect = rs.VectorCreate(self.foodSources[i]. coord,self.coord) newVect = rs.PointAdd(self. coord,self.startVect) newLine = rs.VectorCreate(newVect,self.coord) ang = rs.VectorAngle(foodV ect,newLine) if ang>float(angle)/2: pass #If outside the angle of view, ignore it else: self.visibleFood. append(self.foodSources[i]) #If inside the angle of view, add to the list of visible food def closeFood(self):#Find the closest visible food source

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 foodSources = [] for i in range(0,len(self. visibleFood)):#Measure distance to each food source, and sort the list according to the distance tmpL = []

closestFood.coord,self.closestFood.radius) if rs.PointInPlanarClosed Curve(self.coord,circ) == 1 and self. closestFood.state == 0: self.eating = 1 #1 means it is at a food source

num = i self.start = self.clostmpL.append(num) dist = rs.Distance(self. coord,self.visibleFood[i].coord) tmpL.append(dist) foodSources.append(tmpL) foodSources.sort(key = itemget-

estFood.name else: self.eating = 0 #0 means it is still moving def move(self,_maxDist,_step,_ boundary,_obstacles):#Define the movement of the cell

ter(1)) step = _step if len(self.visibleFood) > 0: obstacles = _obstacles self.closestFood = self. visibleFood[foodSources[0][0]] self.closeIndex = foodSources[0][0]

boundary = _boundary maxDist = _maxDist if len(self.visibleFood) > 0:

elif len(self.visibleFood) == dist = rs.Distance(self. coord,self.closestFood.coord)

0: # es[:]

foodL = self.foodSourcif dist < maxDist:#If the food is close enough and visible distL = []

for i in range (0,len(self. foodSources)): tmpL = []

foodVect = rs.VectorCreate(self.closestFood. coord,self.coord) foodVect = rs.VectorUnitize(foodVect)

tmpL.append(i) dist = rs.Distance(self.coord,self. foodSources[i].coord)

foodVect = rs.VectorScale(foodVect,step) test = rs.PointAdd(self.coord,foodVect)

tmpL.append(dist) tmpL = [] distL.append(tmpL) distL.sort(key = itemget-

for i in range(0,len(obstacles)):

ter(1)) def eatFood(self): if len(self.visibleFood) > 0: circ = rs.AddCircle(self.

tmpL.append(rs.Po intInPlanarClosedCurve(test,obstacles [i])) if rs.PointInPlanarClo sedCurve(test,boundary) != 0 and math.

143

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 test = rs.PointAdd(self.coord,foodVect)

fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,foodVect)

self.coord = rs.PointAdd(self.coord,foodVect) self.moveVect =

self.moveVect =

foodVect

self.startVect =

foodVect

foodVect self.startVect = foodVect elif rs.PointInPlanarC losedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0: while rs.PointInPl anarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: inside = [] foodVect = rs.VectorRotate(foodVect,random. randint(-self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,foodVect) for i in

else:#If the food is visible, but too far away vect = rs.VectorUnitize(self.startVect) vect = rs.VectorScale(vect,step) test = rs.PointAdd(self.coord,vect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.Po intInPlanarClosedCurve(test,obstacles [i]))

range(0,len(obstacles)): inside. append(rs.PointInPlanarClosedCurve(tes t,obstacles[i]))

if rs.PointInPlanarClo sedCurve(test,boundary) != 0 and math. fsum(tmpL) == 0:

tmpL = inside self.coord = rs.PointAdd(self.coord,vect) self.coord = rs.PointAdd(self.coord,foodVect)

self.moveVect = vect self.startVect =

self.moveVect = vect

foodVect self.startVect = foodVect elif rs.PointInPlanarC losedCurve(test,boundary) == 0: while rs.PointInPl anarClosedCurve(test,boundary) == 0:

elif rs.PointInPlanarC losedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0: while rs.PointInPl anarClosedCurve(test,boundary)!=0 and math.fsum(tmpL)!=0: inside = []

foodVect = rs.VectorRotate(foodVect,random. randint(-self.senseAng,self.senseAng),[0,0,1])

144

vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1])

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 test = rs.PointAdd(self.coord,vect) for i in

rs.VectorUnitize(vect) vect = rs.VectorScale(vect,step)

range(0,len(obstacles)): test = rs.PointAdd(self. inside. append(rs.PointInPlanarClosedCurve(tes t,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

coord,vect) tmpL = [] for i in range(0,len(obstacles)): tmpL.append(rs.PointIn PlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClose dCurve(test,boundary) != 0 and math. fsum(tmpL) == 0:

self.startVect = vect elif rs.PointInPlanarC losedCurve(test,boundary) == 0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

while rs.PointInPl anarClosedCurve(test,boundary) == 0:

self.startVect = vect

vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1])

elif rs.PointInPlanarClo sedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0:

test = rs.PointAdd(self.coord,vect)

while rs.PointInPlanarC losedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

inside = [] vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1])

self.startVect = test = rs.PointAdd(self.coord,vect)

vect elif len(self.visibleNeighbors) > 0:

for i in range(0,len(obstacles)):

pts = [] for i in range(0,len(self. visibleNeighbors)): pts.append(self. visibleNeighbors[i].coord)

inside. append(rs.PointInPlanarClosedCurve(tes t,obstacles[i])) tmpL = inside

target = cenPt(pts) vect = rs.VectorCreate(target,self.coord)

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

vect =

145

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 self.startVect = vect elif rs.PointInPlanarClose dCurve(test,boundary) == 0: while rs.PointInPlanar ClosedCurve(test,boundary) == 0: vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect)

vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) for i in range(0,len(obstacles)): inside. append(rs.PointInPlanarClosedCurve(tes t,obstacles[i])) tmpL = inside

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

self.coord = rs.PointAdd(self.coord,vect)

self.startVect = vect self.moveVect = vect else: self.startVect = vect vect = rs.VectorRotate(self.startVect,random. randint(-10,10),[0,0,1])

# vect = rs.VectorCreate(self. closestNeighbor,self.coord)

test = rs.PointAdd(self. coord,vect)

# vect = rs.VectorUnitize(vect) tmpL = []

for i in range(0,len(obstacles)): tmpL.append(rs.PointIn PlanarClosedCurve(test,obstacles[i])) if rs.PointInPlanarClose dCurve(test,boundary) != 0 and math. fsum(tmpL) == 0:

self.coord = rs.PointAdd(self.coord,vect) self.moveVect = vect

# vect = rs.VectorScale (vect,float(step)/2) elif rs.PointInPlanarClose dCurve(test,boundary) == 0: while rs.PointInPlanar ClosedCurve(test,boundary) == 0: vect = rs.VectorRotate(vect,random.randint(self.senseAng,self.senseAng),[0,0,1]) test = rs.PointAdd(self.coord,vect) self.coord = rs.PointAdd(self.coord,vect)

self.startVect = vect self.moveVect = vect elif rs.PointInPlanarClo sedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0:

self.startVect = vect def stepCount(self):

while rs.PointInPlanarC losedCurve(test,boundary)!=0 and math. fsum(tmpL)!=0:

self.steps += 1

inside = [] ###SLIME MOULD SIMULA-

146

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 TION####################

def getState(self,_slime):

###FOOD SOURCE CLASS######################### import rhinoscriptsyntax as rs

slime = _slime circ = rs.AddCircle(self. coord,self.radius)

import scriptcontext as sc from operator import itemgetter

if self.state != 2: if rs.PointInPlanarClosedCu rve(slime.coord,circ) == 1:

import random self.state = 1 #I’m being eaten class snacks:

else:

def __init__(self): self.name = None

self.state = 0 #I’m not being eaten elif self.state == 2:

self.state = 0

self.state = 2

self.radius = None self.circ = None

def setState(self): if self.state == 0:

self.pullRadius = None

self.state = 0

self.pull = None

elif self.state == 1:

self.count = 0 def getName(self,_guid): self.name = _guid def getCoord(self): cen = rs.CurveAreaCentroid(self.name) self.coord = cen[0] def getRadius(self): self.radius = rs.CircleRadius(self.name) #

def drawCirc(self):

# self.circ = rs.AddCircle(self. coord,self.radius) def getPull(self,_radiusScale,_ pullfactor): scale = _radiusScale

self.state = 2#I can’t be eaten elif self.state == 2: self.state = 2 def startState(self,_state): self.state = _state def setCount(self,_pt): circ = rs.AddCircle(self. coord,self.radius) pt = _pt if rs.PointInPlanarClosedCurve( pt,circ) == 1: self.count +=1 else: self.count = self.count

self.pullRadius = self. radius*scale ###ADAPTIVE FLUX MORPHOLOGIES### self.pull = _pullfactor

147

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 ###Slime Mould Simulation Master Script###

if rad > maxRad: maxRad = rad

###Main Parameters for change are: #####Sensing Angle #####Sensing Distance

###################################### ############### ####START CONDITIO NS#################################

#####Number of Cells if RESET: reload(cell) ###################################### #######

reload(snacks)

import rhinoscriptsyntax as rs

for i in range (0,len(start)):

import scriptcontext as sc

radius = rs.CircleRadius(start[i])

import math num = numCells*(radius/maxRad) from operator import itemgetter import random import cellFood_2012_8_1GH as cell

div=rs.

DivideCurve(start[i],int(num)) for j in range(0,len(div)):

import snacks_2012_7_30GH as snacks

cellPts.append(div[j])

###################################### #######

slime = cell.cell() slime.getStart(start[i]) slime.getCoord(div[j])

cellPts = [] slime.getName() slimeL = [] slime.getDest(random.randFoodL = []

int(0,1))

moves = []

slime.getVect(Destination1)

FoodCent = []

slimeL.append(slime)

neighborhoods = [] counts = []

for i in range(0,len(food)):###Makes empty food classes Food = snacks.snacks()

###DETERMINE MAX RADIUS OF NODES##################### maxRad = 0 for i in range (0,len(start)): for i in range (0,len(start)):###finding the biggest radius rad = rs.CircleRadius(start[i])

148

Food.getName(food[i]) Food.getCoord() Food.getRadius() Food. getPull(radScale,pullFactor) circ = rs.AddCircle(Food. coord,Food.pullRadius)

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 neighborhoods.append(circ)

counter = []

FoodL.append(Food)

for k in range (0,len(slimeL)):

sc.sticky[‘points’] = slimeL

FoodL[j]. setCount(slimeL[k].coord)

sc.sticky[‘food’] = FoodL counter.append(FoodL[j]. ###RUN THE SIMULATION#################################

count) counts.append(FoodL[j].count)

else: sc.sticky[‘points’] = slimeL slimeL = sc.sticky[‘points’] sc.sticky[‘food’] = FoodL FoodL = sc.sticky[‘food’] endmoves = moves for j in range(0,len(FoodL)): circ = rs.AddCircle(FoodL[j]. coord,FoodL[j].pullRadius) neighborhoods.append(circ) FoodCent.append(FoodL[j].coord) for i in range(0,len(slimeL)): slimeL[i]. getNeighbors(slimeL,i) slimeL[i].seeNeighbors(angle) slimeL[i].getClosestNeighbor() slimeL[i].getFood(FoodL) if slimeL[i].steps > minStep: slimeL[i].getPullFood() slimeL[i]. getPulled(pullFactor,maxRad) else: pass slimeL[i].seeFood(angle) slimeL[i].closeFood() slimeL[i].move(maxDist,step,bo undary,obstacles) slimeL[i].eatFood() slimeL[i].stepCount() moves.append(slimeL[i].coord) for j in range(0,len(FoodL)):

149

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 ###Gabriel Graph#####################

mid = rs.CurveMidPoint(line)

##################################### import rhinoscriptsyntax as rs

radius = rs.CurveLength(line)/2

import math

rs.DeleteObject(line)

import random

circ = rs.AddCircle(mid,radius)

from operator import itemgetter tmpL.append(circ) ###DEFINE CLASS POINT################ self.circ.append(tmpL) class point: def testCirc(self): def __init__(self): for i in range (0,len(self. self.name = None

circ)):

self.coord = None

temp = self.neighbors[:]

self.neighbors = []

temp.pop(self.circ[i][0])

self.distances = []

testL = []

self.circ = [] def getName(self,_guid): self.name = _guid

for j in range (0,len(temp)): test = rs.PointInPlanar ClosedCurve(temp[j],self.circ[i][1])

def getCoord(self):

testL.append(test)

self.coord = rs.PointCoordinates(self.name) def getNeighbors(self,_list,_index): neighbors = _list neighbor2 = neighbors[:] index = _index neighbor2.pop(index)

check = math.fsum(testL) if check == 0: pts = self.coord,rs.

PointCoordinates(self.neighbors[self. circ[i][0]]) connect = rs.AddCurve(pts,1) def deleteCirc(self): for i in range(0,len(self.

self.neighbors = neighbor2 circ)): def drawCirc(self):

rs.DeleteObject(self. for i in range (0,len(self. neighbors)):

circ[i][1])

tmpL = [] tmpL.append(i) pts = self.coord,rs.

PointCoordinates(self.neighbors[i]) line = rs.AddCurve(pts,1)

150

pts = rs.GetObjects(â&#x20AC;&#x2DC;select pointsâ&#x20AC;&#x2122;,1) rs.EnableRedraw(False)

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 for i in range (0,len(pts)): pt = point()

def getCoord(self): self.coord = rs.PointCoordinates(self.name)

pt.getName(pts[i]) pt.getCoord()

def getNeighbors(self,_list,_index):

pt.getNeighbors(pts,i)

list = _list

pt.drawCirc()

temp = list[:]

pt.testCirc()

index = _index

pt.deleteCirc()

temp.pop(index) self.neighbors = temp

rs.EnableRedraw(True)

def drawCirc(self): for i in range(0,len(self. neighbors)): tmpL = [] radius = rs.Distance(self. coord,self.neighbors[i])

###Relative Neighborhood Graph######### ####################################### import rhinoscriptsyntax as rs

circ1 = rs.AddCircle(self. coord,radius) circ2 = rs.AddCircle(self. neighbors[i],radius)

import math tmpL.append(i) import random tmpL.append(circ1) from operator import itemgetter tmpL.append(circ2) ####################################### self.circ.append(tmpL) ###DEFINE POINT CLASS################## class point:

intersect = rs.CurveBoolea nIntersection(circ1,circ2) temp = self.neighbors[:]

def __init__(self): self.name = None

temp.pop(i)

self.coord = None

test = []

self.neighbors = []

for j in range(0,len(temp)):

self.distance = [] self.closestPoint = None

inside = rs.PointInPla narClosedCurve(temp[j],intersect)

self.circ = []

test.append(inside)

def getName(self,_guid): self.name = _guid

check = math.fsum(test) #

print check

151

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 if check == 0: pts = self.coord,rs.

PointCoordinates(self.neighbors[i]) connect = rs.AddCurve(pts,1) rs.DeleteObject(intersect) def deleteCirc(self): for i in range(0,len(self.

###################################### ### ##DEFINE POINT CLASS##################### class point: def __init__(self): self.name = None self.index = None

circ)): self.coord = None rs.DeleteObject(self. self.X = None

circ[i][1]) rs.DeleteObject(self. circ[i][2])

self.Y = None self.edges = [] self.connected = []

pts = rs.GetObjects(‘select points’,1) rs.EnableRedraw(False) for i in range (0,len(pts)): pt = point() pt.getName(pts[i]) pt.getCoord()

def getName(self,_pt,_index): self.name = _pt self.index = _index def getCoord(self): self.coord = rs.PointCoordinates(self.name)

pt.getNeighbors(pts,i)

self.X = self.coord[0]

pt.drawCirc()

self.Y = self.coord[1]

pt.deleteCirc()

def getEdges(self,_Edges,_pts): pts = _pts lines = _Edges

rs.EnableRedraw(True)

for i in range(0,len(lines)): tmpL = [] start = rs.CurveStartPoint(lines[i])

###MINIMUM SPANNING TREE################# import rhinoscriptsyntax as rs

152

end = rs.CurveEndPoint(lines[i]) if start[0] == self.X and start[1] == self.Y:

import math

tmpL.append(i)

import random

tmpL.append(lines[i])

from operator import itemgetter

tmpL.append(rs.

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 Distance(self.coord,end))

if points[i].X == st[0] and points[i].Y == st[1]:

self.edges.append(tmpL) tmpL.append(i) elif end[0] == self.X and end[1] == self.Y:

tmpL. append(points[i])

tmpL.append(i) tmpL.append(lines[i]) tmpL.append(rs.

Distance(self.coord,end))

tmpL.append(rs.

Distance(self.coord,points[i].coord)) self.connected. append(tmpL)

self.edges.append(tmpL)

else:

def getConnected(self,_points):

pass

points = _points for j in range(0,len(self.edges)):

###CREATE INSTANCES OF POINT CLASS########

st = rs.CurveStartPoint(self.edges[j][1])

pts = rs.GetObjects(‘select points’,1) lines = rs.GetObjects(‘select lines’,4)

e = rs.CurveEndPoint(self. edges[j][1]) if self.X == st[0] and self.Y == st[1]:

pointL = [] for i in range(0,len(pts)): pt = point()

for i in range(0,len(points)):

pt.getName(pts[i],i)

tmpL = [] if points[i].X == e[0] and points[i].Y == e[1]:

pt.getCoord() pt.getEdges(lines,pts) pointL.append(pt)

tmpL.append(i) tmpL.

###################################### ####

append(points[i]) tmpL.append(rs.

Distance(self.coord,points[i].coord))

###GET ALL CONNECTED EDGES FOR EACH POINT# for i in range(0,len(pointL)):

self.connected. pointL[i].getConnected(pointL)

append(tmpL) else: pass elif self.X == e[0] and self.Y == e[1]: for i in range(0,len(points)): tmpL = []

edges = [] vertex = [] tempPoints = pointL[:] rand = random.randint(0,len(pointL)-1) vertex.append(pointL[rand]) tempPoints.pop(rand)

153

SCRIPTS

EMERGENT TECHNOLOGIES & DESIGN

8.1 ###################################### ####

else: pass

####DIJKSTRA’S ALGORITHM##################

vertex.append(pointL[possiblePts[0] [1]])

while len(vertex) < len(pointL): possiblePts = []

pts = vertex[possiblePts[0][0]]. coord,pointL[possiblePts[0][1]].coord line = rs.AddCurve(pts,1)

tempPts = [] for i in range(0,len(vertex)): for j in range(0,len(vertex[i]. connected)): tmpL = [] tmpL.append(i) tmpL.append(vertex[i]. connected[j][0]) tmpL.append(vertex[i]. connected[j][1]) tmpL.append(vertex[i]. connected[j][2]) tempPts.append(tmpL)

### Network Evaluation ### ########################## import rhinoscriptsyntax as rs import math import random from operator import itemgetter ##########################

tempPts.sort(key = itemgetter(3)) for i in range(0,len(tempPts)): checker = [] for j in range(0,len(vertex)): if tempPts[i][2].X == vertex[j].X and tempPts[i][2].Y == vertex[j].Y: checker.append(1) else: checker.append(0) if math.fsum(checker) == 0: temp = [] temp.append(tempPts[i][0]) temp.append(tempPts[i][1]) temp.append(tempPts[i][2]) temp.append(tempPts[i][3]) possiblePts.append(temp)

154

####Select Network to be Evaluated### rs.LayerVisible(‘MINSPAN’,False) rs.LayerVisible(‘SPACE SYNTAX’,True) net = rs.GetObjects(‘network to be evaluated’,4) rs.LayerVisible(‘MINSPAN’,True) rs.LayerVisible(‘SPACE SYNTAX’,False) minSpan = rs.GetObjects(‘minimum spanning tree’,4) pt = rs.GetObject(‘base for text’,1) rs.LayerVisible(‘SPACE SYNTAX’,True) ###Compile curve lengths into a list### lenL = [] for i in range(0,len(net)): len = rs.CurveLength(net[i]) lenL.append(len)

ADAPTIVE FLUX MORPHOLOGIES

SCRIPTS

8.1 ##Enter Minimum Spanning Tree For Comparison### minLenL = [] for line in minSpan: len = rs.CurveLength(line) minLenL.append(len)

###Divide minSpan length by network length for performance min = math.fsum(minLenL) network = math.fsum(lenL)

performance = min/network rs.AddText(‘P =’ + str(performance),rs.

PointCoordinates(pt),200)

155

SCRIPTS

8.1

156

EMERGENT TECHNOLOGIES & DESIGN

SCRIPTS

ADAPTIVE FLUX MORPHOLOGIES

8.1 8-2 Pedestrian Nodes Location Grasshopper Interface

8-3 Impact Map Generation Grasshopper Interface

157

CHARTS AND TABLES

EMERGENT TECHNOLOGIES & DESIGN

8.2 Chart 8-4 District Density Method First Step: Chart shows estimated train length in correlation to district populations.

For example we focused on the district area of Agege.

Local Government Area

Agege

28,464

150

Ajeromi/Ifelodun

42,625

124

133,377

229

Amuwo/Odofin

24,143

50

Apapa

16,494

50

Eti-Osa

38,550

125

Ifako/Ijaiye

32,468

71

Ikeja

23,766

62

Ikorodu

34,994

70

Kosofe

57,206

78

Lagos/Island

15,892

83

Lagos/Mainland

27,678

65

Mushin

38,866

114

Ojo

45,383

90

Oshodi/Isolo

39,076

100

Shomolu

28,358

95

Surulere

34,013

115

Alimosho

158

Estimated light rail, Train Length passengers per Meters (use peak hour 2012 Railcap sheet)

CHARTS AND TABLES

ADAPTIVE FLUX MORPHOLOGIES

8.2 Chart 8-5 District Density Method Second Step: Assuming the total length of the train the Rail Capacity spreadsheet projects the total passengers per peak hour per direction.

Type of train control system Signaling minimum headway

55

Dwell Time seconds

35

Operating Margin seconds

25

TOTAL HEADWAY seconds

115

TRAINS PER HOUR

31.3

Blue indicates entries that can be altered but assume defualt inputs

Passenger per metre

8.0

Loading Diversity

0.8

Train Length metres

150

Train length adjusted to match passenger per peak hour demands from density

30,000

passenger per peak hour direction

ACHIEVABLE CAPACITY

159

CHARTS AND TABLES

EMERGENT TECHNOLOGIES & DESIGN

8.2 Chart 8-6 District Density Method Third Step: Station Spacing and Count.

The chart shows the breakdown of districts into densities.

Three categories (A,B,C) are used to distribute the station placements along the lines.

These are applied after ranking the populations of the districts.

Local Government Area

Agege The result was an allocation of the number of stations along the lines based off the range of populations from the districts.

Total Length of Track 7334

Density 2012

Distance

(per km2)

(feet)

Number of stations

84714 A

1430

8

3691

115514 A

1430

3

Alimosho

14594 3143

24006 B

2318

8

Amuwo/Odofin

11473 3546 1306

5979 C

3210

5

Apapa

4474

20591 B

2318

2

Eti-Osa

2769 5417

6682 C

3210

3

660

40687 B

2318

0

7840

17147 C

3210

4

4470

Ajeromi/Ifelodun

Ifako/Ijaiye Ikeja

3688

Ikorodu

3624

2280 C

3210

1

Kosofe

10344 11766

23426 B

2318

10

Lagos/Island

1344 1187

60890 A

1430

2

Lagos/Mainland

4786

47312 B

2318

2

Mushin

788 2445 2924

74031 A

1430

4

0

9562 C

3210

0

Oshodi/Isolo

1934

29074 B

2318

1

Shomolu

628 3508 1408

81487 A

1430

4

Surulere

465

49295 A

1430

0

Ojo

160

CHARTS AND TABLES

ADAPTIVE FLUX MORPHOLOGIES

8.2 Chart 8-7

Vav Tst Lst L Ns td vmax ds tjl tbr SM tom tsl

average speed time to cover single track section length of single track section train length number of stations on single track section station dwell time maximum speed reached deceleration rate jerk limiting time operator and braking system reaction time speed margin operating margin time to throw and lock switch

26 1029 7334 150 8 35 55 1.3 0.5 1.5 1.1 20 6

km/h seconds m m

16

Dark blue indicates output values

mph

District Density Method Fourth Step:

Dark grey indicates entries that can be altered but assume default inputs

seconds km/h m/s2 seconds seconds seconds

Grey indicates assumptions from defualt entries

seconds

Speed and Time.

In a spread sheet that output the time and speed estimates of a single length of track, we applied our results.

For a 150 m length vehicle over a 7324 m span of track it will approximately take 17 minutes.

This results is accurate assuming the length of the tracks and speed however needs refinement to correctly display the distance between the stations.

60

A

55

B

50

45

C

Completely Grade Separated

At Grade with Highway Crossings

Operation on Median Strip 50% Signal Pre-Emption

Average Schedule Speed (KM/H)

40

D

35

Operation on Median Strip No Pre-Emption

30 500 m = 1 640 feet

25

E

20

Mixed Mode or Transit Mall Chart 8-8

15

District Density Method Fifth Step:

10

Re-evaluation based on Speed.

5

0 0

1.2

0.4

1.6

0.8

1.0

1.2

Distant Between Passenger Stops (KM)

1.4

1.6

1.8

To confirm the steps of this method correctly predicted a feasible outcome, we checked the results. This chart shows a ratio of the distance between stations to an estimated speed.

We were able to confirm that our output provided a ratio that fell within this graph.

161

CHARTS AND TABLES

EMERGENT TECHNOLOGIES & DESIGN

8.2

G 20

R 01

G 19

R 02

G 18

R 03

g 04 g 03 g 02

G 17 G 16

R 04

G 15

G 14

R 05

R 06

G 12

G 13

R 07 R 08

R 09

R 10

R 11

R 12

R 13

R 14

R 15

Y 08

B 16

R 17

B 17

B 18

B 19

B 20

R 18

R 19

R 20

R 21

B 15 B 14

G 11

Y 07

B 13

G 10

B 12 B 11

Y 06 G 09 G 08

R 16

C 02

C 01

C 03

Y 05

C 04

y 01

y 02

C 05

C 06

y 03

C 07

B 10

y 04

y 05

y 06

C 08

C 09

C 10

B 09

G 07 Y 04

B 08

G 06

B 07

Y 03 G 05

B 06 B 05

Y 02

G 04

B 04 G 01

G 02

G 03

P 01

P 02

P 03

P 04

P 05

P 06

Y 01 P 07

P 08

P 09

P 10

P 11

P 12

P 13

P 14

B 03

P 16

P 15 B 02 B 01

162

P 17

P 18

ADAPTIVE FLUX MORPHOLOGIES

CHARTS AND TABLES

8.2 Chart 8-9 Graphic Network Schematic

Line Color P 04

P G g

Line Symbol Station #

Pink Line Green Line

Y y

Yellow Line

B

Blue Line

C

Cyan Line

R

Red Line

Interchange Station Airport National Rail Riverboat services

163

CHARTS AND TABLES

EMERGENT TECHNOLOGIES & DESIGN

8.2 Chart 8-10 All station categories

broken

4

Avg passengars/ daily

Category

Station #

R-01 , G-20

55,022.16

B

R-02, G-19

55,022.16

R-03, G-18

Avg passengars/ daily

Category

C-07, y-03, B-10

68,790.27

A

B

C-08, y-04

45,693.13

B

55,022.16

B

C-09, y-05

45,693.13

B

R-04, G-16

55,022.16

B

C-10, y-06

45,693.13

B

R-05, G-15

55,022.16

B

B-01

32,692.03

C

R-06, G-14

55,022.16

B

B-02

32,692.03

C

R-07, G,13

55,022.16

B

B-03, P-15

40,953.56

B

R-08

19,500.05

D

B-04

32,692.03

C

R-09

19,500.05

D

B-05

36,335.40

C

R-10

19,500.05

D

B-06

36,335.40

C

R-11

19,500.05

D

B-07

36,335.40

C

R-12

19,500.05

D

B-08

36,335.40

C

R-13

19,500.05

D

B-09

22,930.09

C

R-14

25,611.33

C

B-10, y-03, C-07

68,790.27

A

R-15

25,611.33

C

B-11

25,611.33

C

R-16

25,611.33

C

B-12

25,611.33

C

R-17, B-16

51,222,65

B

B-13

25,611.33B-13

C

R-18, B-17

79,894.09

B

B-14

25,611.33

C

R-19, B-18

79,894.09

B

B-15

25,611.33

C

R-20, B-19

79,894.09

B

B-16, R-17

51,222.65

B

R-21, B-20

79,894.09

B

B-17, R-18

79,894.09

B

C-01, G-08

55,022.16

B

B-18, R-19

79,894.09

B

C-02

10,494.13

D

B-19, R-20

79,894.09

B

C-03

23,318.56

C

B-20, R-21

79,894.09

B

C-04, Y-05

46,637.12

B

P-01, G-01

20,988.26

D

C-05, y-01

46,637.12

B

P-02, G-02

20,988.26

D

C-06, y-02

46,637.12

B

P-03, G-03

20,988.26

D

Station #

164

into

Lines

Lines

CHARTS AND TABLES

ADAPTIVE FLUX MORPHOLOGIES

8.2

Station #

Lines

Lines

Avg passengars/ daily

Category

y-04, C-08

45,693.13

B

D

y-05, C-09

45,526.08

B

10,494.13

D

y-06, C-10

45,526.08

B

P-07, Y-01

20,988.26

D

G-01, P-01

20,988.26

D

P-08

50,183.42

C

G-02, P-02

20,988.26

D

P-09

50,183.42

C

G-03, P-03

20,988.26

D

P-10

50,183.42

C

G-04

10,494.13

D

P-11

21,348.27

D

G-05

10,494.13

D

P-12

21,348.27

D

G-06

27,511.08

C

P-13

21,348.27

D

G-07

27,511.08

C

P-14

8,261.53

D

G-08, C-01

55,022.16

B

P-15, B-03

40,953.56

B

G-09

27,511.08

C

P-16

8,261.53

D

G-10

27,511.08

C

P-17

8,261.53

D

G-11

27,511.08

C

P-18

8,261.53

D

G-12, Y-08

55,022.16

B

Y-01, P-07

20,988.26

D

G-13, R-07

55,022.16

B

Y-02

10,494.13

D

G-14, R-06

55,022.16

B

Y-03

23,318.53

C

G-15, R-05

55,022.16

B

Y-04

23,318.53

C

G-16, R-04

55,022.16

B

Y-05, C-04

46,637.12

B

G-17

27,511.08

C

Y-06

23,318.56

C

G-18, R-03

55,022.16

B

Y-07

23,318.56

C

G-19, R-02

55,022.16

B

Y-08, G-12

55,022.16

B

G-20, R-01

55,022.16

B

y-01, C-05

46,637.12

B

g-02

27,511.08

C

y-02, C-06

46,637.12

B

g-03

62,781.69

C

y-03, C-07, B-10

68,790.27

A

g-04

62,781.69

C

Avg passengars/ daily

Category

Station #

P-04

10,494.13

D

P-05

10,494.13

P-06

165

STATION ANALYSIS

EMERGENT TECHNOLOGIES & DESIGN

8.3 Table 8-11 Pedestrian network generation for individual stations

B-03, P-15

B-08

P = 0.733282663083 ROI = 2.637425

B-04

P = 0.736598536306 ROI = 0.841789

B-09

P = 0.611356474015 ROI = 2.637425

B-05

P-03, G-03

B-11

B-06

B-07

P = 0.228842037465 ROI = 0.536612

P = 0.60068450932 ROI = 1.188706

P = 0.709953233937 ROI = 0.998674

P-10

P = 0.396945453757 ROI = 1.188706

P-06

P = 1.0 ROI = 1.188706

P = 0.778516923936 ROI = 0.536612

P-09

P-05

P-01, G-01

P = 0.695335331408 ROI = 0.841789

P-08

P-04

B-12

P = 0.610478485658 ROI = 1.188706

P = 0.510913379274 ROI = 1.188706

P = 0.555544757598 ROI = 0.998674

P = 0.651477037645 ROI = 0.841789

P-07, Y-01

P = 0.621327323944 ROI = 1.188706

P = 0.509717454709 ROI = 0.448948

P = 0.799289540087 ROI = 0.841789

166

P-02, G-02

P = 0.609653293019 ROI = 0.536612

P-11

P = 0.666895934201 ROI = 1.188706

P = 0.627846437331 ROI = 0.912951

STATION ANALYSIS

ADAPTIVE FLUX MORPHOLOGIES

8.3

P-12

P-18

P = 0.621794838328 ROI = 0.329769

P = 0.62029455157 ROI = 0.912951

P-13

Y-07

Y-02

Y-08, G-12

Y-03

P-14

P = 0.375691977684 ROI = 0.329769

P-16

P = 0.685996029271 ROI = 1.126642

g-03

P = 0.788654576918 ROI = 1.188706

P = 0.822509139744 ROI = 1.126642

P = 0.66746573792 ROI = 0.731118

P = 0.910355138512 ROI = 0.697548

g-04

G-06

Y-06

P = 0.877495203736 ROI = 0.329769

P = 0.617755945465 ROI = 1.126642

P = 0.534669893776 ROI = 0.731118

P = 0.646938943467 ROI = 0.329769

P = 1.0 ROI = 1.126642

g-02

G-04

Y-04

P-17

P = 0.669927281345 ROI = 0.731118

P = 1.07546723747 ROI = 1.188706

P = 0.640466859255 ROI = 0.912951

G-11

P = 1.0 ROI = 0.697548

G-07

P = 0.751512879274 ROI = 0.731118

P = 0.977313139224 ROI = 1.126642

167

EMERGENT TECHNOLOGIES & DESIGN

168

ADAPTIVE FLUX MORPHOLOGIES

169

BIBLIOGRAPHY

EMERGENT TECHNOLOGIES & DESIGN

BIBLIOGRAPHY 1. Felix, Bruno and Wolting, Femke (Producers), & van der Haak, Bregtje (Director). (2005). Lagos Wide & Close [Motion picture] Netherlands: Submarine BV. 2. Gabriel Dupuy. “Urban Networks- Network Urbanism.” Techne Press, 2008. p 20, 35 3. Hillier, Bill. 1996. Time is the machine. Space Syntax.

4. Hillier, Bill; Hanson, Julienne. 1984. The Social Logic of Space. Cambridge University Press.

5. Jiang, Claramunt & Klarqvist, 2000, 6. Katrina Stoll and Scott Lloyd. “Performance As Form.” In Infrastructure as Architecture, Designing Composite Networks.” jovis Verlag GmbH, 2010 p. 6.

7. Koolhaas, Rem. Obrist. 2011. Project Japan. Metabolism Talks. Taschen.

8. Oloto, E.N. and Adebayo, A.K. “The new Lagos – Challenges facing the Peri-urban areas and its relationship with its City cenre.”

9. Sanford Kwinter, Mark Wigley, Detlef Mertins, Jeffrey Kipnis. “Phylogenesis: Foa's Ark / Foreign Office Architects” Actar, 2004.

10. United Nations DESA Population division. 2009. World Urbanization Prospects: The 2009 Revision.

11. Unknown. “Transportation in Lagos Nigeria”. Online Article Accessed 08.23.12. pau|ipopo.hubpages. com/hub/Transportation-in-Lagos-Nigeria 12. Weinstock, Michael. “The architecture of Emergence. The evolution of form in nature and Civilization” Page 186 13. Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan.

Routledge.

PAPERS 1. “Amsterdam, NL, ‘Venice of the North”.pdf” 2. 2011. Asia’s “Infrastructure Mania”. SPECTRUMAsia. Issue 02 3. 2011. Station Capacity Assessment Guidance. Network Rail 4. Adamatzky, Andrew; Jones, Jeff. 2010. Road Planning with slime mould: If Physarum built motorways it would route M6/M74 through Newcastle', International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, volume 20, issue 10, paper 3065.

5. Barredo I. Jose; Demicheli Luca. 2003. Urban sustainability in developing countries’ megacities:

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modeling and predicting future urban growth in Lagos: Cities, volume 20, pp.297-310 6. Bozzone, Donna. 2004. Chemotaxis in Physarum polycephalum. Dept. of Biology: Saint Michael’s College, USA. 7. Chiaradia, Alain; Law, Stephen; Schwander, Christian. 2012. Towards a multi-modal space syntax analysis. A case study of the London street and underground network. ed. by M. Greene; J. Reyes and A. Castro. Santiago de Chile.

8. Delalex, Gilles. 2006. Go with the flow: Architecture, Infrastructure and The Everyday Experience of Mobility. University of Art and Design Helsinki: Vaajakoski 9. Gil, Jorge. 2012. Integrating public transport networks in the axial model. ed. by M. Greene, J. Reyes and A. Castro. Santiago de Chile.

10. Hillier, Bill. 2007-8. Using DepthMap for urban analysis: a simple guide on what to do once you have an analyzable map in the system. London: The Bartlett School of Graduate Studies 11. Immerwahr, Daniel. 2007. The politics of architecture and urbanism in postcolonial Lagos, 19601986: Journal of African Cultural Studies, volume 19, issue 2, pp. 165-186 12. Lamata; 2009; Lagos BRT-Lite: Africa’s first bus rapid transit scheme. Lagos 13. Nakagaki, Toshiyuki; Yamada, Hiroyasu; Tóth, Ágota. 2000. Maze-Solving by an amoeboid organism', Nature, volume 407, p. 470.

14. Network Rail. “Station Capacity Assessment Guidance”. May 2011. P 12 15. Siemens. “African Green City Index: Assessing the environmental performance of Africa s major cities.pdf” 16. Tero, Atsushi; Takagi, Seiji; Saigusa, Tetsu; Ito, Kentaro; Bepper, Dan; Yumiki, Kenji; Kobayashi, Ryo; Nakagaki, Toshiyuki. 2010. Rules for Biologically Inspired Adaptive Network Design', Science, volume 327, pp. 439-442.

17. Therakomen, Preechaya. 2001. Mouse.class: The Experiments for Exploring Dynamic Behaviours in Urban Space. Unpublished Master of Architecture Thesis. Department of Architecture, University of Washington p. 9,13 18. World Bank. Sustainable Development Department. “Lagos Urban Transport Project.pdf”.

171

BIBLIOGRAPHY

EMERGENT TECHNOLOGIES & DESIGN

WEBSITES

1. http://people.hofstra.edu/geotrans/eng/ch6en/ch6menu.html 2. http://www.youtube.com/watch?v=c9ER2E7tmHA 3. http://www.youtube.com/watch?v=5qcAh29b4TA 4. http://www.moneyweek.com/news-and-charts/economics/global/a-recycling-fraud-56116 5. “What is space syntax?”, http://www.environment.gen.tr/human-settlements/557-what-is-space-syntax.html; August 2012 6. “Express Rail Link West Kowloon Terminus, Hong Kong”, http://www.aedas.com/Express-Rail-LinkWest-Kowloon-Terminus-Hong-Kong

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ILLUSTRATION CREDITS

COVER http://pinterest.com/pin/156500155772017864/

ABSTRACT 0-1 http://bitacora.citythinking.net/2012_03_01_archive.html

CHAPTER 1: Introduction 1-1 http://www.yannarthusbertrand2.org/index2.php?option=com_datsogallery&func=wmark&mid=2041 1-2 http://bitacora.citythinking.net/2012_03_01_archive.html 1-3 http://www.freefoto.com/preview/23-83-12/Railway-Track

CHAPTER 2 : Domain Title http://www.flickr.com/photos/7186330@N02/3353773931/sizes/l/ 2-1 http://lsecities.net/media/objects/articles/measuring-the-human-urban-footprint 2-2 http://www.cittasostenibili.it/urbana/urbana_L_10.htm 2-3 http://www.fastcodesign.com/1665884/infographic-of-the-day-could-twitter-help-us-create-smarter-transit-routes 2-4 http://www.lablog.org.uk/category/lab1amb07/ 2-5 http://freeassociationdesign.wordpress.com/tag/landscape-infrastructure/ 2-6 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.160

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ILLUSTRATION CREDITS

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2-7 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.160 2-8 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.147 2-9 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.236 2-10 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.236 2-11 Zhongjie, Lin. 2010. Kenzo Tange and the Metabolist Movement: Urban Utopias of Modern Japan. Routledge P.145 2-12 http://upload.wikimedia.org/wikipedia/commons/6/6f/Piazza_della_Signoria.jpg 2-13 http://www.borebags.com/wp-content/uploads/2011/03/road-signs2.jpg 2-14 http://www.cocogeo.com/wp-content/uploads/2011/03/space_syntax_london.jpg 2-15 http://www.slickscience.com/wp-content/uploads/Physarum_polycephalum.jpg 2-16 Nakagaki, Toshiyuki; Yamada, Hiroyasu; Tóth, Ágota. 2000. Maze-Solving by an amoeboid organism’, Nature, volume 407, p. 470.

2-17 Tero, Atsushi; Takagi, Seiji; Saigusa, Tetsu; Ito, Kentaro; Bepper, Dan; Yumiki, Kenji; Kobayashi, Ryo; Nakagaki, Toshiyuki.

2010. Rules for Biologically Inspired Adaptive Network Design’, Science, volume 327, pp. 440 2-18 Adamatzky, Andrew; Jones, Jeff. 2010. Road Planning with slime mould: If Physarum built motorways it would route M6/M74 through Newcastle’, International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, volume 20, issue 10, paper 3065, page 8.

2-19 Adamatzky, Andrew; Jones, Jeff. 2010. Road Planning with slime mould: If Physarum built motorways it would route M6/M74 through Newcastle’, International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, volume 20, issue 10, paper 3065, page 11.

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ILLUSTRATION CREDITS

CHAPTER 3: Methods Title http://www.flickr.com/photos/nkiru/2575487729/ 3-1 http://i.huffpost.com/gadgets/slideshows/195760/slide_195760_452006_huge.jpg 3-2 http://aquariumprosmn.com/wp-content/uploads/2010/01/b2Dedeler-Burak3-09.jpg 3-3 http://www.scidacreview.org/0802/html/abms.html

CHAPTER 4: Site Lagos (Nigeria) Title http://www.ynaija.com/fashola-goes-hard-lagos-governor-signs-terminator-traffic-bill-into-law/ 4-1 Siemens. “African Green City Index: Assessing the environmental performance of Africa’s major cities.pdf” 4-8 Buses and mini-buses (Danfos) http://www.channelstv.com/home/wp-content/uploads/2012/07/Lagos-Danfo.jpg Taxi, private cars http://www.flickr.com/photos/18718768@N07/3186661852/in/photostream Motorcycles (okada) http://www.nairaland.com/495786/lagos-gives-okada-riders-fresh Railway http://thecitizenng.com/2012/09/01/page/2/ 4-9 Abiri Oluwatosin Niyi. “Vehicle Carbon dioxide (C02) Emissions within the Lagos Road Network Based on Traffic Flow. pdf.» Konsult du Logistics and Transport. Accessed October 6th 2012.

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CHAPTER 5: Research and Network Development Title http://thelongestwayhome.zenfolio.com/img/v5/p584465312-4.jpg 5-19 http://spatialanalysis.co.uk/2011/02/mapping-londons-population-change-2011-2030/ 5-22 http://www.google.co.uk/imgres?num=10&um=1&hl=en&client=firefox-a&rls=org.mozilla:en-US:official&biw=1537&b ih=811&tbm=isch&tbnid=6-fUQjNm2gzXzM:&imgrefurl=http://nyc-map.blogspot.com/2012/05/nyc-subway-map-pictures.html&docid=h5RfswTNoPB9GM&imgurl=http://4.bp.blogspot.com/-SgoHehKtg4k/TbdZQKv2XLI/AAAAAAAAA9c/ WoL2MQRyD84/s1600/NYC_Subway_Map.gif&w=748&h=716&ei=jl9LULrqH8L-4QS4poGoDg&zoom=1&iact=hc&vpx= 1099&vpy=172&dur=8333&hovh=220&hovw=229&tx=148&ty=123&sig=102751878842183820961&sqi=2&page=1&t bnh=133&tbnw=139&start=0&ndsp=34&ved=1t:429,r:6,s:0,i:102 5-24 http://www.google.co.uk/imgres?hl=en&client=firefox-a&hs=P4M&sa=X&rls=org.mozilla:en-US:official&biw=1537&bi h=811&tbm=isch&prmd=imvns&tbnid=wghBMmOKyycqlM:&imgrefurl=http://www.evl.uic.edu/davidson/CurrentProjects98/ET_VisualInfo/1st_Principle.html&docid=uTRY_gPXTsit1M&imgurl=http://www.evl.uic.edu/davidson/CurrentProjects98/ET_VisualInfo/EI_TokyoMapp.40.jpg&w=536&h=510&ei=3lJLUJ_dF8yB4AS3joHICQ&zoom=1&iact=rc&dur= 387&sig=102751878842183820961&page=1&tbnh=142&tbnw=150&start=0&ndsp=28&ved=1t:429,r:4,s:0,i:85&tx=93 &ty=88 5-25 Okata, Junichiro; Murayama, Akito. 2010. Megacities: Urban Form, Governance and Sustainability’. 2011, XIV, 418, p.19 5-64 Kone Corporation.”Planning Guide for People Flow in transit stations”. 2009. p. 12

CHAPTER 6: Local Implications Title http://img4.oldkids.cn/upload/21000/u19171/2012/09/05/tblog_20120905130430028327.jpg 6-7 http://www.prorail.nl/SiteCollectionDocuments/Publiek/Doc/Projecten/Asd_centraal/Centraal%20station%20overzicht%20bewerkt.jpg 6-9 http://theweboutside.com/wp-content/uploads/2010/09/b626246b.jpg 6-12 http://www.aedas.com/Content/images/pageimages/West-Kowloon-Terminus-Wins-Three-Awards-NewsWest-Kowloon-Terminus-Wins-Three-Awards-784.jpg

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6-13 http://ad009cdnb.archdaily.net/wp-content/uploads/2012/07/1341933398-integration-studies-of-the-station-and-the-proposed-commercial-development-1000x707.jpg 6-20 http://thepython.files.wordpress.com/2010/10/dsc_2194.jpg 6-48 Buses and mini-buses (Danfos) http://www.channelstv.com/home/wp-content/uploads/2012/07/Lagos-Danfo.jpg Taxi, private cars http://www.flickr.com/photos/18718768@N07/3186661852/in/photostream Motorcycles (okada) http://www.nairaland.com/495786/lagos-gives-okada-riders-fresh Railway http://thecitizenng.com/2012/09/01/page/2/

CHAPTER 7: Conclusions Title http://farm1.staticflickr.com/126/329062128_1423b2d787_z.jpg?zz=1 7-1 http://www.freeworldmaps.net/outline/maps/apian.gif

CHAPTER 8: Appendix Title http://farm5.static.flickr.com/4123/4879340530_dd938f9ďŹ&#x20AC;5.jpg

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ADAPTIVE FLUX MORPHOLOGIES

ARCHITECTURAL ASSOCIATION SCHOOL OF ARCHITECTURE GRADUATE SCHOOL PROGRAMMES COVERSHEET FOR SUBMISSION 2011-2012

PROGRAMME: TERM: STUDENT NAME(S):

Emergent Technologies and Design 4th Golnoush Jalali Javier A. Cardós Elena Dennis Goff Mary Polites

SUBMISSION TITLE:

Adaptive Flux Morphologies

COURSE TITLE:

Emergent Technologies MSC

COURSE TUTOR:

Mike Weinstock and George Jeronimidis

SUBMISSION DATE:

14th September 2012

DECLARATION: “I certify that this piece of work is entirely my/our own and that any quotation or paraphrase from the published or unpublished work of others is duly acknowledged.”

Signature of Student(s):

Javier A. Cardós Elena

Dennis Goff

Mary Polites