YEHEZKIEL - THESIS

Bandung City, 2018

COMPUTATIONAL CITY PLANNING WITH FUTURE URBAN MOBILITY SYSTEM

SUTD M.Arch Thesis YEHEZKIEL WILIARDY MANIK Supervised by SAM CONRAD JOYCE, KRISTOFFER NEGENDAHL

Title

Computational city planning with future urban mobility system

Theme

Agent based urban planning, computational self-organisation

Author

Yehezkiel Wiliardy Manik Master of Architecture Thesis 2017 / 2018 Singapore University of Technology and Design Department of Architecture and Sustainable Design

Period

01 . 01 . 2018 - 17 . 08 . 2018

Supervisors

Sam Conrad Joyce, M.Eng Assistant Professor Singapore University of Technology and Design Department of Architecture and Sustainable Design Kristoffer Negendahl, PhD, Ms. Civ. Eng Simulation Specialist Bjarke Ingels Group, Denmark BIG IDEAS

Pages

116

Page ii

Acknowledgement It is a pleasure to thank the many people who made this thesis possible.

My gratitude to my supervisors; Sam Conrad Joyce. With his enthusiasm, his inspiration, and his great efforts to guide me, sparking new ideas, he challenged me into new possibilities in the computational world. Kristoffer Negendahl, my ex-colleague and mentor, provided me with initial steps and directions in creating simulation algorithms.

Studio 6 friends, Clara, Wicks, Junwei, Gabriel, Ethan, Weiqi, Sikai, Pau, Junkai, Qiboon, and Marsya, together they made studio a home for me. The cheerful term 8 juniors and many friends who helped me with physical models and 3D prints.

I wish to thank my entire family for providing a loving environment for me. Both of my parents supported me financially. My brother and sister helped me with the meaningful photos. To them I dedicate this thesis.

To God be the glory, the best is yet to be.

“First life, then spaces, then buildings - the other way around never works.� - Jan Gehl

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Fig A1. “A Greener, Greater New York,” by Bruce Mccall (2012)

Page iv

CONTENT PAGE Content page Motivation Objective

v vii x

Abstract

01

Introduction City and mobility Bandung

02 03 06

Methodology General outline Phase 1 Phase 2 Phase 3

14 15 16 18 20

Phase 1 - Station Evaluation Transit Oriented Planning

22 23

Understanding the Units Logic and parameters Test case with Singapore Parameters with Bandung Case #1. District based placements Case #2. Population based placements Case #3. Current government's plan

26 28 36 40 42 44 46

Discussion

48

Page v

Phase 2 -Bandung with MRT System Suggested MRT network Urban planning TOD based simulation Simulation Parameter Simulation Result Discussion

52 56 66 70 74

Phase 3 - Master Planning Overview Neighborhood development Chosen site Bandung 2118

80 81 82 86 92

Conclusion Documentation References

50 51

100 102 104

Page vi

MOTIVATION Brief history The planning of Bandung started in 1800s to function as a capital city of the province, centre of the community, and main transit city of the railway system in West Java.1 The city grew uncontrolled until 1900s the government realised the importance of city planning, hence promoted the city status from secondary to primary city. In 1920, an airport was built in the city. Followed by the Japan

Fig A2. Bandung, as seen from South towards North (2018)

invasion and independence war, the city continued to grow until now. The existence of the airport restricted the urban vertical growth (Fig A2) and it is currently planned to be moved out of the city.

1 Social change in Bandung 1810 - 1906. Sobana, A. 2003

Page vii

Fig A3. Bandung’s emerging towers and her last greens (2018)

Page viii

Current city planning One of the goals of the current planning of Bandung is to improve the city quality and efficiency through development of urban infrastructure and mobility (translated).1 The immediate action by the government is to plan an MRT (Mass rapid transit) network that would reduce traffic congestion and needs for cars in specific areas. After the MRTs are placed, the development would start around the stations. This approach is known as TOD (Transit Oriented Development) approach. The city life quality would involve expansion and spread of the green areas too which currently is diminishing. (Fig A3)

City for people “Neither the city planners nor the traffic planners put city space and city life high on their agenda, and for years there was hardly any knowledge about how physical structures influence human behavior." Jan Gehl, a Danish urban planner and architect, in his book, Cities for People. He stressed that changes that should take place should be based on the existing people of the city, learn the behaviour then proposed a design that would empower the people.

The current plan of the MRT network and its TOD development are based on geographical reach. What if, the MRT network is designed based on the existing city density and demographic spread? Can TOD be based on this too? Does the city idea "for people and by its people" applied in the urban planning scale?

1 Long term Bandung development plan 2005 - 2025. Ch 3.4 Page ix

OBJECTIVE Bottom-up design approach Bottom-up developement would be a more democratic solution for the people. The predicted urban form will be a proposal, an alternative answer that fits a city where changes are slow. The objective is to develop an algorithm that allows the current arrangement of urban fabric to transform itself towards an achievable goal / direction within a desired time step. For instance, one of the goals could be an increase in green spaces as a replacement for slum areas near the water body within a span of 20 years. On a realistic comparison, we can take Singapore as a green growth reference (fig A4).

1986

2007

Fig A4. Singapore green space. (4th National Report to the Convention on Biological Diversity, P25)

Urban planning toolkit The study would produce a series of urban planning toolkit that would help in evaluation of the current condition, propose a change to fit a specific design goal, and show how the condition will be improved. In the long run and a more stable environment, architects and urban planners can use it in various urban scale and different places on Earth. The outline of the workaround is illustrated in figure A5.

Page x

DENSITY

ROAD NETWORK

TOPOGRAPHY MRT / UAM OPTIMISATION

WALKABILITY INDEX TRAVEL TIME MAP

TOOL 1

EVALUATION INITIAL DEMOGRAPHIC SPREAD

EP

URBAN DESIGN GOALS

ED IT ER BAS

ION AT

TIME ST

URBAN TYPES’ RULES AND OBJECTIVES

TOOL 2

DESIGN ITERATION PROPOSED DESIGN

USER INPUT

DEMOGRAPHIC SPREAD

TOOL 1

ALGORITHM OUTPUT PARAMETERS

Fig A5. Urban growth toolkit workaround

Page xi

WALKABILITY INDEX TRAVEL TIME MAP

What if UAM works The final goal of the study is to investigate an ideal urban form by using the above tool if Urban Air Mobility (UAM) system can be implemented as replacement for the city mass rapid transport. The drone system is one of the emerging engineering technology. It is rapidly growing and improvements are studied to stabilize its use and safety as public transport.

In order to do that, certain parameters must be created to distinguish the requirements that UAM will need as compared to the MRT train systems. The tool will be equipped to allow such changes, in terms of its noise / privacy distance from the transportation network to residential areas.

Fig A6. Urban Air Mobility as an alternative (Airbus, 2017)

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ABSTRACT

ABSTRACT The primary purpose of the research is to provide a goaloriented agent based urban simulation tool. An evaluation criteria was introduced for the current or proposed urban arrangement to measure the effectiveness of the citizens to navigate between parts of the city. The simulation would be based on transit oriented development (TOD) of what type of mass urban transportation would be available. The agents are representation of activities and number of people occupying the area. The parameters, behavior, and growth factor are set based on historical data. The simulation goals are derived from the city's long term plan for the land use allocation.

In this study, Bandung is used as a test case; a developing city which currently has no mass rapid transport system.

The first step is to propose a mass transportation system, which will be the basis for TOD based simulation The evaluation tool will be used to calculate the effectiveness of the options of assigning the MRT transit stations. The calculation takes the density, topography, and existing urban form. This bottom up approach is chosen to maintain the local qualities, less disruptive to the existing urban fabric, hence a more acceptable approach for the citizen.

The predicted model will be readjusted to fit into he current main streets and fixed areas. The resulting agents will direct the land use allocation and assigning of the FAR of the parametric buildings generation inside the area covered by the agent. The computed result, together with city's historical and cultural values give directions and main concept of the Bandung 2118 master planning.

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INTRODUCTION

INTRODUCTION

CITY AND MOBILITY Car invasion Major cities that emerged during modern era were designed to accomodate cars as the citizens' mains mode of transportation. Personal cars were the pinnacle of the social status; even until today. A city that relies heavily on cars as means of urban transportation has its own consequences on its public life. These cities were not designed on human's scale in terms of size and speed.

Design of the city was to fit with cars in context (figure B1.) Modern cities have 6 - 8 car lanes roads that divides the cities. Rather than to unite the population, the roads separate them. Gehl's mentioned in his book, "If you make more roads, you will have more traffic." While the goal of these roads is to allow rapid movement of the pedestrian, the result is often the opposite in city where cars regulation is lacking.

Fig B1. Signboards and advertisement for people in the cars

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INTRODUCTION

Fig B2. Life in between buildings

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INTRODUCTION

People's oriented city On average, people move from place to another slowly, about 5 km per hour. At this speed, what human sees and notice are different than those inside 60 km per hour vehicles. This stark difference results in how the cities are designed and where people would spend their time.

People's oriented city would be defined as a city that is designed to meet the need of its people; a liveable city where the citizens are active physically and socially. For this reason, the function of roads as social spaces should be reactivated. Daily commute from home to work, to ammenities should be comfortable and encouraging various optional activites in between. Therfore, to fulfill this social goal, planning of the land use must be done carefully.

Fig B3. People are still slow and small

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INTRODUCTION

BANDUNG Topography The city is located in the middle of the island. Most area is flat except some towards the North. Its location is surrounded by mountains which are visible from any area in the town. The city is sitting on an area which archeologists describe as a pre-historic lake, ranging about 800m to 1000m above sea level.

0m

> 2500 m

Fig B4. Bandung topography and its surrounding

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INTRODUCTION

Climate The prevailing wind comes from South-south east direction, takes about 15% of the annual wind. This occurs as the result of the geographical location; it is surrounded by mountains except on its North-West and mountain ridge on its North. Very little wind comes from North.

Generally, the wind are pretty calm, less than 5 m/s.

Bandung's temperature provides a nice environment for outdoor activities. The city was known as city of flowers back in 1980s, because of its cool climate all year round; making it feels like spring season everytime. Average daytime temperature is about 27o C, daily average is 23.2o C, and night time average is 18.9o C. N

NNW

NNE

NW

NE

WNW

> 5.0 m/s

ENE

W

E

1.5% 3.0% 4.5% 6.0% 7.5%

WSW

ESE

9.0% 10.5% 12.0% 13.5%

SW 0.0 m/s

SE

15.0%

SSW

SSE

S

Fig 11. Bandung's annual wind data

12 AM 6 PM

12 PM 6 AM

12 AM

JAN

FEB

MAR

APR

MAY

JUN

JUL

< 25o C

AUG

SEP

OCT

> 35o C

Fig B5 Bandung's dry bulb temperature distribution over the year

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NOV

DEC

INTRODUCTION

Demographics Unlike Singapore, the population is mostly made up by locals. There's a some level of diversity because many universities in Bandung are popular destinations from people from other islands, as well other nationalities. It is, however, twice as dense as Singapore. For the past decade, the growth is reaching its plateau, with almost 50:50 ratio between male and female. 14.3% of the current population has a degree.

SINGAPORE EQUATOR

5.6 mill

BANDUNG

2.4 mill

AREA

1:4

167.7 SQKM

719.9 SQKM

DENSITY

6:3

FOR EVERY 400 SQM 20 M

M 20

20 M

14,700 / SQKM

M 20

7,800 / SQKM

Fig B6. Bandung's size and density as compared to Singapore

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INTRODUCTION

Decorated Kampong PC:: Tribun Jabar

Terraced kampong PC:: CNN Indonesia

Bandung Grand Mosque PC:: Alaurang

Braga Street PC:: Tripadvisor

City square park PC:: Tribun Jabar

Movie Park PC:: Tripadvisor

Singles park PC:: Sebandung

Cihampelas Skywalk PC:: Okezone

Fig B7 Snippets of Bandung's public life

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INTRODUCTION

Understanding the grid The current road network is a centralised system where in the city centre there is an intercity main train station and airport at the North West. There is no inner city highway system, nor any mass rapid transport network.

All roads allow all kinds of vehicles on road, and mostly have parking areas on both sides of the roads. The main roads form layers of rings around the city center, the width can be up to 8 car lanes. The small roads ranges from 2 to 4 lanes width, mostly connecting the landed residential areas to the main road networks.

Intercity highways Province main road City main road Regional road Java railway City border

1:150000 on B5 3KM

1.5KM

Fig B8. Bandung's road network

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INTRODUCTION

Land use The current land use are mostly residential, the commercial area is focussed in the middle of the city and along the major roads. Intercity train station located in the middle of the city, near to the government institutions.

Green areas spread mostly at the North and in various areas of the city as parks or city's green conservation areas. Industrial facilities such as factories, warehouses, are located nearer to the Southern city borders while educational institutions are mostly spread towards the North.

Educational institutions High density residence Low density residence Green / outdoor park Commerce area Service-type commercial River Industries Civil / military area

1:150000 on B5 3KM

1.5KM

Fig B9. Bandung's land use (2017) Source: bandung.org

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INTRODUCTION

Major potentials 2018

Context: Mountain rings

Organisation: Centralised planning

Tropical, yet provide great climate for outdoor activities, conducive topography for cycling in most areas. With a carefully planned vertical growth, the city residences can enjoy amazing views 360o. This could be an attractive city's identity for its citizen and potential tourists.

Way finding is easier with centralised road network, as compared to regular grid cities. Allow special priviledges for the central developement.

Rapid and efficient movement from center space to outside..

Building of 20 storeys or more

Natural resources: Extensive rivers

Built environment: Mostly flat

Possible development for green - blue public facilities along the existing rivers, a major improvement for a sustainable future city.

Clean rivers can function as flood buffer system for flood risk areas in Bandung.

Allow fresh start for a new vertical growth. More adaptable for new public mass transport system to be implemented as compared to a developed vertical city such as Jakarta.

Fig B10. Bandung's major potential Page 12 of 104

INTRODUCTION

Major challenges 2018 To nearest angkot's network <5 mins walk <5 mins cycle <10 mins cycle >15 mins cycle

PC: PR Infrastructure: Public transport's lack of reach

Organisation: Lack of street diversity

The current public transport that is popular is about a car size, known as Angkot. Most of the citizen have to rely on personal motorised way to get from home to the nearest angkot pickup location. This result in heavy reliance on personal cars, motorcycle, or online service.

Public and pedestrian walking are occupied by parking cars. Street shops are taking over pedestrian spaces everywhere. Lack of access for culturally / historically major streets.

? ? ? ?

?

PC: Merdeka Natural resources: Unintegrated river front

Built environment: Dense and difficult access

Monotonous function of the rivers; for drainage purposes. Lack of pedestrian crossing links.

Lack of water-related activities.

Built area / land owned ratio of most residential spaces is close to 1:1. One lane width roads which limit heavily the access into the major residential complex.

Fig B11. Bandung's major challenges

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METHODOLOGY

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METHODOLOGY

GENERAL OUTLINE Bandung in 2050, 2075, 2118 The main objective is to visualise the future urban form of Bandung, derived from the existing arrangemenrt of road networks, mass public transportation system, and demographic spread and density.

At the moment, there is no main mass rapid transportation system in Bandung. The government has a plan to develop two MRT networks that comes across North to South and East to West. The MRT planning, however, was done based on geographical cover of the city. The first phase of the study would find an optimal placement of the mass public transportation network that fits Bandung.

The second and third phase will produce a suggested urban form with MRT trains and example of using the agents as a guide for master planning the ideal city. The outline of the research is illustrated as below.

DENSITY

TOPOGRAPHY

PHASE 1

EXISTING LAND USE MRT NETWORK

PHASE 2

URBAN ALLOCATION

50 TO 100 YEARS TIMESTEP

ROAD NETWORK Fig C1. Outlines of the research

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PHASE 3

FUTURE CITY

METHODOLOGY

PHASE 1 The aim of the first phase is to evaluate the existing city, propose a suggested transit locations for the MRT system, and provide a comparison with the current MRT plan. The network finding algorithm will be take the existing road network, density of the city, and topography.

The output would be a time map, which tells how fast in average people would take to travel to the nearest time

NUMBER OF STATION

ROAD NETWORK

GRID SIZE DENSITY

NETWORK EVALUATION

TOPOGRAPHY

USER INPUT ALGORITHM

LANDMARKS / IMPORTANT LOCATIONS

STATION LOCATIONS

TOOL 1

TRAVEL TIME MAP

WALKABILITY

LAND USE

OUTPUT PARAMETERS

WALKABILITY INDEX Fig C2. Workflow of the first phase

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METHODOLOGY

Network evaluation The evaluation would consider the number of stations, then test these number of points, considering points of interests and topography. The output would be the best average of time taken from each of the location to the nearest MRT station. The time travel time produced would be displaying the map, coloured with time needed.

Similar works have been done by Mapumental, figure 23, where the API displays how far a person can travel with public transportation from any point in map.

Fig C3. Public transport travel times to 5 Virgin Active Gyms in London, arriving 7pm, no more than 30 minutes. PC: Mapumental

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METHODOLOGY

PHASE 2 The second step is to simulate the city to form its owwn optimised urban form for a period of time. The current land use will be interpreted as grid maps, which each grid has its own types and properties; for instance, a 4 sqkm grid [area] of slum region [area type], with density of 12000 people in it [density]. Every year, ideally total slum area is expected to reduce about 5% [rules], making a modern residential area denser [rules]. The elaboration of the workaround is shown in the next page.

Transit Oriented Development Transit-Oriented Developments are a mix of housing, retail and/or commercial areas and amenities within walking distance of public transportation. TransitOriented Developments (TOD’s) are established within 400 – 800 meters walking distance of public transit with areas near transit increasing in density. TOD’s conserve land, encourage walking and bicycling, while reducing infrastructure costs and energy consumption.

In Singapore, this approach has been implemented in various locations. Areas such as Jurong West, Yishun, or Tampines (figure 24) are developed this way, where town activites and commerce centres are centralised near the MRT station.

Commerce Mixed use Residential Green space

Fig C4. TOD of Tampines area, each circle represents 200m. PC: URA

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METHODOLOGY

GRID SIZE

T 1.TXT T=1

LAND USE

TIME STAMPED URBAN DATA

LAND USE TO TEXT CONVERTER

PHASE 1

STATIONS URBAN TYPE PROPERTIES RULES

GOALS

T-100.T XT T = 100

EP

ED IT ER BAS ION AT

TIME ST

TOD / SELF ORGANISATION

TOOL 2

TIME STAMPED URBAN DATA

TEXT TO LAND USE CONVERTER

WALKABILITY

WALKABILITY INDEX

TOOL 1

TRAVEL TIME MAP

USER INPUT ALGORITHM OUTPUT

PROPOSED LAND USE

PARAMETERS

Fig C5. Workaround of the second phase with MRT

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METHODOLOGY

PHASE 3 The last stage of the urban planning tool research is to provide an example of application in using the agents to inform the building design and function.

Parametric Development One of the many different ways of interpreting the agents is through using parametric development of the roads and agents. The diagram on the right displays the workflow of how the agents can be transformed into building massings.

The user would select an area of interest (in terms of agents) and the solver would identify buildable areas where new development of roads will be made depending on the minimal area needed by the government for new roads. The generation of building blocks, parcels, and certain building forms (depending on the functions) are set to fit the desired form. For instance, commercial building would occupy larger footprint as compared to the residential buildings. Depending on the land parcel size, specific types of high-rise development may be assigned to provide variety on the housing typologies.

For this phase, a more in-depth discussion regarding Bandung's values and social changes in the future will be presented. This part, however, would not be done parametrically, but rather a non-quantifiable evaluation of the city's qualities and goals. These values would affect how the city would be designed and programmed. The parametric visualization would help in displaying the movement of people and functions and these suggestions are feasible economically optimised suggestions on urban function and spread.

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METHODOLOGY

FUTURE CITY

BANDUNG 2118 LAND USE ALLOCATION

SELECTED AREAS

MIN AREA

FUNCTION OCCUPANCY 1. ROAD SUBDIVISION

4. FOOTPRINT AND FAR GENERATION

BRANCHING ROAD WIDTH BICYCLE OFFSET PEDESTRIAN

5. RATIONALIZE MASSINGS

2. BLOCK GENERATION

MIN AREA 3. LAND PARCEL ALLOCATION

PLOT RATIO SQM / PEOPLE

GREENERIES

USER INPUT ALGORITHM OUTPUT PARAMETERS

Fig C6. Phase 3 workaround as continued from phase 2

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PHASE 1 -STATION EVALUATION

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PHASE 1 -STATION EVALUATION

TRANSIT ORIENTED PLANNING Bandung and Its Current Development Transit Oriented Development (TOD) has not been implemented in the current planning. Bandung's development follows what it is known as Transit Adjacent Developement or TAD. TAD is development of ammenities along public transport routes. This usually fits a city where there is plenty of public transport pickup points or personal cars as main transportation system. It can be seen by the surface parking dominance along the streets, lack of pedestrian or bicycle access routes.

In early city stage development, TAD approach provides diversity in landscaping the urban look. However, as the city grows and area becomes denser, TAD built city often face numerous hours of traffic jams and focus of the streetscapes are designed for people in the cars; large signboards, tall and lack of building details. For developing cities, TOD is a good solution in promoting active citizens and car-less city.

Educational institutions High density residence Low density residence Green / outdoor park Commerce area Service-type commercial River Industries Civil / military area

Fig D1. TAD development along the public transport routes. Source: bandung.org

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PHASE 1 -STATION EVALUATION

TOD vs TAD The contrast of the pedestrian friendliness between TAD and TOD areas can be noticed by the two images below.

The dependency on personal mobility devices is higher in TAD, while TAD promotes walking around transit areas, approximately about less than 15 minutes walking duration from station to desired ammenities.

The land use in the TOD is more vertical, clustered, and therefore higher density. TAD lacks of any functional connectivity to transit, if there is any; whether in terms of land-use composition, means of station access, or site design. *

Fig D2. TAD along Sudirman road. Source: sebandung.com

Fig D3. TOD around Jurong East MRT. Source: westgate.com

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PHASE 1 -STATION EVALUATION

Bandung's Current Public Transport The two current major public transport in Bandung are city pickup (angkot) and buses. Both systems have routes and mostly cover the major roads well across the city.

The main problem is both transportation system are allowed to pick and drop passengers anywhere. Often, they will wait on the side of the road while waiting for people and initiate traffic jams. There has been an effort by the governments to place pick / drop points but this is often ignored by the drivers and citizens. The culture of pick / drop anywhere along the route has been rooted in the community since the beginning.

Fig D4. City pickup - angkutan kota (Angkot). Source: K. Subrata

Fig D5. Public bus. Source: beritatrans.com

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PHASE 1 -STATION EVALUATION

UNDERSTANDING THE UNITS A. Geographical Distance (m) Considering all possible path, the colour of the grid represents the shortest distance for a person to travel to nearest station with land public transport. The person, however, has to walk/cycle to the nearest pick up point along the main road.

Fig D6. Shortest route geographically

B. Relative Time (RT) The speed that the public transport travel on a aroad is different depending many factor, in this case, wider road is assumed for a faster travelling time and vice versa. The result of differentiation in road types is varying travelling period, for this research is set as relative time, indication of distance over various speed depending of the roadtype.

Fig D7. Relative time needed for the fastest route

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PHASE 1 -STATION EVALUATION

C. Relative time - people / area (RTP/m2) The SI unit for energy is kg m2/s2. In this case, it describes the effort for people with certain acceleration to travel specific distance. Therefore, the relative time people per area is an energy density indication of how much energy is required in a specific area. This provides a sense of priority in urban planning; grid with higher density of people need to be closer to the transit station.

Fig D8. Cumulative effort per area

D. Topography - relative time - people / area (RTP/m2) This unit describes a more intimate look at the people in an area. The calculation takes a closer look at the walking/cycling distance from a grid to the closest main road and takes the topography into calculation. People who would need to travel up/downhill to reach the station will need to spend more effort (higher energy required).

Fig D9. Cumulative effort per area affected by topography

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PHASE 1 -STATION EVALUATION

LOGIC AND PARAMETERS Network Evaluation The workaround of the first phase is shown in the figure 22. First simulation is to evaluate the existing road network. The following figures illustrate a sample test case with various parameters. The current goal is to evaluate the efficiency in getting to specific train station(s) by using land transport system (bus, cycle, or walking), by considering the number of stations, road type, and terrain. This evaluation system will be the basis for finding the optimal locations / station numbers in Bandung.

Highway entry Highway Main road Secondary road Area boundary Terrain contour

Fig D10. Road network test case setup

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PHASE 1 -STATION EVALUATION

Grid size The grid size control the number of people / area. The obtained data from the government website is number people for specific district (figure 32). Therefore the sampling number can be divided into the grid density to obtain a number people for specific area. In the case below the color represents the number of people per 35000 sqm of rectangular area. The level division of the space determines accuracy, also the speed of the calculation.

1 KM

2 KM

4 KM

200 people

1200 people

Fig D11. Averaged number of people per 35000 sqm

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PHASE 1 -STATION EVALUATION

A. Station number (geographical distance)

Parameters

This section shows the basic calculation without altering any values / factors into the shortest route calculation.

The four images below illustrate the distance a person need to travel from any point in the map to the nearest station. The important observation is that, the entry access for the highway is only located at four specific points, which explains the areas along the highway to take longer distances compared to other routes.. The cases below are presented such the stations are aligned to the entry of the highways.

Station numbers

0M

5000 M

Fig D12. Geographical distance closest route Page 30 of 104

Station

PHASE 1 -STATION EVALUATION

B. Road weight factor

Parameters

The road weight is a variable that indicates the speed of the public bus to travel in different types of roads. The assumption here, bus stop is located every 100 m and people can access it from all types of roads except the highway. The effect of this can be seen in the case of 2 stations where at the entrances of the highway which are far from stations, relative time (RT) is faster than the areas geographically. Also, time travelled to nearest station is significantly improved for each addition of station.

Station numbers Road weight: 1. Highway : 1.0 2. Primary : 2.0 3. Secondary : 2.2

0 RT

5000 RT

Station

Fig D13. Relative time (RT), road weight based closest route Page 31 of 104

PHASE 1 -STATION EVALUATION

Density and topography For the proof of concept, a random density map is created.

To make understanding of the impact of the population density, a radial density is designed with varying number of people per grid from 100 to 1000 people. The contour is also placed as indicator of how the people would reach the road where the buses will transit. The access will only available for non-highway roads, and coloured by the how the people would go up / down to reach the roads or stay on the same ground elevation.

100 People

1000 People

0M

350 M

Highway

Doesn't climb or goes down Need to climb up Goes down the terrain

Fig D14. Density and topography calculation -150 M

Page 32 of 104

150 M

PHASE 1 -STATION EVALUATION

C. Density factor

Parameters

The calculation displays a representative of an area of total relative time based on the number of people who occupy the grid; relative time people (RTP). Almost, this can be related to power distribution map; if power is defined as amount of energy spent per unit time by human to reach the nearest station. Interesting observation in case with two stations below, where the RTP is lower near the other two entries of the highway system. Another observation, at areas where stations can be accessed directly by walking, the RTP is significantly lower (1 station case) even it is the most dense.

Station numbers Road weight Density

0 RTP

5000000 RTP

Station

Fig D15. Relative time people (RTP): overall sum of time by the people in that area Page 33 of 104

PHASE 1 -STATION EVALUATION

Which goes where

Parameters

The computation here is splitting the result based on which station is the nearest based on road weight. The color is a gradient from white to specific color which represents the station. The stronger the color is, the lower the RTP value, which is an indication of efficiency that the person from that area should take a bus that goes to the station. The white color display high RTP values, and shows that the person has an option to go to the other stations since either options would take him the same time and effort.

Station numbers Road weight Density

Closest

Furthest

Fig D16. Geographical distance closest route Page 34 of 104

Station

PHASE 1 -STATION EVALUATION

D. Topography factor

Parameters

For this level of analysis, the resulted data from the road factor is added an equation where a person need to travel upwards / downward or stay within an elevation level.

The level of how the topography affects the "effort" for the person to reach the station is parametrically controlled.

The added equation multiplies or reduced the road weight and density map accordingly. The clear effect of this equation can be seen by how certain areas have the RTP value lowered in areas which are steeper.

Station numbers Road weight Density Topography: 1. Affect rate: 2.0

0 RTP

5000 kRTP

Station

Fig D17. Relative time people (RTP) with added topography equation Page 35 of 104

PHASE 1 -STATION EVALUATION

SINGAPORE AS TEST CASE Pasir Ris, Tampines, Simei For quick study on how the tool looks like on the existing study, the research will visualize the different evaluations of Singapore East area as shown below. The data was obtained from www.singstat.gov.sg. The color of the map shows the density of each area per square kilometer.

Fig D18. Areas of interest, coloured by density per sqkm

Page 36 of 104

PHASE 1 -STATION EVALUATION

Road weight factor

Parameters

For the Singapore case, the limitation of the highway only for cars / motorcycles are removed. People are allowed to cycle and walk along the side of highway. The following map shows the distance travelled by the people from each area to the closest MRT station based on the road type. The highest relative distance is shown by the people travelling to Tampines East MRT, since people in the northen part, though less densely populated, have to travel far to reach the station.

Area/grid: 150 x 150 sqm Station numbers Road weight: 1. Highway : 1.0 2. Primary : 2.0 3. Secondary : 2.2

Pasir ris Average: 1818 RT

Tampines east Average: 3042 RT

Tampines Average: 1595 RT

Tampines west Average: 1296 RT Simei Average: 934 RT Bedok reservoir Average: 1259 RT

Upper changi Average: 1070 RT Expo Average: 1576 RT

Tanah merah Average: 1186 RT Bedok Average: 1239 RT 0 RT

4000 RT

Station

Fig D19. Relative time (RT), road weight based closest route

Page 37 of 104

PHASE 1 -STATION EVALUATION

Which goes where

Parameters

The following diagram shows different colour gradient of the relative time (RT) to respective stations from different areas of the map. Based on the density map in figure 39, the expected number of people to travel to the nearest station can be estimated. The analysis below can be treated as indication of the extent of service reach for each station. Expo station, even it seems to cover large areas, it services to relatively small amount of population.

Area/grid: 150 x 150 sqm Station numbers Road weight Density

Pasir ris 183894 people

Tampines east 205981 people

Tampines 116378 people

Tampines west 112000 people Simei 37915 people Bedok reservoir 90120 people

Upper changi 27709 people Expo 39370 people

Tanah merah 64227 people Bedok 43370 people Closest

Furthest

Fig D20. Geographical distance closest route

Page 38 of 104

Station

PHASE 1 -STATION EVALUATION

Density factor

Parameters

Taking the density into calculation, the map shows the areas where amount of energy for each area to reach the respective station. The representation below shows what is the average of energy spent per area to reach the nearest station, gives an idea of how much effort overall are spent. Most of the MRT with high average energy density are located in the high dense residential areas in Pasir Ris and Tampines areas, while low density is located in mostly industrial areas.

Area/grid: 150 x 150 sqm Station numbers Road weight Density

Pasir ris Average: 745 kRTP

Tampines east Average: 726 kRTP

Tampines Average: 681 kRTP

Tampines west Average: 682 kRTP Simei Average: 504 kRTP Bedok reservoir Average: 651 kRTP

Upper changi Average: 420 kRTP Expo Average: 261 kRTP

Tanah merah Average: 724 kRTP Bedok Average: 836 kRTP 0 RTP

2000 kRTP

Station

Fig D21. Relative time people (RTP): overall sum of time per 150 x 150 sqm

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PHASE 1 -STATION EVALUATION

IDEAL MRT PLACEMENT Road network

Parameters

The roads are divided into four types in the following studies. They are highway, primary roads, secondary roads, and tertiary roads. The highways have specific entry points as explained in the case study earlier. The rest of the roads are divided based on how fast a bus would be able to travel on each road.

Station numbers Road weight: 1. Highway : 1.0 2. Primary : 2.0 3. Secondary : 2.2 4. Tertiary : 2.5

Highway Main road Secondary road Tertiary road Area boundary City boundary

1 KM

2 KM

1:150000 on B5 4 KM 3KM

1.5KM

Fig D22. Road type and area boundaries

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PHASE 1 -STATION EVALUATION

Topography, grid, and density

Parameters

The grid size used for the study is 250 x 250m. The density data is based on the district population information obtained from the Bandung's statistic website. The grid size affects the accuracy of the calculation and information. The city itself, being located next to a mountain, has the sloping areas mostly at the North side.

Grid size : 250 x 250 m

Elevation 1200 m

700 m

1 KM

2 KM

4 KM

Population 2000 people

200 people

1 KM

2 KM

4 KM

Fig D23. Elevation level and population per 250 x 250 m grid Page 41 of 104

PHASE 1 -STATION EVALUATION

Case #1: District based There are total of 30 districts in Bandung, for this calculation 20 are selected because 10 areas are closely located to one another. This approach would be similar to how Singapore are placing the MRT stations. Following shows the location and how many people in the city would go to the respective station, together with the distance they need to travel.

Fig 45 Different districts

28165

119295

233200 104730

Nearest to

166943

Closest 143093

203705

73996

149407

73021

226154 52729 153808

46664

182239 109704

17205

218282

1 KM

2 KM

125725

Furthest 45482 Station

4 KM

Average

863

1966

2596 2081

Distance

1945 1741

2035

5000 m 1420

1772

1940

1952 52729 1679

1507

1740 2214 1935

1 KM

2 KM

1637 2082

1742

4 KM

Fig D24. Location of the 20 stations and averaged distance to reach Page 42 of 104

0m Station Average

PHASE 1 -STATION EVALUATION

The average time for the people in the city to reach the station seems to vary about 300 RT, which translates to 5 minutes if the nearest public transport has to reach nearest station with speed about 30 km/s plust walking distance time to the nearest bus stop. Taking the density into the equation, we can see how the highly dense areas are not well serviced yet if we are taking the district center method. However, this approach would provide better familiarity with the current urban fabric.

137

325

427 345

Relative Time

328

1000 RT 275

331

232

286

327

322 275 241

231

295 317

281

309

1 KM

2 KM

0 RT

359

304 Station

4 KM

Average

622

2500

Relative Time-People per 250 x 250 sqm

3854 3341

3424 2155

3582

5000 kRTP 1526

3003

2021

3772 1191 3211

1022

3621 2123 3501

1 KM

2 KM

660 2325

1161

4 KM

Fig D25. Relative time and "energy" distribution Page 43 of 104

0 kRTP Station Average

PHASE 1 -STATION EVALUATION

Case #2: Population based The aim for population - based station location is to lower the overall "energy" or kRTP value for each station.

Each station below derived from the center point of a cluster approximately 120,000 people. The diagram 48 on the right shows how each 10,000 people are located in each curve. The placements might need to integrate with the existing roads for easier access.

Fig 48. Each curve represents 10,000 people

28603

131355

170986 173911

88395

161301

Nearest to 56325

Closest

139494 147358

108123 83283

127964

108034

70564 133402

178552

132871

137982

1 KM

2 KM

Furthest

141283

103088

571132 Station

4 KM

Average

946

2850

2523 2080

2031

Distance

1992

1744

5000 m

1768 1516

1722 1587

1432

1333

1380

22304

1939 3057

1536 1570 1 KM

2 KM

2398

0m 2197

4 KM

Fig D26. Location of the 20 stations based on population average Page 44 of 104

Station Average

PHASE 1 -STATION EVALUATION

The overall energy map shows improvements. However, on the relative time map, it shows some area with less people had to take longer distances to travel to the neraest station. Such as the area in the north east, these areas are places with low density population. The population based approach, however, is a more democratic approach to have a lower energy arrangement for the station The algorithm can be improved by taking the area clustering to be more precise (e.g instead of 10,000 people use 1000 people).

151

479

382 353

339

Relative Time

337

284

1000 RT

268 236

213 333

272 245

229

490

379

193

254

1 KM

2 KM

0 RT

405

200

381 Station

4 KM

Average

684

3500

3043

Relative Time-People per 250 x 250 sqm

3647

3276

2717

1784

5000 kRTP

2354 2693

1733 2461

2848

3992

2027

1271 2623

3306

2866 2134 1 KM

2 KM

0 kRTP

2522

1212

4 KM

Fig D27. Relative time and "energy" distribution Page 45 of 104

Station Average

PHASE 1 -STATION EVALUATION

Case #3: Government's plan evaluation The current plan seems to cover geographical stretch of the MRT network, aiming centralised planning of the city development. It can be seen by how many people can access the nearest station. The strategy looks at where the current commercial area is, and focussing on transporting people there, rather than connecting in between places across the city.

170766

75486

Nearest to

135436

Closest

66289

88290 228544

128887

13233

126259 66289

142101

214840

51642

170971

98817 103876

2239135

2 KM

242413

102667

44440

1 KM

Fig 51. Two initial MRT lines planned by the government

Furthest

Station

4 KM

Average

2981

2628

Distance

3283

5000 m

1179

1305 2851

2388

529

1859 1400

1918

1219

2671

1967 2805

2237 1459

2743

4191

1528

0m Station

1 KM

2 KM

4 KM

Fig D28. Location of the 20 stations based on current plan Page 46 of 104

Average

PHASE 1 -STATION EVALUATION

The effect of the current placement on the existing urban fabric can be seen below in terms of relative time to reach and energy spent overall. The non-democratic approach of this placement would eventually "force" the people from the less dense areas to move towards the city central. The placement does not answer the possible congestion that happened at south-west area of the city, where it seems to be "high" in terms of kRTP.

553

540

Relative Time

658

1000 RT

223

266 554

482

110

352 247

589

378

230

504

361 518

393 298

1 KM

2 KM

837

236

0 RT

Station

4 KM

Average

Relative Time-People per 250 x 250 sqm

3802

5231

5537

5000 kRTP

1811

2573 6915 2729

4645

2081

4338 3160

4425 3817

5332 2462

4861

632

3731

2084

4288 0 kRTP Station

1 KM

2 KM

4 KM

Fig D29. Relative time and "energy" distribution Page 47 of 104

Average

PHASE 1 -STATION EVALUATION

DISCUSSION Top-down vs. bottom-up planning The government plan is a top-down approach where people are "forced" to migrate to the newly TOD areas as influenced by the stations placements. As it can be seen in figure 53, "high" energy areas are created in a number of places, which is an indication of how much effort the people need to make to reach the nearest station. This happen when there is a high density grid that needs to travel considerably far, which is a good indicator of which areas will have traffic jam or MRT congestions. This arrangement forces people who live far from the city to move closer to the nearest TOD developed area, making more space for green-spaces that the city is currently lacking of.

As opposed to top-down approach, a more democratic method would be a bottom-up planning; it can be seen from case 2. The idea is to let the current urban fabric decides where is the most ideal placements for TOD to start. The result is a less, more familiar, distruptive changes to the local people; possibly preserving moments / meaningful objects that the areas are currently wellknown of.

For instance, the current city center is full of historical monuments, along Braga road and its neighborhood.

The government's plan would be disruptive, as two train network is intersecting the area. While it would attract more people, keeping the original atmosphere would be challenging. The case 2 provides a more distant solution, yet it provides reasonably accessible (walkable) routes to Braga neighborhood.

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PHASE 1 -STATION EVALUATION

Fig D30. Braga road. Credit: Jevera photography

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BANDUNG WITH MRT SYSTEM

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BANDUNG WITH MRT SYSTEM

SUGGESTED MRT NETWORK LEMBANG

East - West line South - North line South circle line Circle line

CIMAHI SUBANG

SUMEDANG SOREANG

1 KM

2 KM

4 KM

3KM

Fig E1. Suggested MRT network and surrounding secondary cities

The motivation of the approach here is to allow easy access for people from all over Bandung to the center of the city. Also, provide future connection to the neighboring secondary cities around it. These stations are based on the population, where each represents the center point of 100,000 people. The algorithm allows for more subdivision for more precise location and number of stations, for this output the accuracy is 10,000 people with 20 stations. .

The suggested routes have some similarities with the government's plan while additional two extra routes are added along the south circle line and inner city circle line to extend the reach to the major residential and industrial areas. Additional stations can be added along the lines too to improve the access.

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1.5KM

BANDUNG WITH MRT SYSTEM

URBAN PLANNING Concentric Model

Fig E2. Concentric layers of city development. PC: Pearson, 2005

The concentric model idea was developed by Ernest Burgess in 1925.1 The theory display the distribution of social hirearchy and status. Chicago was one of the cities that has this urban structure. Clear division and layered based on economy status allow better wayfinding in cities, organised planning, and centralised system. Distances from the centre becomes important and influence the market pricing of lands and spaces in both residential and commercial areas.

There are some criticism about the cities developed in this way. Social division becomes clear and segregation might occur. As the city grows, it becomes difficult to expand because of the restrictive boundaries. In the long term there is a need for urban regeneration and gentrification.

Often such places results in more expensive property can be found in formerly 'low class' housing areas.

1

"The Burgess Urban Land Use Model". people.hofstra.edu.

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BANDUNG WITH MRT SYSTEM

Sector (Hoyt) Model

Fig E3. Sector (Hoyt) model for city development. PC: Pearson, 2005

Proposed by Homer Hoyt, a development from Burgess's model of cities where urban functions are distributed in slices of sectors stretching from the center, which usually represents the central business disctrict area.1 Each of the cluster has similar function and they are aligned with the major transportation routes. Cities like Bruges and Copenhagen are designed with this model. This allow expansion of functions, although it gets further from the city.

There will be some challenges to design a good and strategic public transportation since the organisation of functions are not regularly organised. Prices of spaces and land would be easier to determine (based on sector.

The sectors sometimes depend on the topography of the city. For instance, people who live in the higher areas are usually richer than those who live at the lower part of the city. Land use within each sector would remain the same because like attracts like. The industrial sector would remain industrial as the area would have a common advantage of a railway line or river.

1 Hoyt, H. (1939) The Structure and Growth of Residential Neighborhoods in American Cities Washington, Federal Housing Administration

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BANDUNG WITH MRT SYSTEM

Multiple Nuclei Model

Fig E4. Multiple nuclei model for city development. PC: Pearson, 2005

The model was developed by C.D Harris and E.L Ulman in 1945.1 They argued that cities do not grow a single nucleus but several separate nuclei. Each nucleus acts similar to a growth point.

It describes a city that develop with a central business district, or CBD to outskirts of the city near the more valuable housing areas to allow shorter commutes from the outskirts of the city. This produces nodes or nuclei in different parts of the city besides the CBD which results in the name multiple nuclei model. The aim was to produce a more realistic, more complicated city model.

The model allows flexibility on city growth and emergence of new functions. The complexity is the result of the "human scale" development, a bottom top transformation guided by its own people. There are other models proposed by Ebenhezer Howard, Garden city model with multiple small towns. Singapore is one of the example of Howard's model.

1 Harris, Chauncy D.; Ullman, Edward L. (1945-01-01). "The Nature of Cities". The Annals of the American Academy of Political and Social Science. 242: 7â&#x20AC;&#x201C;17 Page 54 of 104

BANDUNG WITH MRT SYSTEM

Bandung's Urban Model

Commerce Greeneries Mixed use Residentials Fig E5. Simplified diagram of Bandung urban allocation

The initial city started as a concentric model where main commercial areas, hotels, and city parks are located in the middle. The main roads are reaching to the edges of the city reaching the residential areas. The city is enveloped by dedicated green areas, especially towards the north.

As the time progressed, there was a need for closer commercial centers. The residential spaces along major roads were eventually transformed into commercial spaces. As the city was expanding, there was a need to have dedicated green spaces, also some commercial areas started to emerge. The city became a hybrid model, which the society itself has shaped the old city to be what it is today.

The phase 2 of the thesis would predict the ideal form in terms of effectiveness of the pedestrian travelling of allocation and emergence of urban functions. The goal of the urban model can be seen as a more complex model that is developed by the citizens with some constrains of the government's input and distribution of each urban function in the city.

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BANDUNG WITH MRT SYSTEM

TOD BASED SIMULATION Time step self organisation simulation The tools take the initial urban data and store it as a text file. It allows flexibilities in setting up the properties and behaviors of how different urban functions relate / interact with one another. Specific goals can be set in the the system as a direction where the city should be transformed into. The main calculation takes consideration of multiple stakeholders involved in the city development, mainly from the perspective of the citizens. The diagram on the right illustrates in detail of the calculation for each time step.

Scopes The simulation model would take each grid as an "agent."

Each agent has properties that govern the relationship with other types of agent. For this research, the types are limited to a number of functions. They are residential, slum, commercial, mixed, fixed, and green agents.

Due to simplification of function in an area, size of the agent becomes important such that it is not too large as representation or too small for calculation.

Residential agents would be dedicated areas ranging from landed houses to high rise apartments depending on the number of people / area of each respective agent.

In the simulation, this type is allowed to change density, merge with similar agent, or transformed into others if needed.

Commercial agent represents spaces which most spaces are used for commercial purposes. They have commercial values and have possibilities to appear when there is lack of the agents within specific range.

Mixed use agent (experimental) emerges when commercial and residential spaces have to merge under certain conditions. Slum area, fixed, and green spaces will have its own custom properties which will be explained in detail in later section.

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BANDUNG WITH MRT SYSTEM

PROJECT DIRECTORY

T = 03 T = 02

T = 01

URBAN TYPE PROPERTIES RULES SELECT / LATEST TIME

DYNAMIC PROPERTIES

TIME = N

TOOL 2

PROPERTIES TO TEXT

READ URBAN DATA 1.

2.

3.

LOOPER

CONSTRUCT AGENTS REGION CLUSTERING TRANSIT SYSTEM RECOGNITION PROP

CONSTRUCT TRANSFORM 1.

2.

3.

CITY CHECK MONTE-CARLO AVERAGING CELLULAR AUTOMATA

APPLY TRANSFORM 1. AGENT SHIFT AND CHANGE 2. NEW AGENT EMERGENCE 3. TIME N+1 GENERATION

PROP.tx

GOALS AVERAGING SETTING

TIME = N + 1

T = N+1

VISUALIZE TEXT TO LAND USE

EVALUATIONS

Fig E6. Tool 2: time step self organisation simulation

Page 57 of 104

t

BANDUNG WITH MRT SYSTEM

Agents

The agent is a representation of urban function within a specific boundary. The city, or assigned area, will be represented by group of agents. Diagram on the right shows the different types of agents and information for each type. The agent text file contains the information and details, each agent has its own line in the text file.

The format of the text file is described as below: Line 0 : Time information Line 1 - 2 : Global size of the city Line 3 .. : Agents, properties are separated by coma {Corner 1, Corner 2, Function, Density, ID1, ID2, Distance to nearest MRT network} Location contains the information of the two corners (Corner 1, Corner 2) of the agents. It gives information of the center point and averaged elevation of the region that the agent represents. The two points create a rectangle, a boundary which we can use to measure the area covered.

Function, in integer, provides information of the agent type which largely occupy the space or has substantial effect on the calculation (e.g space of high-end landed residential housing which has plenty of greeneries is considered as residential agent.). Fixed agent is agent which will not change throughout the calculation because of its historical / functional importance.

Density, in people/sqm. In residential and slum type agents, the property describes the total population occupying the area. In green and commercial spaces, the value describes the total population who have immediate adjacent access to this agent.

ID and Distance to network are the properties that responsible for evaluating adjancency between the agents and the relation to the MRT network. The ID is in string format of XXXXYYYY where x and y are the index of the global agent array.

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BANDUNG WITH MRT SYSTEM

T = 01

1. LOCATION 2. DENSITY 3. AREA 4. ID 5. NEAREST DISTANCE TO MRT

AGENTS

RESIDENTIAL 1. LOCATION 2. AREA 3. ID 4. NEAREST DISTANCE TO MRT

COMMERCIAL 1. LOCATION 2. DENSITY 3. AREA 4. ID 5. NEAREST DISTANCE TO MRT

SLUMS

1. LOCATION 2. AREA 3. ID 4. NEAREST DISTANCE TO MRT

GREENERIES 1. LOCATION 2. AREA 3. ID

STATIONS

1. LOCATION 2. AREA 3. ID

FIXED Fig E7. Agent types and its properties

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BANDUNG WITH MRT SYSTEM

Constructing Settings

These three properties set up boundaries, determine how the agents relate to one another and setting rules for emergence or take over.

Goals are sets of global controls and parameters, to set boundary, the limit of maximum density, and total desired area in a city (e.g 40% green areas by 100 years of the entire city). To help with some decision making, the goal has priority input for each of the agent type, certain type has priority to emerge when the total area fall too low from what is required.

Averaging setting helps in understanding the current urban spread in neighborhood level. The properties control the emergence of the new agent type. The calculation take the inputted radius and sample a percentage of agents within this region. Example of workaround at this level would be, if there is less than 3 commercial agents within radius of 2km of the test agent, the agent would convert itself into commercial agent while the population inside will be distributed to its neighboring agents. The same applies to green type agents in order to increase diversity and even spread of these functions througout the city.

Cellular automata properties control the immediate relation to the adjacent agents (ID1, ID2 +1, -1). The direction is determined based on the nearest transit station. If TOD approach is used as a guide for the simulation, the global direction will determine how the population will distributed annually and to which type people will go and take over. There is also local direction which describes the distance from the network system.

At some phase of the decision logic, residential spaces will move further away from the mass transport network because of the noises.

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BANDUNG WITH MRT SYSTEM

CITY GOALS 1. MAXIMUM DENSITY 2. AREA GOAL 3. EXPANSION PRIORITY

MONTE CARLO AVERAGING 1. CONTROL NEW EMERGENCE 2. GROW, TAKE OVER OR MERGE ID1+1, ID2+1 ID1, ID2 ID1-1, ID2-1

1. GROW / DECAY 2. CHANGE TO OTHER TYPE CELLULAR AUTOMATA

Fig E8 . Three different level of setting up the parameters

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BANDUNG WITH MRT SYSTEM

Applying Transformation For Each Agent The diagram on the right shows the sample workaround on how each agent is processed based on the set properties, setting, and existing urban composition. The calculation is clustered as group of agents with 1 nearest station.

Initial time (Time = N), before the calculation of the next generation of agents, the agents are constructed based on the latest time-file in the working folder. The basic information is shown as shown. Station vector is also obtained to find which main and secondary target agents will be affected in creating the cellular automata transform. The population is changed based on the growth property (possible to decay too).

Time = N + 1 State 1. For the case when the population has to decay and reaches zero or negative population, the agent has to change to new type and its population will start from zero. The first change will be based on the city goal, to see which type has priority and has not been achieved.

The change, however can be overridden by the result of the monte carlo averaging. If within certain radius, there is lack of other functions (and sorted by its priority), the new function of the agent will be that most priority which is missing in that radius. (e.g there is no green space within 1 km radius, if the agent has to change it will be a green type agent, given green is the top priority).

State 2. If population transfer is possible, it will affect the main agent, if it reaches its maxium capacity, the transfer will go to the secondary agents. In cases where all reached maximum capacity; the agent would not change.

If the transfer causes any of the agent's population to be zero or less, the respective agent will undergo State 1 transformation.

Statistic. The statistic provide global information of the city, consisting total area cover for each agent type.

It is mainly used as check if goal for each type has been achieved or not. The information is constantly updated after each generation of the agent to avoid repetitive transformation.

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BANDUNG WITH MRT SYSTEM

TIME

AGENT TO CHECK

ID1+1, ID2+1

0024 0152

H WT GRO

N

IO ECT

DIR

+ ID1

NEAREST STATION

ID2+

INITIAL AGENT: A

APOPULATION PROPERTY 1 AGENT ANNUAL GROWTH

TIME: N ID POPULATION FUNCTION AREA

0024 0152 125 RESIDENCE 10000 SQM

TIME = N TIME = N + 1

APOPULATION IF

POP ≤ 0

POP > 0

STATE 1 AGENT TYPE CHANGE

STATE 2: TOD GROWTH TARGET AGENTS

S

SECONDARY ID1+1, ID2

PROPERTY 2 CITY GOALS RESULT

GROWTH DIRECTION

OVERALL AREA, PRIORITY

MORE RESIDENCE?

MAIN TARGET (M) ID1+1, ID2+1

A

MORE GREEN?

ATYPE = FUNCTION

MORE COMMERCE?

SECONDARY ID1, ID2+1

IF

LESS SLUM?

MPOP < LIMIT NO

YES

UPDATE STATISTIC

IF

APOP= APOP - AMRATE YES

PROPERTY 3 MONTE CARLO

SPOP < LIMIT NO

APOP= APOP - ASRATE

APOP= APOP

RADIUS, SAMPLE, PRIORITY

IF

NO RESIDENCE?

ATYPE = FUNCTION

NO GREEN? NO COMMERCE?

POP ≤ 0 STATE 1

POP > 0

NO SLUM? UPDATE STATISTIC

AGENT: A

ID POPULATION FUNCTION AREA

0024 0152 NEW NEW 10000 SQM

AGENT: A

STATE 2 TIME: N + 1

STATE 1 TIME: N + 1

ID POPULATION FUNCTION AREA

Fig E9 . Decision making logic flow for each agent Page 63 of 104

0024 0152 NEW RESIDENCE 10000 SQM

BANDUNG WITH MRT SYSTEM

Grasshopper Setting: Phase 2 The components used here are custom built based on the algorithm and logic explained previously in C# custom components or Visual Studio 2018.

Time 0 Initializer. The components grouped in blue areas are the initializer for the city. It reads the current land use of specific points, takes the density, topography, and evaluate the station locations. It also assigns automatically the ID, based on the relative location between each agents such neighboring agents can be find by means of dictionary call.

Tool 2. The calculation start with tool 2. The properties and goals are inputted into the setting constructor.

The main computing component takes 3 inputs to run: setting, statistic, and agents. At the absence of setting and statistic, it will run everything on default (zero growth and zero goal) when running input is set to true.

The looper takes latest time file and write the time N+1 agents into next text file.

Tool 1. For each calculation, the performance of the city is evaluated. This part takes the result of the road network and compare it with the agents density and function. The explanation of tool 1 is described in the phase 1 of the thesis.

Agent visualizer. These components contain various visualization options from visualzing the agents' function, density, RTP/area, and other useful data.

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BANDUNG WITH MRT SYSTEM

Time 0 initializer

Properties setting

Goal setting

Time = n

Loopers

File write / read

Recompute

Time = n + 1

Fig E10. Tool 1 and Tool 2 setting

Network evaluation

Visualizations

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BANDUNG WITH MRT SYSTEM

SIMULATION PARAMETERS Initial agents

Agent Statistic

There are three main inputs for the initial agents; location, land use, and density. These data is obtained from various sources such as the government website about the current land use and density distribution. The algorithm will sort out the agents based on each location and allocate ID for each agent.

Residential: Areas: 85.03 sqkm Population: 963 892 Number: 1352 agents Density: 11 339 ppl/sqkm Perc Area: 45.8%

Size of the agent chosen was 250m by 250m. This approximation for land use provides sufficient representation of people and future growth. In terms of walkability, the area can be covered less than 3 minutes. Topography changes within this area would be considered minimal. The picture below shows the area size in relation to Singapore context.

Commercial: Areas: 36.16 sqkm Population: 536 570 Number: 575 agents Density: 14 904 ppl/sqkm Perc Area: 19.5%

Slum area was manually selected as there is no geographical data could be found. The selection is described in picture E12, where highly dense residential can be observed from above. The initial agent of the city after careful research is shown by the picture E13.

Slum: Areas: 39.18 sqkm Population: 980 032 Number: 623 agents Density: 25 129 ppl/sqkm Perc Area 21.1% Green: Areas: 18.11 sqkm Number: 288 agents Perc Area: 9.8%

Agent size: 250 m x 250 m

Fixed: Areas: 7.16 sqkm Number: 114 agents Perc Area: 3.8%

Fig E11 . Singapore as comparison

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BANDUNG WITH MRT SYSTEM

RESIDENTIAL

SLUM

COMMERCIAL

GREEN

Agent size: 250 m x 250 m

1 KM

2 KM

4 KM

Fig E12 . Slum and other agent type classification

Station agent Residential agent Slum agent Commercial agent Green agent Fixed type agent

1 KM

2 KM

3KM

4 KM

Fig E13. Time = 0 (2018) Bandung representation

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BANDUNG WITH MRT SYSTEM

Agent Properties

Parameters

In this section, it will be explained the rationale of setting the growth, goal, and agent relationship properties for each agent type.

Residential: ToSlum = - dynamic (EQN) ToResi = dynamic (EQN) ToComm = dynamic (EQN) Max = distance based

Goals. The goal will be aligned to the long-term city goal (50 years) which was revised in 2014, known as RPJPD (Rencana Pembangunan Jangka Panjang Daerah Kota Bandung) - Long term masterplan of city-level planning of Bandung. The detail plan can be found in the government website.1 Growth. The growth indicates population growth that exist in the region bounded by the agent. The rate of growth is a projection based on the historical data of countries that grew from developing to developed status. The number is currently set to 0.7% following the UNESCO prediction of population growth. 2 Growth for slum areas, however, is an exception where the rate is set as negative, which will be compensated in the increase of other functions to maintain 0.7% global growth. Maximum population for each agent is capped depending on its distance to the network.

Residential and Slums. The properties set for this function is based on migration and economic growth rate. Considering how land value increases as it get nearer to the transit areas, the economic growth is a good estimation of how the movement of residential agent into another residential agent would be. This value would vary for each year based on mathematical model of GDP annual projection of Indonesia as shown in figure E14.

Commercial The properties here are representing the similar characteristic with residential. However, in the visualization of the people who live in that area which also used as commercial function. Similar to mixed-use or town based distribution.

Green. The green agents' properties will only affect the area change / take over.

1 " Rencana Pembangunan Jangka Panjang Daerah (RPJPD) Kota Bandung " - Bandung. https://portal.bandung.go.id/assets/download/RPJPD_2014.pdf. Retrieved 2018-06-30 2 "World Population Prospects - Population Division - United Nations". esa.un.org. Retrieved 2018-06-29.

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Commercial: ToSlum = - dynamic (EQN) ToResi = dynamic (EQN) ToComm = dynamic (EQN) Max = distance based Slum: ToSlum = dynamic (EQN) ToResi = -dynamic (EQN) ToComm = -dynamic (EQN)

TIME = N

RESIDENTIAL

RESIDENTIAL

COMMERCIAL

COMMERCIAL

SLUMS

SLUMS

GREENERIES

GREENERIES

STATIONS

STATIONS

Fig E14. Relationships between agents

FIXED

FIXED

Fig E15. Projected economic growth by IMF. imf.org EQN: 6.12 - 0.0926(t-2010)+0.00667(t-2010)^2 = GDP growth Page 69 of 104

TIME = N + 1

BANDUNG WITH MRT SYSTEM

BANDUNG WITH MRT SYSTEM

SIMULATION RESULT Station Performance Evaluation

Parameters

As the evaluation criteria, the agents are clustered by which stations they will go to (calculated from the modified shortest path.) The output will be groups of agents of same station target, in the case below the color represents which station it will go (Figure E16). For each agent in each group, RTP value is calculated. Averaged RTP of each group is obtained for each time step and used as performance index of the station.

The time 0 statistic (Figure E17) shows how each of the station is performing. The color is matched with the RTP distribution map. The initial city composition is presented in the pie chart on the left, while distribution of people living in specific areas are presented in the right pie chart.

Residential: Growth 0.7% Goal 40%* Priority: 2 Commercial: Growth 0.7% Goal 25%* Priority: 3 Slum: Growth -0.7% Goal 0% (eradicate slum) Priority: 0 Green: Goal 35% Priority: 1 *RPJPD 2014, Point 4.2.2.5 Page 91/272 **RPJPD 2014, Point 2.3.1.2, Page 64/272

16

2

3

1

4 5 21

12 11

13

6

14 18

10

17

9

8

15

7 19 20

1 KM

2 KM

4 KM

Fig E16. Stations numbering for the RTP averaging

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BANDUNG WITH MRT SYSTEM

684

3500

3043 3647

3276

2717

1784

2354 2693

1733 1271

2461 2848

3992

2623

3306

2027

2866 2522

2134

1212

Avg Relative Time-People 1 KM 2 KM 4 KM per 250 x 250 sqm Time = 0 5000 4000 3000 2000 1000 0 kRTP

1

2

3

4

5

6

7

8

9

10 11 12

13 14 15 16

17 18 19 20 21

Station Agent Function Distribution

Population Distribution

Time = 0

Time = 0

Station agent Residential agent

Slum agent Commercial agent Fig E17. Statistic at Time = 0

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Green agent Fixed type agent

BANDUNG WITH MRT SYSTEM Land Use

Averaged RTP

Time = 0

Time = 10

Time = 20

Time = 30

Time = 40

Time = 50

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Function Ratio

Population Ratio

BANDUNG WITH MRT SYSTEM Land Use

Averaged RTP

Function Ratio

Population Ratio

Time = 60

Time = 70

Time = 80

Time = 90

Time = 100

Avg Relative Time-People

Legend

per 250 x 250 sqm 0 kRTP

2500

Station agent Residential agent

Slum agent Commercial agent

5000

Fig E18 Statistic of Time 0 to 100

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Green agent Fixed type agent

BANDUNG WITH MRT SYSTEM

DISCUSSION Averaged RTP/Area

Parameters

The main objective of the simulation is to find the best possible population allocation (in terms of efficiency to travel around the city - RTP), given sets of parameters, period, behaviors, and land use goals The RTP is calculated per agent at specific time. Mapping the value on all agents give us indication of areas that are performing inefficiently (high RTP/area).

Residential: Growth 0.7% Goal 40%* Priority: 2

To recall, the calculation of RTP is as follow (Phase 1): RTP = Relative Time x Population / Agent Area Where relative time is calculated based on road network of various road types. The difference between road types are exceptionally small, except the highway, where travelling period is significantly faster. This was set because over time, the speed of each type will change. Therefore small differences would not affect the calculation, making population the only variable in the equation. For this simulation, the road types are kept the same throughout.

Improvement. The RTP of the agents over the times can be improved by strategically move the population to another agent which is closer to the transit station.

This will strategically move the population of agent with high Relative Time to a smaller one, result in lower RTP for the "giving" agent. This method, however, explains why certain areas are highly populated. These occurence happens mostly to those agents near the stations. The maximum capacity of each agent is varied, depending on how far the agent is located from the nearest station. The population spread will be further discussed in the later part of the discussion.

The average RTP for all stations are reduced by this method, as seen by the bar charts of time 0 and 100.

Some agents are observed to have high RTP value, this is as the result of the fixed agents blocking towards the transfer direction. The gradient maximum capacity was implemented to keep excessive accumulation of people in such cases. These high RTP agents are the best solution the system could achieve.

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Commercial: Growth 0.7% Goal 25%* Priority: 3 Slum: Growth -0.7% Goal 0% (eradicate slum) Priority: 0 Green: Goal 35% Priority: 1 *RPJPD 2014, Point 4.2.2.5 Page 91/272 **RPJPD 2014, Point 2.3.1.2, Page 64/272

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RTP Performance Distribution

RTP Performance Spread 1 KM

2 KM

4 KM

Time = 0

RTP Performance Distribution

1 KM

3KM

2 KM

RTP Performance Spread

4 KM

Time = 100

1.5KM

Avg Relative Time-People

Legend

per 250 x 250 sqm 0 kRTP

2500

Station agent Residential agent

Slum agent Commercial agent

5000

Fig E19. Bandung's stations performance before and after Page 75 of 104

Green agent Fixed type agent

BANDUNG WITH MRT SYSTEM

Land Use

Parameters

The first step of calculation for each agent is to recognize the city statistic. During a case when an agent can not migrate any people, it needs to change to a new type. The statistic solver (Figure E9) will find which new function it would be and update the new statistic for the next agent calculation.

In this simulation, the initial statistic shows that the land use for slum and residential areas are exceeding the target goal, while green and commercial agents are below the goal. The system will start with eliminating the excessive agents and try to replace it with a type of most needed priority.

Time: 100. The system tries to meet the goals with given parameters. The highest priority was set for green type.

Starting with 10%, the system managed to get 30% of the overall areas. Residential was reduced to 41% to meet the target goal as 40%. Commercial spaces were set as third priority, and this type is performing badly. The system was trying to fill the gap of the missing items of the first priority type, and in doing so commercial type has to sacrifice its function when its population is too low.

The slum was not completely eradicated, mainly because of high number of people occupying these areas. More time is needed to completely eradicate slum agents with the projected economic growth. The migration of the slum agent was set to move further from the station.

(Page 70, behavior parameter.)

The emergence was overriden by the monte-carlo averaging result, this was done to ensure variety within a neighborhood region for each agent type. (Set to 2 km radius, 60% sample size) The result of this method can be seen in random emergence of various types as effort in keeping variation of types exist within the 2km.

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Residential: Growth 0.7% Goal 40%* Priority: 2 Commercial: Growth 0.7% Goal 25%* Priority: 3 Slum: Growth -0.7% Goal 0% (eradicate slum) Priority: 0 Green: Goal 35% Priority: 1 *RPJPD 2014, Point 4.2.2.5 Page 91/272 **RPJPD 2014, Point 2.3.1.2, Page 64/272

BANDUNG WITH MRT SYSTEM

Area Ratio

45 Land Use 1 KM

2 KM

4 KM

Time = 0

19 21 10

Area Ratio

41 1 KM

2 KM

10

Land Use

4 KM

Time = 100

14 3KM

30

1.5KM

Legend

Station agent Residential agent

Slum agent Commercial agent

Green agent Fixed type agent

Fig E20. Agents change from start to end Page 77 of 104

BANDUNG WITH MRT SYSTEM

Population Spread

Parameters

The population will be the indication of what kind of residential development is needed in the area bounded by the agents. At the moment, Bandung is having some transformation by the emerging tower of residential and apartments. These emergences, however, are uncontrolled, and often located in unfortunate locations and causes traffic jams.

The parameters for the population takes growth percentage, where each year the number of people of that agent type changes. It can increase or decrease for every time step. The values are set to be constant throughout the simulation to be 0.7%, approximately in a neighborhood of 1000 people, 7 children were born annually. As comparison, Singapore's population growth is 1.3%.

The population of the agent is also influence by its position relative to the nearest station. Growth direction is obtained by taking the station as target, and center point of the agent as initial direction. This vector set distribution target, as explained in the previous section about the tool.

Initial composition of the city was set, based on density data of each of the district. Based on how many agents are sitting in that district, even population are used. This is justified by the lack of variety in the building heights in Bandung. As seen before with the aerial overview of the city, the houses, buildings are mainly flat and lack of variation.

The final composition, we can observe how certain agents are beginning to be occupied by a lot of people (about 3000 people in 62500 sqm, 5 people in 100 sqm).

As comparison, Singapore's densest area (Tiong Bahru) has density is 5 people per 100 sqm. These highly dense agents are located nearer to the stations, while some areas are left empty to be utilised as dedicated green spaces, which are desperately needed by the city. There is migration of people from the slum areas to residence and mixed used areas. (27% to 10%)

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Residential: Growth 0.7% Goal 40%* Priority: 2 Commercial: Growth 0.7% Goal 25%* Priority: 3 Slum: Growth -0.7% Goal 0% (eradicate slum) Priority: 0 Green: Goal 35% Priority: 1 *RPJPD 2014, Point 4.2.2.5 Page 91/272 **RPJPD 2014, Point 2.3.1.2, Page 64/272

BANDUNG WITH MRT SYSTEM

Population Distribution

39 Population Spread 1 KM

2 KM

4 KM

22

Time = 0

27

Population Distribution

53 1 KM

2 KM

Population Spread

4 KM

Time = 100

10 29 3KM

1.5KM

Population

Legend

per 250 x 250 sqm 200

Station agent Residential agent

Slum agent Commercial agent

3000

Fig E21. Population change, before and after Page 79 of 104

Green agent Fixed type agent

PHASE 3 - MASTER PLANNING

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MASTER PLANNING

OVERVIEW LEMBANG

East - West line North - South line South circle line Circle line

16

CIMAHI

2

SUBANG

3

1

4 5 21

12 11

13

6

14 10

18

17

9

8

15

20

SOREANG

1 KM

2 KM

SUMEDANG

7 19

4 KM

3KM

Fig F1. Suggested MRT network and surrounding secondary cities

In this phase, it will be discussed the interpretation of the simulated agents on the neighborhood level, about 2 km radius of the chosen station. The time span considered is 100 years (2118). The focus of this phase is around station 12. Station 12 is chosen because it is the current location of the central station and intersection of the North-South and East-West lines. The bounding box above show proximity where there are historical sites, river, and city hall.It will be discussed on how the simulated agents will help with development of secondary roads, FAR, buildable area, and footprint.

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MASTER PLANNING

NEIGHBORHOOD DEVELOPMENT Parametric Approach

Parameters

The final phase of the thesis is to show how the agents can be used as an aid for designing a master plan of an area. The chosen area is around the central station, which is also the intersection between north south and east west lines.

The first step will loop the area bounded by the main roads into appropriate subdivision of spaces. If the divided space still larger than the minimal area, it will subdivide further. The block generation takes the offset from the functions used from the roads for cyclist, pedestrian, and green spaces.

Allocated land parcel based on the minimal area size for each of the parcel. Based on the function and the number of people occupying the space, the footprint of the building is set differently. Commercial and slum will take more space while residential takes less. At this step, it is possible to immediately visualize what the city line as shown in the picture on the right. The adjustment of the massing is based on the surrounding and main concept.

These steps, however, does not provide a dynamic evolution of the street network as the population change.

The street, along the years, would likely to change and shift as the function around the building change. The next page shows the development through the different time files around station 13 and 14. The images show the population densification around the transit nodes.. (Red colour buildings)

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Step 1: Min Area: 40000 sqm Branching: 1 Step 2: Main Road: 12 m Cyclist: 1.5m Pedestrian: 2m Greeneries 2m Step 3: Parcel Area: 250 sqm Step 4: Commercial: 80%, 20 sqm/ppl Residential: 60%, 30 sqm/ppl Slum: 100%, 16 sqm/ppl Green: 100%

MASTER PLANNING

FUTURE CITY

BANDUNG 2118 LAND USE ALLOCATION 5. RATIONALIZE MASSINGS

SELECTED AREAS

BRANCH

FUNCTION OCCUPANCY

EVERY 40000 SQM

1. ROAD SUBDIVISION

MIN AREA

BRANCH =1 4. FOOTPRINT AND FAR GENERATION

BRANCHING ROAD WIDTH

MIN AREA

2. BLOCK GENERATION

BICYCLE OFFSET PEDESTRIAN

SUB LV

1. ROAD SUBDIVISION

3. LAND PARCEL ALLOCATION

MAIN ROAD: 8 - 16 M PLOT RATIO

SQM / PEOPLECYCLIST: 1.2 M

GREENERIES

PEDESTRIAN: 2 M

BRANCH = 1

SUB LVL 2

BRANCH

BRANCH

GREENERIES: 2 M

EVERY 40000 SQM EVERY 40000 SQM

BUILDABLE BLOCK

SUB LVL 2

BRANCH = 1 SUB LVL 1

SUB LVL 12. BLOCK GENERATION

1. ROAD SUBDIVISION 1. ROAD SUBDIVISION MAIN ROAD: 8 - 16 M CYCLIST: 1.2 M PEDESTRIAN: 2 M GREENERIES: 2 M

BUILDABLE BLOCK

MAIN ROAD: 8 - 16 M CYCLIST: 1.2 M PEDESTRIAN: 2 M GREENERIES: 2 M

BUILDABLE BLOCK

3. LAND PARCEL ALLOCATION 2. BLOCK GENERATION 2. BLOCK GENERATION

3. LAND PARCEL ALLOCATION 3. LAND PARCEL ALLOCATION

4. FOOTPRINT AND FAR GENERATION

Fig F2. Decision making of road and building plot allocation

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S

MASTER PLANNING

Fig F3a. Result of road subdivision and building plot allocation (Time 0 - 50)

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Fig F3b. Result of road subdivision and building plot allocation (Time 51-100)

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MASTER PLANNING

CHOSEN SITE City Hall and Central Station The site chosen is around station 12, where the central station is currently located. Buildings in red are important to Bandung city due to its political use and historical values. It is also intersection of two proposed lines (North - South and East West) where in the future they will extend to the secondary cities.

The existing station is part of the Java rail loop which connects major cities in Java island, stretching over 1500 km. Bandung is located at the intersection from Jakarta - Jogjakarta, and Merak - Surabaya. Known as the education city, where there are more than 5 national universities in the city, the station is mostly busy with students who came from all over Java to pursue their higher education here. During semester breaks and public holidays, the students would take the train from this station to go back. It is relatively a cheaper, more comfortable, and faster option as compared to airplanes or buses due to its extensive reach to the rural and secondary cities. The intimate identity with the students therefore demands a revitalization of the station to fit the need and empowering the youth that come to the city to pursue higher education.

Cikapundung river flows from the mountain ridge on the north of Bandung towards the Citarum river. At the past, the river was populated by slums on its sides.

Some factories used to dispose the waste there. The government decided to revitalize the use of the rivers for recreational use for the citizens. Cleaning up of unauthorized buildings, rubbish, and factories have been done the past decade.

City hall is currently used as public spaces for local events, campaigns, or rallies. The spaces are occupied by local for recreational purposes, attracting mobile hawkers to populate the streets. The street activities, however, often result in the traffic jams at various nodes around Bandung.

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MASTER PLANNING

1

2

3

4

5

8 6

7

9

10

1. Old Kina Factory 2. Bandung Electronic Center 3. Hospital 4. City Hall

5. Governor's officet 6. Bandung Central Station 7. Station Central Management 8. Polytechnic

Fixed buildings Cikapundung River

9. St. Peter Cathedral 10. Braga shophouses

Building footpring Dedicated green space

Proposed East - West line Proposed North - South line Current Java Railway

250

Fig F4. Bandung's current figure ground map Page 87 of 104

150

50

0m

MASTER PLANNING

1

2

3

4

5

6

7

8

9

10

Fig F5. Bandung's current figure ground map Page 88 of 104

MASTER PLANNING

Fig F6. Cikapundung River current conditions Page 89 of 104

MASTER PLANNING

Inside the Slum The majority of Bandung is covered by slum areas. A highly dense mixed use uncontrolled development deep inside the main roads. The access is usually narrow. The width would only fit a tricycle, motorcycle, or a cart. The speed of street inside is very slow. The streets are the social spaces for the residences for all ages, from children playground to chess tournaments. The circulation can be confusing for people who do not came from the area.

Communities are large but cultural values are highly upheld by the people. Living close to one another demands respectful and considerate attitudes. The functions inside are mixed in sizes and variety, ranging from a 50 sqm single storey houses to 2 sqm coffee shop.

Basic necessities can be found within a walkable distance.

Small mosques and shops can be easily found.

From the city perspective, these areas are considered to be the "most stagnant" in terms of economic growth.

Removal of slum areas are getting common, especially in the major cities in Indonesia. The drive is mainly economic, however, original kampung spirit should be maintained in the more efficient context.

Fig F7a. Bandung's slum areas. Photos taken in June 2018

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MASTER PLANNING

Fig F7b. Bandung's slum areas. Photos taken in June 2018

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MASTER PLANNING

BANDUNG 2118 Agents to Masterplanning There are 25 agents that cover the chosen site (5x5, 1.25 x 1.25 km). The first image shows the selected agents at time = 100, the numbers displayed are the total of people assigned in the area bounded by the agent.

The immediate translation of the agents are based on the areas bounded by the road network. The important landmarks are marked and the population are split accordingly based on the area covered. (Step b) The key concepts for the central station masterplanning are as follows. These values are aligned with the city's vision in making a liveable city; rich in its socio-economy aspects through nature and technology.

1. Center of public activitties 2. Unifiying urban functions 3. Bandung as city of technology 4. City for people

To fit with the concept, a green circulation around different function was introduced. This green routes will intersect and sprawling into the areas between the residential and commercial spaces connecting the people from these functions. The routes provide access to the central station from most of the residential and commercial spaces.

The building footprints are different based on the function of the land. For this research, two typical modern housing footprints are used to fit into the residential areas.

The height of the buildings are based on the number of people assigned to the areas, divided by the floor area and area per people (which will return a required floor number).

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MASTER PLANNING

Station agent Residential agent

Slum agent Commercial agent

Green agent Fixed type agent

Fig F8. From top left (Clockwise). a. Time 100 (St12) b. Block function and occupancy Allocation. c. Negotiation of functions to concept. d. Land use, FAR, and building footprint

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MASTER PLANNING

Proposed Development Station Interchange. The proposed form is an organic, open space, encapsulates multiple commercial spaces forming new concept of central public attraction. The structure opens up to green routes, becoming the center of the pedestrian infrastructure. There is a new bus interchange next to the station to extend the reach and improve efficiency.

Roads. The streets are envisioned to focus on pedestrian mobility, car-lite, and encouraging people to cycle. The cycling culture used to be famous in Bandung, due to its cool climate and relatively flat topography. Renewal use of the roads would be focussed on personal mobility device rather than personal cars. Additionally, an underpass that connect the Otista road is added into the plan to improve the connectivitiy.

Conserved spaces. Some slum areas are kept. The societal values in the slum areas are unique to Bandung. Activities such as ronda and hanging out at the parks are common and very pleasantly received by the community. Such features should be encouraged and kept in the future.

Fig F9. Existing social activities in Bandung Page 94 of 104

MASTER PLANNING

11

9

8 7

6

4 5

3

10

1

2

Legend East-West Line

North-South Line

Building footprint

Green areas

Cycling / nature route

Ciliwung river

1

Central Station

2

CBD

7

Riverside amphitheatre

3

Bus terminal

8

Vertical housings

4

City Hall

9

Padjajaran sports park

5 6

City Park

10

Otista underpass

Kampung village

11

Industrial park

100

Fig F10. Proposed road network with the functions in the new urban fabric

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300M

MASTER PLANNING

CAR PARK LANE NARROW SHARED PATH

1.2 m

7.5 m

7.5 m

3.6 m

Fig F11. Existing road condition

From traffic place to people place Invasion of cars as personal mobility had been the major problem with the traffic jams in Bandung. For a short 2 km trip would take a car 30 minutes through the traffic jam, when a bicycle would simply take 10 minutes. There is very little space for pedestrian and bicycle.

Often, hawker sellers will populate the pedestrian space making walking extremely difficult. The five foot walkway are used as part of the commercial space. The people who need to walk along the road are exposed to sunlight or rains.

There is also a dedicated lane for car parking, most of the time it is on both sides of the road. The function of Bandung's roads is primarily suited for personal / family cars. The economic status is displayed by the possession of personal cars. People from better financial status would not take public transport, but choose to take longer time to reach their working space.

The new proposal is people oriented. The goal is to make the occupants of the city healthy, happy and resourceefficient. It is to invert the space given to cars for people.

The new section of the road is primarily to open up most of the road spaces for pedestrian or usages of personal Page 96 of 104

1.2 m

2.2 m

MASTER PLANNING

PEOPLE SPACE DEDICATED BICYCLE AND PEDESTRIAN LANE

deliv eroon

2.2 m

2.2 m

4.5 m

s

4.5 m

2.2 m

5.4 m

Fig F12. Proposed road section

mobility device such as bicycles or segways. Separation between walking with cycling should be implemented for safety reasons. Streets were badly lit because the initial design were for cars, which already have lights on it. The proposed demands adequate street lighting.

The second priority is to provide spaces for the public transports. The design of the bus stops, intersectioncrossings, and tunnels are pedestrian friendly. It is focussed on the safety between walking and alighting the public busses. A gentle turn as displayed in figure 13 would slow down the cyclists as they enter the bus stop areas where people need to cross over the cycling lanes.

The main goal is to create a sustainable city, in 100 years.

With the simulation in phase 2, we achieve ideal land use within walkable distance. The infrastructure of the roads and green routes should promote healthy and happy living of the citizens.

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Fig F13. Pedestrian friendly designs

MASTER PLANNING

FLOOD BUFFER MARK

BIOTOPES

Fig F14. Blue-green approach to Cikapundung river

Blue-green infrastructure Blue-green infrastructure of Cikapundung river adds into the sustainable values in improving socio-economy condition of the city. This approach is to address urban water challenges, aiming to recreate a more nature-oriented water cycle in the urban environment by bringing together water management and green infrastructure. The rivers will have vital roles in activating the public and natural function in the city as 1.

To enhance urban liveability 2.

Water source for the adjacent greenies 3.

Improving the air quality 4.

Flexible design and reaching out to the urban functions

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MASTER PLANNING

GREEN ROOF SAUNG

Strategic addition of plants would help to cleanse and retains storm or waste water from the functions around the river. At the moment, the river has no interaction with the community. It serves merely to channel water from the mountain to the sea. The proposed river programme includes the built environment like public space that hos local community infrastructure and the social environmental recreations.

In many cases, the treeâ&#x20AC;&#x2122;s functions can be enhanced/ maintained without excessive use of potable water: for example, by irrigation with harvested roof and street rainfall runoff and recycled grey water from surrounding functions. This includes careful selection of the tree species based on their characteristics and the specific requirements they will need to fulfil.

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MASTER PLANNING

CONCLUSION The thesis tried to find a democratic solution between the bottom-up and the top-down method of TOD urban planning through simulation. The calculation is run by using historical and current data, and targetted (biased) to meet the goals set by the government.

Phase 1: Creating evaluation tool for TOD based development. The values reflected by the heat map from the calculation is a representation of work done intensity.

The efficiency of travelling to the nearest transit node reflects what means of transport the person would take.

For sustainable cities, an efficient urban form means more people travelling with bicycle / walking than using their own personal cars or public buses.

Phase 2. TOD based simulation. The aim of the simulation is to reduce the work done (with evaluation tool) by densification of people around the transit nodes within an expected rate. The simulation relies heavily on the accuracy of projected and existing data. The result can be used as feasibility studies of urban planning goal of a city, to see how long a city would need to reach the goals.

It also provides urban planner / city planning specific economic growth goal to reach the urban form within a desired duration.

Phase 3. Translating the agents to masterplanning.

Generation of building function and mass would give us some ideas on how the city line would look like. The convertion to master plan is particularly site specific, one has to consider cultural, identity, and heritage values of what defined the people. In case of Bandung, where the slum areas were aimed to be eradicared, these spaces are not exactly showing poor living standards. It is, in fact, a result of uncontrolled urban development from the people without interference from the government.

Within walkable distance anyone would find their basic needs. The slum has qualities which reflect the values that Bandung citizens have. The question was on how these values would still be retained despite the modern development of the city.

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MASTER PLANNING

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MASTER PLANNING

PHYSICAL MODEL

Fig F15. Aerial view of the master plan model

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MASTER PLANNING

Fig F16. MRT Interchange station

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References

Belden, Russonello, Stewart. (2004). American community survey: National survey on communities. Conducted for Smart Growth America and National Association of Realtors. Washington, DC: Smart Growth America and National Association of Realtors.

Cervero, R., Ferrell, C., and Murphy, S. (2002). Transit cooperative research program, research results digest number 52. Washington, DC: Transportation Research Board, National Research Council.

Newman, P. & Kenworthy, J. (2006). Urban design to reduce automobile dependence. Opolis, 2 (1), 35–52.

Renne, J.L. (2009). From transit-adjacent to transit-oriented development.

Journal of Local Government, 14(1), 1-15.

RPJMD kota bandung 2014 - 2018 - Perpustakaan BAPPENAS “World Economic Outlook” Finance & Development, March 2017, www. imf.org/external/datamapper/NGDP_RPCH@WEO/OEMDC/ADVEC/ WEOWORLD/IDN. “Indonesia.” UNESCO UIS, 12 Apr. 2017, uis.unesco.org/country/ID. Lawson, E. et al (2014). Delivering and evaluating the multiple flood risk benefits in Blue-Green Cities:an interdisciplinary approach. WIT Transactions on Ecology and the Environment 84, 114.

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THANK YOU