Driverless: more or less? (second edition)

Transform Transport

Driverless: more or less?

SECOND EDITION

"in order to change an existing imagined order, we must first believe in an alternative imagined order." — Yuval Noah Harari Sapiens: A Brief History of Humankind

Driverless: more or less?

Driverless: more or less?

Second Edition Made in Milan by Systematica Srl Š 2020 Systematica Srl All mobility studies presented in this book are developed by Systematica Srl. All rights reserved. Unauthorised use is prohibited.

No part of this publication may be reproduced in any form or by any means without the written permission of Systematica Srl. Systematica Srl Via Lovanio 8 20121 Milan, Italy +39 02 62 31 19 1 www.systematica.net milano@systematica.net ISBN: 978-88-944179-3-7 Cover: Courtesy of Voyage Graphic design: Parco Studio Printed in Milan in May 2020

Systematica Srl Transport Planning and Mobility Engineering

Via Lovanio, 8 20121 — Milan Italy

t +39 02 62 31 19 1 milano@systematica.net www.systematica.net

Table of Contents

Introduction p.4 ● today driverless is all about vehicles technology and design ● the vision in the 50s ● what has changed? ● driverlossary ● the Human perspective of the unmanned device ● there are no predefined scales of intervention ● is a hybrid future possible?

Urban Air Mobility p.18 ● technology ● safety ● infrastructure ● planning ● impact on cities

Demand Estimation p.22 ● fleet size dimensioning ● car occupancy Car occupancy policies ● parking demand Parking strategies and ownership model ● parking structures design

Parking structures of the future Changes in parking stalls and parking aisles Changes in vertical circulation ramps An imagined phased evolution of urban parking structures

High Density Vs Suburbs p.36 ● an attempt to predict early mobility patterns and use of driverless vehicles through observing uber movements

scenarios ● masdar: the first driverless network Internal mobility strategy ● citymobil2: oristano Testing a driverless transport system in torre grande

New Development Vs Retrofitting p.74 ● pilot driverless zone in riyadh Fleet estimation

Travel behavior

A dynamic mobility system

Travel distance

On-street parking and drop-off points

Land use What can we learn for better planning the driverless network? ● milan driverless perspective Current network structure Driverless network management From public to private More or less traffic? Different scenarios for Milan 2030 ● los angeles 2060

Flexible curbsides Adaptive street design ● dubai automated humanized From marasi drive to marasi driverless ● milan innovation district (mind) First deployment phase of driverless grt fleet

Looking Ahead p.92

The smart experience Planning principles

Private Vs Shared p.60 ● future-proofing airport car park provision Driverless: development

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To begin to understand how driverless vehicles will influence how we currently conduct traffic engineering and planning we researched changing trends, technologies in development, and performed traffic modelling exercises to envision how travel behaviours might change as driverless vehicles emerge. We documented this information in a book to narratively express our ideas on the subject. This book is a starting point that will evolve as more information is presented. As such, we focus on the possible impacts of the driverless revolution on cities and urban transportation, and investigate the extent in which the science of traffic engineering and transportation planning is struggling to keep up with the pace of changing cities in a technology driven and connected world.

Driverless vehicles will disrupt our current transportation ecosystem on multiple fronts. Cities will be challenged with infrastructure planning, ethical values, and developing safety standards.

The very principles for dimensioning public space, parking areas, roads and streets will be altered.

The adoption of driverless vehicles is expected 4

to occur alongside the revolution in information technology and that poses questions regarding the distinction between public and private transportation modes as we move into an era of sharing and mobility as a service (MaaS).

In addition, questions regarding the basic parameters of mobility engineering that have often steered our assumptions and conclusions, such as car ownership, car occupancy, vehicle trip generation, modal share, etc will need to be adapted to account for new travel behaviours.

Furthermore, this book seeks to develop measurement tools and units that make it possible to rethink the traffic engineering toolkit and adapt principles to the expected changes and challenges, while aiming to achieve inclusive, equitable, and sustainable cities.

The concept of “more or less� is an exploratory exercise, a modest attempt, to predict the different facets of this revolution, to read and analyze the transformation process while considering both its negative and positive sides.

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Today driverless is all about vehicles technology and design Driverless. Autonomous. Self-driving. Robot Cars.

All are different terms currently used to describe vehicles capable of sensing and understanding their environment and navigating with limited or no human input.

Today, most leading auto manufacturers and tech companies are working on driverless innovations, whit the major effort and focus is on the design of vehicles and the technological devices to improve their performance. Driverless vehicles are providing planners with a unique chance to rethink cities in regard to the automobile itself.

With this technology urban mobility will be revolutionized.

Bulky transport infrastructure such as parking facilities and depots, will be eliminated. The expected reduction of vehicles on the road will offer the possibility to humanize cities, prioritize people over cars, and dedicate more space to pedestrian activities and bicycling.

The vision in the 50s The iconic poster shown below was published in 1957 as an advertisement by America’s Electric Light & Power Company to show: “Highways will be made safe – by electricity” showing a family enjoying a board game in their self-driving car with accompanying text : “No traffic jam… No collisions... No driver fatigue” An ideal depiction that is still relatable to many drivers commuting daily on congested roads around the world.

Credits: Detailed version of the panorama—Magazine vol. 40

The very first vision for automated highways was revealed during the “1939 New York World's Fair” attended by over 44 million visitors with “The World of Tomorrow” as the main theme.

The installation “Futurama: Highways & Horizons” designed by Norman Bel Geddes and sponsored by General Motors Corporation received heightened attention from visitors. It included an automated highway system where cars travelled through trenchlike tracks: this was the first realization that highway driving lends itself to automation.

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What has changed? with and without backup drivers, these vehicles are gaining the trust of urban politicians around the globe to issue permits for test drives on their city streets and public roads, besides other road users. Their presence proves that mobility and mobility-related technologies could significantly influence the way people experience cities. This urges urban planners to rethink cities, public spaces, street design, etc. and urges transport planners to re-examine basic concepts of mobility such as daily commute, first and last-mile transport, delivery, and so on.

Credits: Rinspeed XchangE

During the past decade, a substantial amount of effort has been put towards improving the design of vehicles and their technological features.

Today the universal presence of Internet 4.0 links every person and object in a fabric pulsating with a unique pace. Tech companies have already started with the dynamic mapping of cities and seamless realtime data transfer makes it possible for vehicles to communicate with each other and with infrastructure to operate in real urban and inter-urban roads.

With kilometers worth of completed test-drives both

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Driverlossary Driverless vehicles and their related technologies introduce new vocabulary to the world of mobility and transportation. Below are some of the most frequently used terms.

5G

Automation

New mobile connections standard.

Driverless vehicles can take advantage of this technology for communication and information issues.

Systems and methods used to let a machine work automatically.

connection devices and automated driving systems.

Autopilot

Connected and Automated Transport (CAT)

Advanced Driver Assistance Systems (ADAS) ADAS systems monitor the environment surrounding the vehicle, taking corrective actions responding to the external conditions. These systems use inputs from multiple sensors, such as cameras and radars.

Artificial Intelligence (AI) Artificial intelligence refers to software technologies that make a robot or computer act and think like a human.

Autonomous A fully autonomous vehicle, is the one that can run without any human intervention. This means a vehicle without controls or steering wheels..

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Autopilot is a system that controls the course of an airplane or ship without constant control by human operator. Tesla used this word to name its ADAS suite.

Big Data Data sets whose size or type is beyond the ability of traditional relational databases, having one or more of the following characteristics – high volume, high velocity, or high variety.

Cloud Connected Device connected to the "cloud", which often refers to the Internet and more precisely to some datacenter full of servers that is connected to the Internet.

Connected and Automated Vehicle (CAV) Vehicles that integrate both

connected and automated transport technologies and systems for all transport modes.

Connected Vehicle Technologies Technologies thet allow vehicles to talk to each other and to the infrastructure around them.

Deep Learning When AI gets more "intelligent" by moving past simple rules-based logic.

Driverless It currently encompasses everything from a car without a steering wheel or pedals, to a vehicle with self-driving capability carrying only passengers, to no one in a vehicle at all.

Driver Monitoring System (DMS) System that monitors the driver’s conditions, aimed at invoking actions to maintain the driver attention in both manual and automatic driving conditions.

Gateways It is a piece of networking hardware used for telecommunications networks that allows data to flow from one discrete network to another. This is where a hacker can gets into a vehicles system

Geotonomous Car An automated vehicle capable of functioning without human input within a clearly defined geographic area.

Internet of Things (IoT) The IoT is the extension of Internet connectivity into physical devices and everyday objects.

Embedded with electronics, Internet connectivity, and other forms of hardware such as sensors, these devices can communicate and interact with others over the Internet, and they can be remotely monitored and controlled.

LIDAR (Light Detection and Ranging) It is a remote sensing method that uses light in the form of a pulsed laser to measure ranges (variable distances) to the Earth. These tools are used by autonomous vehicles to detect obstacles.

Mobility-as-a-Service (Maas) It describes a system the integrates mobility solutions, offering to

the users the possibility to use a “package� of different service, avoiding distinguishing operators and data.

Patch A remedy to a security risk that car manufacturers will issue as soon as they are aware of a vulnerability.

Vehicle's software should be updated as soon as the patch becomes available.

Platooning The coordinated operation of multiple self-driving vehicles in a convoy, meant to increase road capacity and efficiency.

Radar Radar is a system that uses radio waves to detect other objects. The technology will help vehicles to read the area around them.

SAE Levels of Automation Levels of Driving Automation standard, defined by SAE International that identifies the six levels of driving automation, from no automation to full automation.

Self-Driving This should only describe fully autonomous cars, as the phrase itself suggests. Although the term so diluted that it's essentially worthless.

Vehicle to Cloud (V2C) The technology exchanges information about and for applications of the vehicle with a cloud system. This allows the vehicle to use information from other, though the cloud

connected industries like energy, transportation and smart homes and make use of IoT.

Vehicle to Infrastructure (V2I) The technology captures data generated by the vehicle and provides information about the infrastructure to the driver. The V2I technology communicates information regarding safety, mobility or environment-related conditions.

Vehicle to Pedestrian (V2P) The technology senses information about its environment and communicates it to other vehicles, infrastructure and personal mobile devices. This enables the vehicle to communicate with pedestrians and is intended to improve safety and mobility on the road.

Vehicle to Vehicle (V2V) The technology communicates information about speed and position of surrounding vehicles through a wireless exchange of information. The goal is to avoid accidents, ease congestions and positive environmental impact.

Vehicle to Everything (V2X) The technology interconnects all types of vehicles and infrastructure systems with another. This connectivity includes cars, highways, ships, trains and airplanes.

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The Human Perspective of the Unmanned Device Driverless Vehicles can be considered an effective solution towards reducing the high number of traffic injuries, which according to the World Health Organization is the 8th leading cause of death.

Considering that most of roadway incidents occur as a result of human error driverless vehicles offer a great benefit to society. The fascinating technology will forever alter human life, similar to how the evolution and the spread of devices, such as smart phones changed people’s lifestyle.

The rapid technological progress of driverless vehicles and the rising scientific and social discussions around the topic urges the necessity to define the ethical requirements and laws for developers.

The communication Autonomous driving, vehicle sharing, and the electric vehicle ecosystems are based on communication, such as Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), and IT (Information Technology) platforms, building a third environment, in addition to the physical network and the mobile operating systems. By 2011, Cisco Systems Inc. research had already determined that the number of connected devices were exceeding the population.

The movie, "Six Degrees of Separation" (1993, Fred Schepisi) refers to the homonymous theory which, in semiotics and sociology, is defined by the hypothesis that each person can be connected to any other person through a chain of knowledge and relationships with no more than five intermediaries. The affinity of sociological theory is found in the importance of communication (nexus, network theory), but the reference to number six also catches our attention. If in sociological theory six connected people can diffuse knowledge to all other people in the world, today this same value (number six) represents the average number of connected devices per capita.

Switching from six degrees of human separation, as in the movie, to six interconnected devices per capita means that communication between devices becomes more frequent than communication between human 12

beings. Luciano Floridi, Professor of Philosophy and Ethics of Information and the Director of the Digital Ethics Lab at the University of Oxford, believes if an E.T. (Extra-Terrestrial) attempted to study our planet and our human species on earth, they would find a great deal of communication between ICT (Information and Communication Technologies) systems, while the "human" communicative language would appear to be statistically marginal and irrelevant.

A further contribution to the quantitative and evolutionary processes of communication comes from Gregory Bateson, late English anthropologist, social scientist, linguist, visual anthropologist, semiotician, and cyberneticist, the author of Steps to an Ecology of Mind (1972), with his interpretation of the management of modified environmental conditions in homeostatic systems.

Bateson portrays a man who decides to move to a high altitude. Rising the summit, his body reacts by shifting ‘forward’, stressing the human life parameters including acceleration of the heartbeat, shortness of breath, etc.

The rarefied air acts as an error signal and induces the body’s survival mechanisms through several derivative homeostatic circuits of the organism, pushing all subsystems to operate outside the known equilibrium threshold. The body’s whole communication circuit is

engaged by this temporary adaptation, which in turn overloads the communication system, as in a phenotypic adaptation rather than genotypic adaptation.

When a status change is persistent, it becomes advantageous for the organism to transfer the modifications to a genotypic, and therefore irreversible, level. The transition from reversible to irreversible takes time due to the complexity of the subsystem variables in accordance with an evolutionary survival strategy, which adapts only to a structural pressure and not a temporary one. Once the change has been fixed at the genotypic level, the adaptation potential (overload capacity) of the communication system is recovered. Communication between homeostatic systems and slowness are therefore two pillars of evolutionary capacity.

The rules: human safety The IT system communicates exclusively through logical and consequential processes, unlike the human approach. If the transition to driverless vehicles is encouraged for its benefits such as a decrease of fatalities caused by human error, the system implemented for driverless vehicles should act as a third impartial judge concerning the choices among stakeholders.

In addition to promoting possible benefits of these technologies, it is essential to consider the conflicts of interests and undesirable situations such as accidents and fatalities to investigate possible ethical solutions.

Guglielmo Tamburrini, Professor of Philosophy of Science and Technology at the University of Naples Federico II, considers some well-established examples in everyday life in order to highlight the necessity for defining ethical considerations. The exasperation of the "trolley dilemma" can be used to explain the potential phenomena of self-educational consequentialism and related machine learning. The hypothetical situation considers the possibility of an accident between a car and two different motorcyclists: the first wears a protective helmet the second does not. The car cannot avoid both of the motorcycles and must choose which of the two to hit. The rule of pursuing the maximum benefit or maximizing the likelihood of benefit leads to sacrifice the motorcyclist with a helmet, considering the possibility of a higher survival rate. However, if the car had a deep learning system, able to learn from past experiences, the rule of self-educational consequentialism, the AI (Artificial Intelligence) would learn to preserve the weakest - even if this disrespects laws. Continued self-evolution built on similar examples would, in the long run, lead to discouraging the use of the protective helmet, given that driverless vehicles would sacrifice the motorcyclist with the helmet when presented with both scenarios.

Finally, this will promote the wrong behaviour instead of encouraging a universally accepted safety goal of using protective helmets.

Filippo Santoni de Sio, Professor in Ethics of Technology at TU Delft, is appointed by the Dutch Ministry of Transport to develop guidelines on “Ethics and SelfDriving Cars" a white paper on "Responsible Innovation in Automated Driving Systems (2017)”, approaches the discussion through the lens of applied philosophy. The document addresses the need to redefine and update concepts such as liability, rights, wrongdoing, and fraud, which have different definitions based on the relevant legal codes of various nations.

The trans-diegetic effect: active and passive roles Some of the vast and complex research of Luciano Floridi is published in “Onlife Manifesto”, freely available in Copyleft from the Springer website. He uses the concept of trans-diegetic communication and relative enveloping of human aspects by IT. Floridi’s example on trans-diegetic describes the permeability threshold that separates the human from the digital in regards to communication.

These trans-diegetic communication strategies are increasingly implemented in video and role-playing games, coherent with an evolution of virtual reality that seeks to be blended and confused with physical reality and materiality. The objective relies on the ability to shape and wrap - "enveloping" - perception and reality.

The interaction between man and machine, or rather the mutually crossed border, leads to a symbiotic exchange like no other.

The development of virtual reality, even in games, through "wearable" devices, makes the separation between reality and fiction even less clear. Within this framework it is essential that designers and urban planners; those involved in land-use management, appointed from the academic and professional world, take action to shape the environment to improve human life.

Previous examples of how society and its physical footprint is changed with the advent of the car can be seen in parking lots of shopping centers. The Walmart retail chain, founded by Sam Walton in 1962, was the first retail chain in the world to take advantage of the importance of the car, to which it owes its success. The role of planning territorial transformations historically aimed at improving people’s quality of life. Today a further factor seems to infringe on the relationship between man and environment: technology of the human instrument acquires its autonomy and identity, and therefore, dictates needs and requirements itself.

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There are no predefined scales of intervention

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There are no predefined scales of intervention and analytical limits; access to any given building is influenced by the deployment of driverless vehicles.

While there is the promise for benefits such as improved safety by fewer collisions, reduced traffic congestion, less air pollution, social inclusion and cheaper urban transportation, there are challenges such as legislation, ethical concerns, cyber security, technological issues

and most importantly restructuring urban fabric by altering and redeveloping street designs and public spaces for driverless vehicles access.

Considering the diversity of urban fabrics, transportation availabilities, travel behaviour, and modal choices around the globe, there will be a wide range of systems for driverless vehicles.

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Is a hybrid future possible?

No significant changes will occur if the current mobility paradigm is not re-imagined.

During the past decade, driverless vehicles have emerged as a reality from sci-fi fantasy; providing a unique opportunity for planners, designers, and decision-makers to revolutionize urban mobility through strategic mobility and infrastructure plans that can provide effective, efficient, affordable, and sustainable urban transport for over half of the world’s population.

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Urban Air Mobility Cities are becoming increasingly more urbanized as land is being developed, repurposed, and rising in density.

Large concentrations of jobs and economic prosperity is leading to high growth in many cities around the world, however, this growth is putting a strain on transportation systems. Roads are becoming increasingly more congested and transit systems are under strain with aging infrastructure. The transportation sector is struggling to meet the growing demand, while simultaneously reducing congestion and improving safety. The way people travel in cities is going to change and it’s largely being driven by advancements in technology. One of which includes new advances in aircraft technology and electric propulsion systems, which are leading to the development of flying vehicles capable of operating in urban air space.

Air travel is poised to revolutionize our transportation system in what’s being called “advanced aerial mobility” of which urban air mobility (UAM) is a subset. Advanced aerial mobility refers to the broader range of opportunities in passenger transport, cargo logistics, and deliveries in both urban and rural contexts. The term UAM is used to describe vertical take-off and landing (VTOL) and electric

vertical take-off and landing (eVTOL) vehicles that will transport people and cargo in a safe and efficient method in a new dimension of the urban transportation network.

VTOL and eVTOLs are small aircraft, similar to helicopters, in their vertical take-off and landing. They are currently designed to carry a small number of passengers, between 2 and 5, while other hybrid designs estimate 10 to 15 passengers. While in its infancy today, it’s estimated by 2030, as many as 500 million flights a year for package delivery, and 750 million flights per year for metro service.

(Source: NASA) The integration of UAM into our transportation system will involve communication and collaboration with the public and private industries. The main factors for UAM success are the vehicle technology, the infrastructure to support the landing and take-off of the aircraft, and the business model for operations and passenger demand. Among these factors are several considerations that will need to be addressed. These include, the appropriate number of passengers, optimal range of the vehicles, the market, the aircraft type and design, as well as flight operations.

eVLOT Aircraft by lilium Source: https://lilium.com/

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Technology The technology accelerating the advancement of eVTOL and VTOL is the electric propulsion system. As opposed to helicopters combustion engines, which have multiple parts and a fuel system, an electric system means one moving part, the bearing. The distributed electric propulsion (DEP) system turns smaller rotors for either a tiltwing thrust, lift and cruise, or multirotor operation.

Having multiple engines means they can operate at different speeds allowing for better performance and maximum control in varied weather conditions. The DEP system allows flexibility in different vehicle design, thus allowing for an aerodynamically optimized design, and compared to a helicopter, an eVTOL produces a lower noise level and is more reliable.

As with Lilium, a manufacturer of eVTOL in Germany, the duct design captures and dissipates noise before it leaves the engine. Noise levels are an important consideration in the operation of eVTOL air taxis in urban air space.

In addition to advancements in electric propulsion, technology advancements in batteries are enabling the lightweight and efficient design of the vehicles. The batteries allow an efficient discharge rate which allow the eVTOL to travel distances of up to 300 miles. The production of these vehicles is relatively lower in cost compared to the automotive industry because of the advancements in batteries, the propulsion system, and material used in the design of the vehicles. As battery technology improves, eVTOL vehicles will be able to fly further and operate longer in the air.

Despite the technological progress in the manufacturing of the vehicles, more advanced technology is needed in the avoid and detect capabilities, as well as in autonomous navigation. The regulation aspect to bring this technology to market will need to be rigorous.

Allocation of airspace with increasing number of vehicles will be an immediate challenge to address.

Safety The success of urban air taxis will largely depend on the safety of the vehicles and people’s acceptance of these vehicles in urban air space.

All vehicles will undergo a rigorous aircraft approval certification process to operate in urban air space, but in addition to certification, manufacturers are incorporating testing, such as, Failure Mode, Effects, and Criticality Analysis (FMECA) as an analytical method for testing the safety of the vehicles.

This test is a step-by step approach for testing

products manufacturing to identify design or manufacturing flaws and to carry out the corrective measures to reduce critical failures.

EVTOL and VTOL are new vehicle concepts without proven in-flight experience, therefore rigorous testing to acquire data and research concepts that improve propulsion reliability are needed. The first flight operations will likely exist outside of urban areas because of regulations, and for safety reasons.

Example of vetriport Source: https://lilium.com/

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Infrastructure Heliports, vertiports, and other ground infrastructure will be needed for the loading and unloading of passengers and the charging or fueling of the vehicles.

The vertiports will most likely exist in areas convenient to other modes of travel, but all in new places that haven’t existed before. This new infrastructure will be costly and complex involving many entities for the planning and coordination of the facilities to ensure passenger safety, as well as the general public’s safety.

The landing sites will need to exist in areas to attract the most passengers to the sites, while at the same time, located in air corridors supportive of urban air taxis. Contributing to the planning of the UAM network will require an understanding of the urban context and

include socioeconomic considerations in route citing, eye pollution, and air traffic operating in dense urban areas.

Most of the current vertiports in operation today can accommodate 2 to 4 helicopters, but the need for a greater amount of air taxis landing and taking off from a vertiport will exist. In the United States, the Federal Aviation Administration (FAA) is currently soliciting industry to create standards for vertiport design. Existing vertiports are the likely infrastructure to transition to pads for urban air taxis and many cities around the world have high numbers of vertiports for helicopter landing.

Planning Efficient planning of a UAM network will involve the consideration of a seamless transition between air taxis and other modes of transportation in operation within a city. There are many start-ups as well as vehicle manufactures building air taxis. How a city develops the network with one or more operators within a city will be crucial to the success of the UAM system. The planning for the take-off and landing vertiports will need Low

High

Isochronal analysis by train & walking (1 hour, 300 Km radius)

to consider the major origins and destinations that will attract passengers that consider UAM convenient to their travel. In addition, vertiports to other origin and destinations for purposes such as medical and cargo transport. Therefore, having a command-and-control center specifically for UAM will be significant in helping prevent accidents and manage air taxis congestion at ports, as well as integrate various functions of the city.

Low

High

Isochronal analysis by drone & walking (1 hour, 300 Km radius)

Created by Bakunetsu Kaito from the Noun Project

Created by Adrien Coquet from the Noun Project

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Impact on Cities The impact of UAM on cities will vary with the adoption of the technology, as well as, the policy and regulations for the service to operate safely. To date, aviation has been constrained to airports, with the exception of helicopters operating as medical transport, and the few heliports for private operations. With UAM, vehicles will operate beyond airports and in cities with more obstacles and elements to navigate. Vertiports located in urban areas will likely encounter NIMBY (Not in my backyard) occurrences which could possibly limit preferred routes.

While urban air mobility might be an efficient way of

travel, it will be unlikely to reduce a significant number of trips from the transportation system. In fact, it may be that trips will increase because of the convenience of trip making modes and apps. Whereas before a person might conduct trip chaining, that may very well change, this of course will largely be dependent on costs.

There are a number of roadblocks that will need to be addressed as this technology develops. The challenge for cities and communities will be in evaluating how this technology appropriately fits within their community.

Transit origin-destination pairs

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Fleet size dimensioning

Different factors will influence the principles and outcomes of travel demand forecasting One of the most important challenges for designers and planners is to forecast the mobility needs of a project. Typically, forecasting methods rely on historical trends and travel behaviours. However, the driverless revolution will profoundly impact the current transportation ecosystem, both in terms of behavioural changes, as well as, infrastructure changes. As a result, forecasting tools will have to be updated to account for changing travel behaviours, and trips that could include zero occupants.

Opportunities in the estimation and development of the driverless fleet and its management will create a new market for competition. Costs and efficiency of the

system will depend on properly incorporating this new technology into an existing transportation ecosystem that’s been designed and built for private vehicles, and as such, have crafted our planning and forecasting methods.

In a complex ecosystem, it is beneficial to determine some of the variables that will most represent the size of the fleet. These 6 variables are not in a consistent equilibrium and change over time, but they influence each other. Based on the understanding of their mutual impact, we created a scenario with a plausible outcome to properly estimate the fleet size for any given development.

Resident and User Population

Modal Share

Car Occupancy

The expected resident and user population of any given development is the starting point for determining the number of produced trips. A clear understanding of demographic aspects requires a thorough analysis of demographic trends, changing habits, household structures, etc.

Modal share is another key factor for the estimation of a driverless fleet and a determining component that governs the overall mobility dynamics. Modal share is both an outcome of policies and actions and an objective that each city predefines to itself. It is essential to provide options for both private and shared public vehicles in order to reach a balanced modal share and avoid overdependency on private cars, among which driverless cars may be.

Higher car occupancies lead automatically to less vehicles on roads. It is necessary to implement policies encouraging higher occupancies in order to reduce congestion. A future without regulation through policy could lead to the travel of zero occupant driverless cars thereby increasing traffic loads, challenging traffic management schemes, increasing pollution, and leading to inefficient energy consumption.

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x2 x3 Car Ownership

Internal capture

Human Factor

The same car can be used multiple times in an hour, depending mainly on the ownership model.

Challenging car ownership and encouraging private owners to share their cars is a crucial behavioural shift that will certainly affect the number of cars on the network and in their garages or driveways.

Internal capture is the estimated number of trips of a given site or mixed-use development based on complimentary land uses that support the range of trip purposes.

These trips can be estimated to utilize infrastructure that supports shorter trips such as walking, biking, or micro transit. The estimation of this variable will be critical in determining fleet size internal capture in developments may range from zero (e.g. single land use developments) to 20% and above (e.g. mixed-use developments).

Even in a controlled and connected system, the unpredictable human factor contributes to the efficiencies and inefficiencies of the entire system, such as delays caused by users boarding and disembarking times, users’ preferences on board that will be able to set some driving conditions (speed, acceleration/ deceleration, etc.).

Introducing this concept early on will allow researchers to spend time measuring, analysing and quantifying these factors.

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Car occupancy Car occupancy is influenced by multiple factors.

Not only convenience and practicality, but also social and behavioural factors are determinants of car occupancy and our willingness to car-share.

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By encouraging passengers to share their vehicles, and combining trips with nearby destinations in the same vehicle the number of cars on the streets has the potential to drastically decrease. Public agencies are encouraging vehicle sharing by putting different policies into action, such as introducing and regulating micro mobility sharing schemes, providing advantages for High Occupancy Vehicle (HOV) lane users, etc.

Despite all efforts, we believe that many will avoid sharing a car as it goes against the common human perception of the vehicle as a ‘private space’.

Therefore, it is essential to provide innovative and diverse policies and incentives along with influencing campaigns to make car sharing an attractive option.

The following are some suggestions to encourage sharing: • Enforcing more strict conditions to High Occupancy Vehicles (HOV) lanes. A fast-track lane reserved for high occupancy vehicles is a necessary step given the advantages gained in terms of time saving. However, this policy alone is not enough to outweigh the disadvantages

of relinquishing the pleasure of a self-owned private environment. Additional incentives are required, although many believe that if traffic conditions do not improve with advances in technologies the advantages HOV lanes offer will increase, and will, therefore, transform into substantial benefits.

• Implementation of a dynamic pricing system instead of a fixed price system based on the number of passengers per vehicle. With static pricing strategies, car occupancy will not be significantly affected and would remain constant through the different hours of the day (1.1-1.2 pax/veh on average for commuters).

While dynamic pricing can affect the sharing ratio in a substantial way, it is essential to have a comprehensive understanding of the daily travel demand profile in order to achieve tailored pricing mechanisms and incentives that vary according to person travel demand. This policy will ultimately help to reduce the surge in vehicles on the network during peak hours, therefore maintaining a relatively constant fleet of vehicles at all times.

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Car occupancy policies Car occupancy and fleet dimensioning In congested urban areas, having a higher car occupancy rate is beneficial in reducing the number of vehicles on the network. A driverless future should be focused on the concept of sharing to promote higher occupancy rates. In order to provide an efficient and high-quality service, MaaS companies are exploring ways to forecast the variations of car occupancy during different times of the day as they begin to implement their car pooling programs. Accurate fleet dimensioning is essential to reduce the costs and provide a more accessible service for customers.

Hourly fleet dimension curve By considering varying car occupancy (CO) values and fleet size, changes in vehicle occupancy are significant. As shown in the figure below, if CO reaches 3.5 persons per vehicle during the demand peak, only 29% of the current vehicles are necessary to match the demand.

A different method to determine and increase the car occupancy rate is through the implementation of a dynamic pricing policy. Having a flexible pricing structure would allow for matching supply to demand. During peak hours, as travel demand surges, higher costs will encourage pooling of more passengers into a single vehicle. This allows fluctuation in person travel demand to be managed by the available seats in the driverless vehicles and not by an increase in fleet size.

Price variations and carpooling should be managed by the service provider and operator in order to provide a continuous service.

Trips 1000.00

100%

833.33

285.71

29%

07:00

08:00

09:00

Trips

10:00

11:00

12:00

13:00

14:00

Car Occupancy 1.2

15:00

16:00

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18:00

Car Occupancy 3.5

19:00

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Peak

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Max CO 3.5

Car occupancy index It’s possible to estimate the car occupancy rate by considering a scenario in which the large majority of trips are shared.

By adopting a very optimistic car occupancy rate of 3.5 pax/vehicle during the peak hour, the required vehicles would represent 29% of the vehicles needed in the single passenger scenario. Keeping the number of vehicles dispatched on the road network fixed, the car occupancy rate for the rest of the day varies Hourly car occupancy values The curve was further corrected in order to avoid car occupancy less than 1 during night hours as this implies vehicles are circulating with zero occupants. This might result in longer travel times during those hours, however, is considered to be essential for reducing the fleet dimension and avoiding inefficiencies.

between 0.71 and 3.5 pax/vehicle.

A sensitivity analysis was conducted in which car occupancy was reduced to 2.5 on average during the day. This shows that car occupancy reaches a minimum of 1.9 pax/vehicle, which is still higher than the current average. However, it is considered more realistic, and it is expected to reduce operational pressure on the connected driverless vehicles information system in assigning vehicles to trips.

CO 3.5

3.5

3.35

3.34 3.07

2.97

2.5

2.41

2.41

2.42

2.27

2.22

1.94

1.91

1.82

1.66

1.59

1.2

1.00 0.71

07:00

26

08:00

09:00

10:00

11:00

12:00

13:00

14:00

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

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CO’ CO

Car occupancy and parking demand By increasing car occupancy through a dynamic pricing system, it will be possible to accommodate different travel demand levels through a fixed and reasonably dimensioned fleet. As car usage is maximized, cars will be in motion during most of the hours of the day.

As a result, the need for parking space will decrease while the need for drop-off and pick-up areas will increase. This will be achieved only if the fleet size is dimensioned appropriately and if travel demand and supply are matched through an efficient traffic management system.

Correlation between car occupancy variance and parking demand With a high CO variance, the parking demand is very low since the fleet is always moving.

The parking demand is directly related to the car occupancy rate: broader range of flexibility during the day would virtually eliminate parking needs. However, a stricter range would require different fleets designed to accommodate the demand during peak hours.

The space saved from the reduced parking demand can be utilized to enrich and diversify land use, providing an opportunity to acquire higher revenues from the development and even reduce travel demand by providing alternative travel services.

Parking demand

$$$ $$ $

Car occupancy variance

Car occupancy and travel demand Currently the trends of car occupancy are mostly influenced by the quality of alternative transit modes and user behaviour rather than travel demand itself.

However, the number of trips linked to car occupancy at a low rate usually translates into a rise in the number of trips, generating traffic congestion and increasing the need for new infrastructure to match demand.

Correlation between car occupancy and number of trips With a low CO the number of trips becomes unsustainable due to the rise of traffic.

The ideal scenario, to be reflected through appropriate policies, is to achieve the highest car occupancy while reducing the number of vehicles on the network.

This will be a crucial objective for minimizing the space occupied by road infrastructure and freeing up cities for other functions.

A high car occupancy with low trip demand results in increased waiting time

Car occupancy

A high trip demand can’t be managed with a low CO

1

Person trips

27

Parking demand Parking demand estimation is one of the most crucial aspects of planning, especially in new developments. However, dedicating land to parking areas and structures significantly alters the land use and can impact the project’s economic feasibility.

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Parking facilities of the future will be more efficient as vehicles will autonomously find the best arrangement. In addition, parking facilities will be more efficient and do not need dedicated space for pedestrians to circulate and access the vehicles inside parking areas. The ratio between the number of parking places and the gross floor area will drastically change, gaining up to around 25% increase in space efficiency as it is shown in the next chapter, Parking Structures of the Future.

From a strategic point of view, the forecasting of mobility demand allows for allocating and managing different portions of the driverless fleet during the day. A strategic choice would be the relocation of parking structures to remote areas, freeing

high value development area, and in some cases, consuming less of the allowable built-up area.

The ownership model of driverless vehicles is an important factor that affects the parking demand and the sizing of parking structures. In a scenario where the fleet is managed by a central system, the size of remote parking areas is expected to significantly decrease. Said parking areas will be used not only for the development dedicated fleet but also will be put at the disposal of any ride-hailing company offering services such as peer-to-peer ridesharing, ride service hailing, food delivery, etc. This will transform the parking structure into a service rather than a stagnant reserved property.

Credits: Industria Italia

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Parking strategies and ownership models Possible ownership models The utilization patterns of driverless vehicles and in a broader sense, mobility trends, will largely depend on the ownership model of driverless vehicles. A city scale parking strategy will depend on how vehicles are owned and used.

Consider, in a business-as-usual scenario, in which driverless cars are owned privately, parking demand and provision can hardly be challenged. There will still be a need for both on-street parking spaces and parking structures located within the city or development.

In a car-sharing scenario, vehicles are owned and managed by a mobility service provider and operating companies. In this scenario strategies will be substantially different. The necessity for on-street parking will be significantly reduced and replaced by pick-up and drop-off areas. Curb side management will increase as vehicles will need pick-up and drop-off locations that do not impede the travel lanes. Parking structures programmed for MaaS will be necessary for fleets to park and wait, rather than circle the

Required parking lots Assumptions based on progressive increase in the use of Driverless vehicles: 2020 scenario without driverless vehicles; 2025 driverless vehicles for residential movements; 2030 for residential and visitors; and 2040 full implementation (residents, visitors and employees).

network, for the large amounts of trips that will utilize the network.

Remote parking structures could host another less used portion of the fleet. In this scenario, the necessity for MaaS parking structures depends on the dimension of the fleet and the daily demand. If demand is constant throughout the day, peaks excluded, the MaaS parking structures can be smaller in size with a lower impact on the land use.

A transition phase scenario, an example between the two extremes is probably the most likely scenario to occur and be considered. With the spread of MaaS providing companies and an inevitable optimization of public transport in parallel, the use of privately-owned cars could decrease. Parking will follow the same logic, a combination of both on-street parking and off-street parking structures will be necessary and will coexist with the driverless drop-off areas. Case-specific policies and strategies will determine the ratio between the two with special attention to user segmentation and land use.

30000

# 25000

24448 22329 18792

20000

15000 11669

15231

10079

10000

Driverless vehicles 7098

5000

7124

0

0

0

2020 2020

2025 2025 Stalls

30

Required parking lots

2030 2030 CAVS

Required Parking Lots

2040 2040

Stalls

A revised parking distribution strategy This section is not aimed at developing a new method for estimating parking demand, however, it’s aimed to develop a parking strategy based on the average daily movement profile. This profile typically consists of two major peaks during the morning and evening, a minor peak during lunchtime and off-peak hours spread through daytime and night-time intervals.

Assuming the fleet will be sized according to travel needs generated during peak hours, the size of parking areas and structures will be based on off-peak hours during which a large portion of the fleet is not being used, i.e. night time. Although this will not change in a driverless scenario, the driverless revolution will open up the possibility to allow for a distinct distribution of parking areas, opening up the possibility to locate part of the parking stock away from the final destination of users – typically located in underground basement parking areas – in remote parking structures

Parking daily segmentation During the day, different portions of the fleet are active or parked. During the night the majority of the vehicles are in the remote parking structures.

where vehicles self-park during night time. Although this will generate two additional trips per vehicle for traveling to the parking area and back to the final destination, however, the expected benefits on space utilization inside the development premises and the cost reductions are enormous.

As argued previously, the remote parking and on-site parking will provide two different response times: low response times for remote parking areas for matching predictable demand surges during peak hours and high response times for on-site parking areas for swiftly matching unpredictable demand surges that occur during daytime off-peak hours. This distinction will allow us to free up valuable real-estate property and reduce the area occupied by parked cars inside the development premises. Once remote parking areas start to be shared with other developments, an extra benefit will be cropped as the use of the parking areas and structures will be further optimized.

100%

remote parking on site parking

75%

50%

moving fleet 25%

07:00

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A revised parking distribution strategy The dynamic fleet management strategy will be an optimal way to manage the driverless fleet offering low response times only if is supported by a robust parking distribution strategy. To develop this system, a centralized fleet managing infrastructure and operating system is required as it provides a fully connected, more consistent, and highly efficient service.

A real time demand analysis and algorithms on effective trip planning could help to keep the number of travelling vehicles as low as possible. During the transition phase from a hybrid fleet of privately owned person-driven vehicles and driverless vehicles, parking structures should be designed in a flexible manner in order to temporarily accommodate both systems and gradually undergo a transition to the full driverless scenario.

31

Parking structures design Parking structures of the future The implementation of driverless vehicles will play a key role in altering urban mobility and land use structures, as car ownership and parking demand will significantly decrease in cities and mega-urban developments.

Driverless vehicles will have a series of implications not only on our streets, public space and transport systems but also on parking structures and areas.

Driverless vehicles could transform parking structures and areas as the number of vehicles that can be stored in a single structure will significantly increase. The efficient use of space could lead to smaller parking structures and areas and to free up urban space. Existing buildings can be transformed over time into different uses, therefore designing future-proofed buildings becomes essential, as parking garages may be easily converted into a residential, retail or office buildings if appropriate design, such as ceiling heights, horizontal non-inclined floor slabs, column grid, light quality, etc. is put in place in advance.

We need to design today’s parking structures to accommodate efficiently both traditional human-driven and driverless vehicles hence making the transition as simple and less costly as possible. Driverless vehicles will most likely fill our parking garages soon, however before this can happen, we will pass through a transition phase during which both vehicle types will need to co-exist.

Almost any conventional parking structure can be used by driverless vehicles, however a well-designed structure which takes into consideration this possible change will prove to be the most cost effective and efficient solution.

The design considerations to be taken into account are numerous, including architecture, structures, mechanical and electric systems, etc. In this article, we will focus on two main architecture design features, the columns grid and vertical circulation, and imagine a phased evolution of parking structures in cities.

Stalls Aisles Space

-

+ Changes in parking stalls and parking aisles Driverless vehicles will require different parking stalls and aisle geometries and therefore will achieve a better overall parking efficiency. Parking capacity will increase as the dimension of stalls will decrease due to the fact that driverless vehicles can be stacked closer to each other given that passengers will most likely be dropped

32

off in designated drop-off areas outside the parking area.

Moreover, the highly precise vehicle maneuvers of driverless vehicles will make it possible to narrow parking circulation aisles to their minimum.

This is expected to increase efficiency by at around 25%.

Changes in parking grid Column grids are typically sized according to the stall size and circulation aisle width. Most of the parking structures have a clear distance between columns of around 7.5m (2.5m per vehicle), where 3 human-driven vehicles may fit.

In addition, circulation aisles typically measure around 5.5-6.5 meters in width. The grid we are proposing is designed to increase the efficiency of parking structures when used by driverless vehicles. The downside is that the proposed grid might prove to be less efficient for parking human-driven vehicles, in the short term, however will prove to be very efficient, in the long run, once the parking structure is fully used by driverless vehicles. The proposed grid is conceived to work for both the highest number of parked driverless vehicles and the highest number of parked human-driven vehicles. The ideal grid for parking 4 driverless vehicles and 3 human-driven cars is a grid that maintains a clear distance between columns of 8.4 m – a grid of roughly 8.8m. While the required surface area for a single car parking place in a parking structure is around 35 to 40 sqm, the proposed grid is expected to double the efficiency, bringing the ratio of surface area per vehicle down to around 20 sqm once it is fully utilized by driverless vehicles.

Rectangular Grid

Square Grid

Today Vehicles

Driverless Vehicles

Rectangular Grid of 15 by 8.8 meters Aisles One way

Stalls 3 Between Columns

Stalls 4 Between Columns

Space Efficiency +160 %

Aisles One way

Stalls Stacking

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Changes in vertical circulation ramps There are different typologies of vertical circulation ramps in parking structures. When designing for driverless vehicle parking structures the selection of the ramp type turns are crucial for ensuring a future-proofed parking design. After analyzing different options, positioning the ramp outside the structure proves to be more efficient in both space and time. Locating the ramp outside the building allows also for an easier reuse of the structure. As the parking structure turns into a fully driverless vehicle garage, the need for space will decrease significantly, as argued previously, therefore the ramp should only connect to parking floors

at the middle levels, as the lower and upper floors will be used for different purposes. This will make it possible to demolish the ramp at the upper levels. In case the entire parking structure is to be transformed into a different use the ramp can entirely be demolished without any particular impact on the building structure or can be used for pedestrian circulation, thus its slope should be adequate.

For this reason, it is recommended to avoid locating the main vertical circulation ramps inside the building as this will make building reuse more complex and will eventually save a significant amount of money.

Step #1: Traditional parking structure

Step #2: One parking floor dedicated to driverless vehicles/ two parking floors freed up for other functions

Step #3: Two parking floors dedicated to driverless vehicles/ three parking floors freed up for other functions

Step #4: Entire parking structure retrofitted for other functions

34

• Designing more efficient parking structures that are capable of accommodating more vehicles in each floor • Stocking driverless vehicles in remote parking structures and areas during off-peak hours • Reducing the number of vehicles as car ownership decreases Parking structures can be changed into affordable residential buildings, bearing in mind the global increasing demand for housing and the immense advantage of densifying the city center for preventing urban sprawl. Densifying and diversifying the land uses in the urban cores provide people with better access to housing and other day-to-day needs and services.

The transformation of parking structures would occur in three main phases: short, medium, and long term.

In the short term, both driverless and human-driven vehicles will circulate in urban areas therefore some floors of parking structures will be dedicated to driverless vehicles accommodating higher number of vehicles due to their higher efficiency.

In the medium term, and as the number of driverless vehicles increases and the number of privately owned vehicles decreases, some floors will be reused for other functions.

Second Phase 2025 - 2035

Urban space is expected to be gradually freed from parking structures as a result of:

First Phase 2019 - 2025

An imagined phased evolution of urban parking structures

Third Phase 2035

In the long term, in a fully driverless vehicle scenario, most of the parking structures will be retrofitted to accommodate other functions and only a few will be used as short-term parking inside the city center.

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Chapter 3

HIGH DENSITY The driverless revolution will be different in high-density cities and in low-density areas or suburbs.

The experts’ opinion on the matter is divided into two main arguments.

The first argument states that by freeing many of the spaces dedicated to cars from city centers, the cities will have the possibility to provide more affordable housing in the centers and therefore prevent urban sprawl. Densifying and diversifying city neighborhoods with different functions will consequently reduce the mobility needs since people will have access to services and amenities in their neighborhoods.

36

vs

SUBURBS

The second argument is that by providing faster and more convenient travel options, people will move even further from the city center. Since the commute time from home to place of work can be spent as they wish, they might prefer living in the suburbs so they have the opportunity to purchase bigger homes, perhaps with larger gardens in comparison to the ones in the city center. Therefore, driverless vehicles might cause an even greater expansion of suburbs in more remote areas.

37

An attempt to predict early mobility patterns and use of driverless vehicles through observing UBER movements

38

PREDICTING THE PATTERNS OF MOVEMENT FOR DRIVERLESS VEHICLES AS A FLEET AND MOBILITY AS A SERVICE WITH ITS OWN CHARACTERISTICS, CAPACITY, USAGE, PATRONAGE, ETC. IMPOSES THE NEED TO REFERENCE THIS BEHAVIOR TO A SIMILAR CURRENT OFFERING. In order to carry out this exercise, we firmly believe that there is nothing more similar than UBER, or any similar ride hailing service, to the mobility dynamics that the spread of driverless vehicles will result into in terms of travel demand patterns and mobility offering.

The motivations for this direct correlation are numerous: • The driverless fleet is expected to be ubiquitous and spread in different parts of the city according to travel demand patterns and surges.

• The driverless fleet is expected to have very good response time in order to compete with other systems and provide a prompt service.

• The driverless fleet is expected to cover trips with a travel time that does not exceed 10 to 15 km.

• The driverless fleet will accommodate some travel purposes more than others, such as Home-Based and Non-home-Based Others rather than Home-based Work trips which are expected to rely on private/ public transit modes and less on ride sharing.

• The driverless fleet will not be owned by specific individuals however by mobility operating companies due to commodity and possible high costs.

16,000 people. The above figures ensure a reliable and trustworthy sample size.

Vehicle Types | UBER has no limit on the type of vehicle that may join its crowd-sourced fleet. This enriches the offering and provides statistical output regarding the different vehicle typologies that are used and the magnitude of their usage in different context, in different areas, and at different hours of the day. The UBER classifications, i.e. UBERXL, UBER Black SUV, Black, Lx, etc. which reflects demand characteristics in terms of the number of occupants and their comfort/ luxury needs. Moreover, the UBER classification of vehicles also collects information on pooling and nonpooling dynamics which informs us about the users’ willingness and openness to sharing rides and bearing longer journey times for reducing travel costs.

If our assumption that driverless fleet will operate in an early phase in a similar manner to ride hailing today, further explorations were carried out in two cities: Los Angeles and Seattle. The choice of the two cities stems from their distinct characteristics and their differing trends from multiple perspectives: building density, road morphology, transit mode share, etc.

UBER Movements The UBER Movements platform, which is openly accessible to anyone, already includes movement information on a number of cities such as Boston, Los Angeles, Pittsburgh, San Francisco, Seattle and several others worldwide, such as London, Manchester, Amsterdam, Paris, Cairo and Johannesburg. Uber Movement is a tool that shares dynamic insights about traffic and mobility in cities where Uber operates. The platform displays an Origin/ Destination matrix, hourly aggregate travel times, and other useful information for reconstructing and associating movements to a given city geography.

A few more reasons why we think that the available movement data on UBER are sufficient to build a case are listed below: Sample Size | The total number of UBER trips per day worldwide and the markets/ cities where UBER managed to penetrate are considered sufficient for building a case. To mention some, UBER has 75 million users and 3 million drivers; UBER registers 15 million trips every day worldwide; UBER operates in 65 states and in over 600 cities; and, UBER employs more than

Total desire lines - UBER data Los Angeles (Left) and Seattle (Right).

39

Travel Behavior As opposed to the three trip purposes classified in transport literature and statistical records, i.e. Homebased Work, Home-based Other, Non-home based Other, UBER movements seem to establish a new profile characterized by the following: active throughout the day with a decrease at night-time; gradual increase in morning time that reaches rapidly a high number of trips; and a constant demand throughout the day which starts again to decay after 22.00.

The UBER movements profile might be associated with demand patterns generated by driverless cars to accommodate a regular demand due to easy access, very good response time and travel expenditure caps that limit travel distance and consequently allow the fleet to be uniformly distributed on the network.

In order to understand further the trip purposes that UBER currently covers, and consequently predict the travel purposes that will be responded to by driverless cars, a quick comparison is carried out among profiles.

It is true that the UBER profile does not match any of the above mentioned three trip purposes however some similarities are noticed once compared to Homebased Other and Non-home based Other trip purposes, hence confirming that driverless cars will most probably correspond to said profiles and not to Home-based Work commuting type of movements that will still depend heavily on public collective and private individual transit modes.

1400000 1300000 1200000 1100000 1000000 900000 800000 700000 600000 500000

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UBER movements mobility profile Los Angeles.

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UBER movements mobility profile Seattle.

Trip purpose - week-day profile Los Angeles.

180000 160000 140000 120000 100000 80000 60000 40000 20000 0

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Home-based Work

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Travel Distances The comparison between Los Angeles and Seattle also implies that despite the distinct urban and morphological traits of the two cities their mean distance profiles are very similar, with a mean travel distance of 13.30 km. A closer look at the two profiles actually shows that travel distances in Los Angeles are higher compared to Seattle during the day; nevertheless, this tendency changes during night hours as Seattle maintains consistency in travel distance between day and time, resulting into a higher night-time travel distance compared to Los Angeles.

15 Travel distance will impact heavily the number

of driverless cars that will be necessary for accommodating demand. The use of UBER cars is considered to be exploited very efficiently compared to the use private cars that are parked for nearly 95% of their lifetime. Another interesting conclusion might also be that travel distances are mainly dependent on the passengers’ willingness to pay for the transit service which defines the limit of travel distance and justifies the similarity between the two cities, as the maximum difference in travel distances between Los Angeles and Seattle is roughly half a kilometer.

6.00%

14.5

Mean of distance (Km) First quarter 2018 (weekday) - UBER data.

5.00%

14 4.00%

13.5

13.30 3.00%

13 12.5

2.00%

12 1.00%

11.5 11

0

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% Trips Seattle

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% Trips Los Angeles

Day time population UBER movements are also concentrated in areas with high Daytime Population. Daytime population refers to the number of people who are present in an area during normal business hours, including workers. This is in contrast to the resident population or people who reside in a given area and are typically present during the evening and night-time hours (United States Census Bureau).

Correlating UBER movements and daytime population in 25 selected zones in Los Angeles and Seattle results into Low

High Low

Los Angeles UBER O-D normalized data (Left) and Daytime population in 2015 (Right).

a high level of correlation in Los Angeles – 0.92 – and a medium-to-high level of correlation in Seattle – 0.76.

Further research might be necessary to explore further the reasons why in some area correlation levels go down drastically and why in some others the relation is pretty direct. Despite this, the high correlation levels confirm that UBER is eventually highly requested by the daytime population and in areas which witness high numbers of daytime users.

High Low

High Low

High

Seattle UBER O-D normalized data (Left) and Daytime population in 2015 (Right).

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Land use As opposed to the three trip purposes classified in the last exercise that was carried out for predicting driverless cars’ travel patterns, an in-depth study of the land use characteristics of the areas that are affected by high UBER movements. Los Angeles was selected for this exercise and the three areas that showed the highest number of UBER movements were as follows: • Los Angeles International Airport • Harbor/Century Transitway Station • Downtown Los Angeles While the first and third highest areas were expected to generate a high amount of UBER movements, the Harbor/ Century Transitway Station area provided an interesting insight on the association between UBER movements and Public Transit service hubs. The transitway station is located in an area that is dominated by residential land use therefore it was concluded that the high demand was not generated by land use but rather by the fact that a number of transit lines, such as the green line, the silver line, commuter express 448 and several others, intersect in that area. This finding is rather interesting as it also sheds light on the complementary role between current ride hailing and public transportation services. Several had debated that driverless cars might compete with Public Transportation and consequently lead to its demise. We believe that the

opposite might occur as we think that, similar to UBER movements, driverless cars will fill in the gap and provide a flexible and efficient transport offering that covers the last part of a journey.

Further explorations were carried out in order to understand if different travel distances could be associated to trips generated by a specific land use.

To this end, O-D analysis for the same three above mentioned areas was carried out which showed interesting findings as the three areas gave three different average travel distances, as shown below: • LAX Airport - Average travel distance 23.88 km • Interchange - Average travel distance 17.95 km • Downtown - Average travel distance 16.34 km Airport movements result to be the highest as people reaching or leaving the airport need to take this ride despite distance. This apparently applies differently to the two other zones where the Public Transport interchange area results to be higher as again, for a similar reason but a different scale, users within a catchment area of roughly 18 km, passengers are willing to take this service for reaching interchange hub before shifting to another mode. Downtown results to be lowest due to a number of daytime movements mainly concentrated inside the city center which reduce the average distance to less than 16.50 km.

Los Angeles desire lines Top three zones of airport, downtown and hub UBER data.

Downtown Airport Hub

0

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15

30 Km

What can we learn for better planning the driverless network?

Assuming that the basic assumption of this article that driverless movement patterns will be similar UBER movements due to a number of similar traits and uses is right, a series of insights and interesting findings are concluded. From a planning point, this might be interesting for carrying out the following:

• If desire lines are clearly identified between origins and destinations for driverless car movements, then the mostly used paths can be predicted and consequently can be equipped with EV charging points, rest points and several other support services and amenities that will be essential for running the driverless vehicles.

• Fleet size estimates may be supported through reference to average travel distance and time, daytime population concentration and surges, the number of per-purpose trips, and other traits in preparing early business plans by ride hailing operators venturing to run a driverless car fleet.

• Areas with high driverless cars demand are the areas that most likely should start witnessing road diet projects and parking transformations as well as reduction or onstreet and off-street parking provisions.

• Early pilot projects for implementing parking areas/ structures dedicated for driverless cars are most likely to be located in areas with land uses similar to the ones indicated above.

• Transit areas shall become the first grounds for experimenting different drop-off and pick-up typologies, smart info-mobility points, passenger customer service points, etc.

Los Angeles Parking areas (Left) and Public transport stops (Right).

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Milan driverless perspectives

44

HOW WILL AUTOMATED VEHICLES BE INTRODUCED TO A CITY LIKE MILAN, WHICH IS CHARACTERIZED

BY ITS HISTORICAL ARCHITECTURE AND NARROW COBBLESTONE STREETS? THE CITY OF MILAN IS WORKING HARD TO PRESERVE ITS HISTORICAL ARCHITECTURAL IDENTITY. EVEN WITH AREAS OF GREAT DEVELOPMENT AND NEW BUILDINGS IN CONSTRUCTION, THE OVERALL CHARACTER AND FEELING OF THE CITY REMAINS. In the last century, European cities, like Milan, transitioned from a transportation system that predominantly consisted of trolley cars and horses to one dominated by cars. This transition did not happen overnight and it brought significant consequences, however, it showed the intrinsic resilient nature of cities.

Currently the city politicians are encouraging the shift towards the use of public transport through different initiatives. Among these, a traffic-limited zone called "Area C" in the central and older part of the city allows access only upon payment. In addition, new metro lines are currently under construction and significant investments are being made in cycling infrastructure and services.

The current public transport network of Milan includes: • • • • • • • •

Suburban Railway Service (13 Lines) Subway System (4 lines + 1 under construction) Tramway Network (17 Lines) Trolleybus System (3 Lines) Bus Network (56 Lines) Car Sharing Scheme (4 different operators) Docked Bike Sharing Scheme (BikeMI) Dockless Bike Sharing Schemes

accommodate two or three vehicular lanes along with buses with higher frequencies and capacities.

Despite the ring roads and transport system Milan has high levels of congestions. We argue that, if properly planned, the introduction of automated vehicles (for mass transit and personal use) could be seen as an opportunity to rethink the current paradigm, delivering a system that increases its overall capacity, by organizing different transport networks and optimizing the current infrastructure.

This study considers four main modes of transport for the future: (i) public transport including tram and bus, (ii) shared driverless vehicles, (iii) private driverless vehicles, and (iv) private person-driven vehicles. These modes of transport are interlinked and should all share the public realm in an organized manner. It is essential to understand how they interact and to create a functional hierarchy among them.

In the following pages, a realistic future scenario is explored and described. This should be considered as one of the many possible alternatives, not necessarily the only solution. Taking into account the presence of person-driven cars, and providing them access to the majority of the city could be seen as the ideal transition scenario towards a more fully automated future.

Train Urban rail Metro network Roadways

The road infrastructure of Milan spreads radially and is composed of several ring roads. The inner ring road, called “The Circle of Canals” (Cerchia dei Navigli) along the Medieval Walls, marks the historical center of Milan (Centro Storico). This ring is followed

by Circonvallazione Interna ring road, the "Cerchia dei Bastioni" of the 16th century Spanish Walls, now marking most of Milan's "Area C" for traffic limited zones. The outer ring road is called, Circonvallazione Esterna, which was designed in Milan's first city plan of 1884, the "Beruto Plan". The final ring is that of the National Bypass Motorways (Tangenziali) for external traffic.

The transport networks of Milan, both public and private, follows a similar logic. This wasn't planned, it is the simple result of what is possible on the roads: most of the trips occur on the main arteries and on the radial ring roads, the only ones with enough space to

Milan general transport network Main connections and means of transport linking the different parts of the city.

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Current network structure The road infrastructure of Milan consists of ring roads and radial roads.

Circonvallazione Esterna

Circonvallazione Interna

Cerchia dei Navigli

Milan road network The existent road network connects the city with a radial configuration.

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Milan public transport network The public transport network follows the same logic of the road network, connecting radially the city.

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Driverless network management Different modes of transport can coexist and operate in a seamless network with a flexible grid.

Milan future transport framework Different means of transport can operate at different levels interlinked with each other.

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PUBLIC TRANSPORT

SHARED DRIVERLESS

PRIVATE DRIVERLESS

PRIVATE CARS

Vehicles Typologies

Road Policies

The hierarchy of different systems should prioritize those with higher occupancy in order to maximize the system capacity.

To allow this some restrictions should be applied. Public transport and shared driverless vehicles could share the same infrastructure, while private cars use other routes so as not to impede mass transit. A driverless-only infrastructure, both for private and shared driverless vehicles, would link the system.

A transport system formed by a set of four modes requires a precise, yet flexible, hierarchy. The result is an overlap of different grids that allow for maximum interchange between transport domains and capillary accessibility to every area of the city.

49

FROM PUBLIC

Mass Transit

Shared Public Vehicles

The backbone of public transit is a reliable and high capacity transit system capable of sustaining the travel demand of daily commuters. In terms of capacity to occupancy, mass transit is the most space efficient and the only one that is extensively applicable in a dense urban context. Therefore, the system will use the main radial and orbital corridors, linking the main transport hubs and railway stations.

This will also ensure continuity with the regional transport network.

The first and last miles of collective trips are managed by a dense system of smaller public vehicles capable of providing capillary accessibility from the suburban residential area to more central and historic areas. Shared public vehicles would also be allowed to use the same infrastructure of mass transit to better connect to adjacent districts.

50

TO PRIVATE

Ride Hail

Private Vehicles

Along with public transport, a flexible public and/or private system of ride hail vehicles will be used to cover the remaining demand within the urban area. This system is already in operation with companies such as Uber and Lyft, and this system will continue to attract users who desire independence from car ownership and convenience in trip making.

Private vehicles with no automation will be allowed to enter the city and circulate with access to every district. Travel will be restricted to dedicated lanes for private vehicle operation.

Adequate parking areas will be provided along these routes with the possibility to interchange with other modes of transport.

51

More or less traffic? Different scenarios for Milan 2030

• Car Occupancy is estimated at 1.6 passengers/ trips in a scenario where CAVs will be shared among different users.

It’s widely understood the introduction of automated vehicles in our cities will have very different impacts.

From a traffic perspective the impacts can range from positive to extremely negative depending if CAVs will complement public transport, integrating transit hub providing additional travel option for users, or if instead CAVs will simply replace private vehicles, making individual commuting even more attractive.

To understand the impact of automated vehicles on a city like Milan we tested different scenarios with a 10-year horizon. We created a conservative and broad assumption scenario based on research done

by McKinsey & Company, our own research on the topic, and PTV, then created a model to visualize performance. Our scenario assumptions are as follows:

Scenario 2: Positive scenario (CAVs in synergy with transit): • Car usage will be reduced by 5% where automated on-demand vehicles will work in full synergy with transit and cover last mile connections or trips in less dense areas.

• Car occupancy will range from 0.8 passengers/trips, given the fact private CAVs will often travel with no passengers.

Baseline Assumptions: • In both scenarios we assume the baseline mobility demand will increase at approximately 6.6%, which is in line with the same growth registered in Milan over the last ten years.

• Both scenarios assume 20% of vehicles will be driverless, which is an average between different sources estimates.

Scenario 1: Conservative scenario (CAVs competing with transit): • Car usage will range from +5% where moving with private automated vehicles will be more attractive for users who will use their pods as extension of living rooms or working desks.

Traffic variation ■ = > +20% ■ = +20% ■ = +5% ■ = 0% ■ = -5%

Among the many interesting results, we highlight two key findings. First, comparing overall traffic performance to current conditions in Milan (2020) there are considerable differences. In our conservative scenario, congestion increases by 26%, which considering congestion levels in 2020, this would be a tremendous negative impact to roadways. While in our positive scenario we see an encouraging reduction of 6% of vehicles, despite the increase mobility demand.

This stark difference in performance shows, if we don’t plan for automated vehicles, we have a lot to lose. The second finding, if we look at traffic distribution in the positive scenario, we notice traffic does not decrease homogeneously. Instead, traffic increases in streets of higher hierarchy, benefitting from the increased capacity allowed by CAVs, and decreases where we have a finer grain of smaller streets. This distribution offers incredible opportunities to pedestrianize and downgrade streets in residential neighbourhoods, increasing the resiliency of Milan. (See the table above for more insights).

Traffic variation ■ = > +20% ■ = +20% ■ = +5% ■ = 0% ■ = -5%

CAV competing with transit + 26% of private vehicles during peak hour

CAV in synergy with transit - 6% private vehicles during peak hour

+ 6.6% mobility demand increase + 5% car usage 20% driverless cars and 80% traditional cars 0.8 average car occupancy for CAVs

+ 6.6% mobility demand increase - 5% car usage 20% driverless cars and 80% traditional cars 1.6 average car occupancy for CAVs

52

private vehicles in peak hour

reference milan 2030 (no cav)

cav competing with transit 2030

cav in synergy with transit 2030

number of cars

117%

126%

94%

vehicles*km

107%

115%

93%

vehicles*hour

112%

128%

89%

speed (km/h)

95%

90%

105%

KPIs for different tested scenarios Percentage variation on 2020 traffic conditions.

Milan 2030. In green the reduced traffic flows are shown, as difference between the scenario where CAVs work in synergy with transit and a reference 2030 scenario with no CAVs at all.

Despite the increased mobility demand, by coordinating CAVs and public transport an overall traffic reduction can be achieved 53

Los Angeles 2060

Award winning project at

Design & Developer Challenge of 2017 of LA Auto ShowŽ and AutoMobility LA™ presented by Microsoft.

54

SYSTEMATICA WAS AWARDED THE “INDUSTRY PICK AWARD” IN THE 2017 DESIGN & DEVELOPER CHALLENGE PRESENTED BY AUTOMOBILITY LA AND MICROSOFT. THE GOAL OF THE CHALLENGE WAS TO ENVISION WHAT LIFE WOULD BE LIKE IN LOS ANGELES IF IT WERE TO HOST A MAJOR INTERNATIONAL SPORTING EVENT, SUCH AS THE OLYMPICS, IN 2060.

THE DESIGN & DEVELOPER CHALLENGE IS LA AUTO SHOW'S ANNUAL COMPETITION THAT INVITES CREATIVE AND ANALYTICAL MINDS TO DESIGN FUTURISTIC CONCEPTS BASED ON HOW WE GET FROM POINT A TO POINT B. SYSTEMATICA’S SHORT FILM PRESENTS A GLIMPSE INTO THE POTENTIAL FUTURE OF MOBILITY, SPORT AND LIFE IN THE CITY OF LOS ANGELS. Los Angeles was built in the era of mass automobile ownership, and its landscape will always reflect that history. But the city has been transformed into something beyond that, through investment in mass public transport and the encouragement of active modes of transport for more than half a century; all carried out through policies adopted by its politicians in order to make the city ‘future proof’. The key to a smarter future is to constantly optimize the existing infrastructure in order to adapt them to the everchanging mobility needs of citizens, by using the latest technologies available, and planning new infrastructure only when needed.

Systematica's vision for LA 2060 is a smart global city of the new era with approximately 23 million residents moving in and around the city through a connected and smart transport network. The ubiquitous presence of the Internet 4.0 links every person and object in a fabric pulsating at a unique pace. Seamless real-time information and responses wipe out boundaries. In a society where time is a precious resource, the need for a reduction in trip-time is crucial. The 20 million attendees expected for the games are adding another layer of complexity to the day-to-day movements of the city. Their mobility needs will be managed through a seamless and highly efficient hyper-connected network of vertical and horizontal transportation.

A high-speed driverless, mass transport network is going to transfer commuters from the greater LA area and surrounding regions to downtown LA. It will also provide convenient connection with San Francisco, Las Vegas, Phoenix and San Diego, de facto enlarging the catchment area and upgrading LA2060 to CA2060 Olympics. In addition, an on-demand shared and personally connected auto–drone fleet is going to make it possible to move around the city, linking every single venue in a matter of minutes. All citizens and attendees will be able to choose their preferred mode

of transport using personal smart devices, while receiving updates and information about their favourite sports events in their native language during the ride.

Today, a surge in person-trip travel demand is reflected in the increase of a proportionate number of personal cars on the road network. Sharing driverless vehicles will allow an increase in the number of persons occupying a single vehicle, rather than a direct increase in the number of vehicles on the road. The driverless fleet will dispatch larger cars when necessary in order to intelligently absorb demand surges. The “mobility as a service” business model will become prevalent; it will forever alter society and urban mobility. The new era of ownership is not a vehicle ownership but a service subscription. A service that guarantees reliable and efficient transfer to any place at any time.

Systematica’s vision sheds light on the fact that mobility and mobility-related technologies could significantly influence the way people experience cities and the distribution of functions within urban areas.

They allow for the “Diffused Venues” solution for any major event, where there would be no need to build new sport facilities or any other type of bulky structure that is unsustainable and will place a financial risk on city administrations; a risk that today many mega cities around the world are not willing to take. The city can make use of its already existing structures through simple reinterpretations and adaptations to host new events both within the city centre and within the outskirts.

This solution is conceivable since reduced number of vehicles means less road infrastructure and facilities related to vehicles such as parking structures, gas stations, repair shops, etc. resulting in cities having ample space for other usages. This solution will allow the event attendees to fully experience Los Angeles with its unique and diverse landscape. LA 2060 is about connecting people and encouraging friendship, respect and excellence through an exciting collective experience.

San Francisco Las Vegas

Phoenix

Los Angeles San Diego

55

The smart experience Systematica’s vision for LA 2060 is a new approach towards smart planning. It envisages a future where mobility demand is managed through a seamless, efficient and hyperconnected autonomous network of vertical and horizontal transportation offering.

The Smart Venues Unlocking LA Potentials The hidden potentials of LA will eventually be unlocked.

How? By simply moving more efficiently, optimizing time by spending it in the right places at the right times and by enjoying the daily commute as machines are in control of steering, pressing the clutch and changing gears.

Diffused venues Venues are no longer seen as places just for events to take place in but as simple interpretations and adaptations of existing places for hosting those events.

Events are naturally constrained by the need of their users to move, park, walk, sit, watch, leave, search, queue, etc. But mobility devices will change, the number of moving vehicles will be significantly reduced and the roads we built will not be utilized anymore, leaving ample space for alternative uses.

Smart venues Venues are freed and automatically turned into smarter, cleaner and more fun places to spend time. Smart venues are connected through smart vehicles and are in turn connected through a smart network that learns, stores information and ultimately gets to know individuals and their preferences better.

Smart venues will adapt better to security levels and will improve safety conditions through improved user interface and connected networks.

56

New Mobility Mass rapid transport Although the driverless vehicles are going to accommodate the urban mobility, it is impossible to ignore the importance of public transport. The future of public transport would be a high-speed, driverless service that connects nearby cities and regions to each other. The existing infrastructure is going to be adapted to the new technologies so we will see science fiction meet scientific fact!

Advanced aerial mobility Today’s cars are limited to surface transport, leaving transit along the Y-axis to other devices. Drone development and advancements in urban air mobility technologies will create a new multi-dimensional plane that connects us not only at ground floor, but also at rooftops.

Demand surge Our future transit devices will eventually be able to react smartly to long-term changes or abrupt surges in travel demand during the peak hours of the day, special events and similar. Sharing driverless vehicles will allow for an increase in the number of persons occupying a single vehicle during demand surges, rather than a direct increase in the number of vehicles circulating within the network. The driverless fleet will dispatch larger cars when necessary in order to intelligently absorb demand surges.

57

Planning principles A comprehensive study of current mobility dynamics and other demographic characteristics of Los Angeles County is the first step for planning the future of the city.

Low

High

Residential

Commercial

Traffic intensity

Parking provision by main land uses

Pasadena

Santa Monica

Pasadena

Santa Monica

Downtown LA

Downtown LA Anaheim

Anaheim

Torrance

Torrance Long Beach

Least Walkable

Most Walkable Walkability Index

Pasadena

Santa Monica

Long Beach

Low

High Injury collissions involving pedestrian and bikers

Pasadena

Santa Monica

Downtown LA

Downtown LA Anaheim

Torrance

Anaheim

Torrance Long Beach

58

Industrial

Long Beach

A thorough analysis of the city's socio demographics and transport infrastructure (such as land use, population, road traffic, parking, public transportation, soft mobility and many more) is completed using Big Data in order to have a deeper understanding of the mobility demand patterns and conditions.

to identify the main challenges of the city, the potentials of its mobility patterns and opportunities within its general mobility framework that should be considered in the bigger picture for addressing future mobility challenges.

The city’s mobility dissection tool serves as an analytical basis for further elaborations and identification of possible interventions. It helps

Low Provision

High Provision

Low

High

Cycling network provision

Intersection Density

Pasadena

Santa Monica

Pasadena

Santa Monica

Downtown LA

Downtown LA Anaheim

Anaheim

Torrance

Torrance Long Beach

Low

Long Beach

High

Low

High

Bus stop frequency

Pasadena

Santa Monica

Job density

Pasadena

Santa Monica

Downtown LA

Downtown LA Anaheim

Torrance

Anaheim

Torrance Long Beach

Long Beach

59

Chapter 4

PRIVATE

Driverless vehicles raise a fundamental question about car ownership. If cars can arrive on their own when and where they are needed, will someone still buy a car and pay for its extra maintenance costs?

Today, private cars are preferred as they provide their owners with reliable, flexible, comfortable and affordable modes of transport.

These assets are the main challenges that driverless vehicles will face as they replace traditional cars and the private ownership model of vehicles.

60

SHARED vs The key challenge to a sustainable and inclusive urban mobility system is to move more people with less vehicles. High capacity public transport should remain a key component of urban mobility and the space previously dedicated to cars should be dedicated to active modes of transport, as they remain the cheapest, healthiest and most sustainable modes of transport.

It is the responsibility of city politicians to ensure diverse and flexible mobility options with smart policies and integrated payment options. The business model of the future should change from “car ownership” to “service subscription”, which is highly reliable and affordable for everyone.

61

Future-proofing airport car park provision

62

THINK OF AN AIRPORT AND THE IMAGES THAT COME TO MIND ARE PLANES, POLISHED GLASS FACADES, GIANT CHOCOLATE BOXES AT DUTY FREE SHOPS, QUEUES TO PASS AT SECURITY CONTROL, HIGH-END SHOPS AND FOOD COURTS. BUT IN REALITY, THE PRIMARY COMPONENT OF AN AIRPORT IS ITS PARKING LOTS, WHETHER THEY ARE IN CLOSE PROXIMITY TO THE TERMINAL OR MORE REMOTE FOR LONGER STAYS. To define the expansion plan of an airport it is necessary to consider a long term scenario, to make sure the terminal building and its surroundings are able to accommodate future changes seamlessly.

Traditionally this meant providing ample interior space for passenger increase and new commercial activities, but the rise of driverless vehicles could drastically change the entire arrival experience as well as the usage of the airport and its surrounding land.

case, parking provision will decrease, although not dramatically, as there will be a portion of users that will still prefer to use their private vehicle. Traffic congestion will instead be eased, due to higher public transport efficiency and ridership increase.

Once the outcome of these three scenarios is assessed it will be possible to make strategic decisions and further refine the calculation while also exploring other hybrid situations.

This section addresses how parking provision will be greatly affected by driverless vehicles: this is particularly true in the case of airports, where long term car park areas occupy as much (if not more) space as that for airplane operations. We are currently exploring this within a confidential expansion plan of an airport in Europe.

To simplify the large amount of potential future scenarios, we are addressing the project through three situations very well defined and different from each other.

The first scenario (A) describes a situation where driverless vehicles are owned in the very same way people own cars today. The result, in terms of parking is uncertain and depends on pricing: if the price is low, parking demand will be the same as today; if instead parking pricing is higher, there will be a situation where driverless cars go back empty and park at home or in an alternate location. This scenario would most likely increase traffic conditions and lead to the detriment of public transport.

The second scenario (B) creates an optimistic situation where the vast majority of driverless vehicles are shared through ride-hail services like UBER or other similar providers. On the one hand, this scenario increases congestion, moving with shared vehicles will be convenient and relatively inexpensive. On the other hand, parking provision would decrease significantly as there will be only a limited portion of users that will opt to reach the airport with their private vehicle.

The third and last scenario (C) delineates a situation where automated vehicles will be mainly collective and serve as a public transport backbone. In this

Parking footprint in Orio Al Serio (BGY) Airport The area dedicated to parking in an airport significantly exceeds the one dedicated to airplanes Note: The project displayed above is not the project referenced in the article.

63

Driverless: development scenarios Different alternative scenarios are taken into account in order to drive the decision making process and opt for the most effective mobility strategy.

SCENARIO A Private driverless vehicles • Vehicles owned • Travel cost similar to regular cars • Increased comfort for users • Immediate availability of vehicles • Risk of significant travel demand increase • Risk of high number of trips with car occupancy < 1 • Risk of increased congestion • Risk of reduced public transport ridership

SCENARIO B RoboCabs (Shared driverless vehicles) • Vehicles owned by Public Authority or by Service Operators (Taxi companies, Ride-Hailing service providers, etc.)

• The service works like a regular taxi service • Possibility of individual or collective use of vehicles • Users' cost is between private vehicle and conventional taxi • Parking demand potentially null (drop-off/pick-up only) • Risk of increased congestion

SCENARIO C Driverless vehicles dedicated to Public Transportation • Level 5 autonomy will be reached earlier on public transport vehicles • Increased vehicle efficiency, more capillary and flexible schedule • Private vehicles will be both driverless and traditional • Users' cost in line with current public transport costs • Increased ridership • Reduction of parking demand • Reduction of vehicular traffic 64

Driverless vehicles dedicated to the Public Transportation The table shows the trend in demand for parking and mobility according to the considered scenarios.

Scenario

Base

A

B

C

Estimated parking provision 16724 (Average stay, 7gg. High-range scenario)

16724

836

12543

Total passengers (p/h)

7724

7724

7724

7724

Total vehicle movements

2878

7771

3339

2159

Vehicle movement IN

1635

4414

1896

1226

Vehicle movement OUT

1243

3357

1442

933

Driverless vehicles dedicated to Public Transportation Each scenario implies and proposes a reduction of the required square meters for parking; it is possible to save space suitable for other uses.

65

Credits: Arabian Business

Masdar: the first driverless network

66

Transportation and Mobility

FEW HAVE BEEN AS AMBITIOUS AS THEPROJECTS PERSONAL RAPID TRANSIT SYSTEM MASDAR CITY, WHERE A COMPLETELY NEW URBAN PARADIGM WAS DESIGNED AND TESTED. TODAY, TEN YEARS LATER, IT IS POSSIBLE TO LOOK BACK AND LEARN FROM THIS EXPERIENCE: THE PERSONAL RAPID TRANSIT SYSTEM, THE nearest PRT station and continue their 3.5.7 The traffic model FIRST ATTEMPT TO DELIVER A FULLY trip on PRT until the station nearest their SHARED, destination. Also the LRT and the RTL The traffic model, based on the multiCONNECTED DRIVERLESS SYSTEM AT URBAN lines are modelled. The commuters level network and the matrix arisen from arriving at the two SE parking lots can the demand analysis, used the BE NOW SCALE, COULD RETROFITTED DUE TO choose to reach Masdar using the PRT equilibrium iterative model to evaluate GREAT SAFETY IMPROVEMENTS THE VEHICLEor the LRT. The model assigns IN the trips the traffic flow on each link. to both systems.

Specific BPR flow curves, as found in HUMAN literature, were INTERACTIONS. adapted to fit the sharp PRT network capacity drop off.

and driverless vehicles is no longer required: how long will it take to populate and retrofit Masdar with additional public amenities?

3.5.7

The PRT network has been assigned an average speed that depends on the

road: 33 km/h for the spine, the Tripsmain shorter thanobjective 200m have been The ofspecific Masdar City, which was edge and the NE–SW connections; 21 considered pedestrian trips. There are conceived byto Foster +km/h Partners almost ten years ago, for the cross-connections.

The 3675 which correspond 8% of the difference reflects the greater complexity total number of trips. These are was to provide residents and visitors with the highest of the cross-connections. The vehicles essentially people who walk from their are modelled to take alternative paths residence to their place of work, or from quality of life and the lowest environmental footprint. and distribute along the lines, in order to the LRT and HST stations to work, and the minimum time-cost for the do not board the neutral PRT. These do not A carbon city, achieve 17 kilometers away from trip include the trips on foot between the downtown Dhabi, with around 45,000 residents origin of the trip andAbu a PRT station and The results of the simulation are as vice-versa. and 60,000 commuters in a plot of approximately 6 follows: It is important to note that the choice of 200m as a maximum walking distance is Number of iterations: 25 square kilometersentirely reliant on renewable energy.

Total number of trips (on foot) 3,675 justified by the assumption that a traveller will choose to walk when the

Total number of trips (PRT) 43’,62

PRT trip, including walking to and from

In the order to adequately respond to the overall aspiration Max flow: 4,107 passengers/2hours stations requires a longer perceived Min trip length (PRT) 200 m than the whole trip on foot. A of time-cost the project and support the mobility of the city Average trip length (PRT) 1870 m number of pedestrian trips will be longer Max trip length (PRT) 5000 m than 200m, but for simplicity these are fulfilling the “carbon neutrality” design principle, assigned to the PRT as well.

The final results are shown Systematica developed anassignment integrated and multi-layered in figure 9.

The model is based on two connected transport and mobility strategy, including a dense and networks: one is the pedestrian network, which uses the whole network of main driverless Personal Rapid Transit (PRT) network, on streets of Masdar and can walk in both directions; the other is the PRT network reserved lanes.

It provides direct access (less than which uses part of the network of main streets and can run in one direction only 200m on average) and ensures comfortable, efficient in each street. and seamless door-to-door trips throughout Masdar The pedestrians that need to perform a trip longer than 200m will reach the Citadel, completely free of privately owned vehicles.

It is noteworthy that Systematica was appointed from 3-113 the early concept to Procurement phase to cover all aspects of transport and mobility.

TheMasdar elevated PRTcity Express network model: (suggested) traffic

elevated PRT express network (suggested) The traffic model, based on the multilevel network and the matrix arisen from the demand analysis, used the equilibrium iterative model to evaluate the traffic flow on each link.

The PRT system was designed to accommodate for around 46,000 trips in the AM peak period (7.009.00) of a typical weekday, ensuring full accessibility to 50% of urban functions in less than 100m, thanks to 83 stations with an average waiting time between 2 minutes (50% of trips) and 3 minutes (90% of trips).

Although the full implementation plan of the original concept of Masdar City has yet to be accomplished the overall sustainable strategies have been radically revised. Masdar City can be acknowledged as the world’s first attempt to deliver a complete zero emission urban development model, which has surely paved the way towards low carbon and, to some extent, climate resilient developments.

Today, after 10 years, our cities are becoming more and more intelligent, fully connected and “driverless”.

Thanks to the availability of sensors and devices, it is possible to understand and monitor in real time what happens, by treating and analyzing Big Data to develop new services and plan more efficient and sustainable infrastructure. Grade separation between pedestrian 67

Internal mobility strategy Sustainable mobility is the core component of Masdar City. According to IRENA, headquartered in Masdar City, transport accounts for approximately one third of the global energy consumption. Carbon emissions from transport have also increased 28 percent since 2000, according to the International Energy Agency (IEA).

A new approach: driverless Personal Rapid Transit (PRT) The cityâ&#x20AC;&#x2122;s walkable environment offers multiple clean-tech transit options including Personal Rapid Transit â&#x20AC;&#x201C; an internal electronic driverless mode of movement. PRT vehicles connect all points of the city through a dense network composed of 84 stations arranged so that over 50% of the system users do not need to walk more than 100m to reach any station. Externally, two major Abu Dhabi networks, the Metro and the Light Rail Transit (LRT) are planned to pass through Masdar City.

The personal rapid transit (PRT) system is made up of electrically powered driverless vehicles, which guarantee passenger privacy in a similar way to a car since, once boarded, no other passengers will board the vehicle along the route. The vehicle will not stop until the chosen destination is reached. The PRT vehicles are completely connected, autonomous and driverless. Their movements are controlled by a centrally operated system, which consists of the redundancy and safety features essential to guaranteeing a faultless and reliable service, round the clock.

Apart from guaranteeing internal connections, the PRT will serve as an interface connection between external and internal traffic. PRTâ&#x20AC;&#x2122;s will connect to remote parking lots where commuters may leave their cars, and all LRT and metro stations.

68

Personal Rapid Transit Vehicles These personal vehicles run at a different level from the pedestrian traffic, along PRT-only corridors located under the street level. Credits: Techgenmag

Personal Rapid Transit These vehicles are electric powered, reducing CO2 emissions, noise and vibration. Credits: Techgenmag

69

Credits: Foster + Partners

CityMobil2: Oristano

70

EUROPEAN CITIES FACE THE FOUR MAIN CHALLENGES OF CONGESTION, LAND USE, SAFETY, AND ENVIRONMENT IN THE TRANSPORT SECTOR. CAR OWNERSHIP RATES ARE HIGH, ALTHOUGH IN THE CENTERS OF LARGE METROPOLITAN AREAS CITY ADMINISTRATORS TRY TO ENCOURAGE THE SHIFT TOWARDS OTHER MODES OF TRANSPORT

BY INVESTING IN MASS PUBLIC TRANSPORT AND RESTRICTING CAR ACCESS, BUT PERIPHERAL AREAS AND SMALLER CITIES REMAIN HIGHLY CAR DEPENDENT. In order to adequately respond to the overall aspiration CityMobil2 was a major research, development and demonstration project that addressed the integration of automated transport systems in the urban environment. The project highlighted three main barriers: the implementation framework, the legal framework and the hard-to-predict wider economic effect.

The aim of CityMobil2 was to address these barriers and try to remove them by featuring 12 cities, each tasked with revising their mobility plans and adopting effective automated transport systems. The project procured two sets of automated vehicles and delivered them to the five most motivated among the twelve cities for 6 to 8 months demonstrations in each city.

Oristano was among the cities that were selected for demonstration. City of Oristano is the capital of the Province of Oristano in the central-western part of the island of Sardinia with approximately 32,000 inhabitants.

The touristic harbor of Marina of Torre Grande, that has over 400 berths, is located in the Gulf of Oristano at the western end of the village of Torre Grande. It is only 6 km away from Oristano and accessible by a city bus line. The peaceful, former fisherman village, has become the residence for many inhabitants of Oristano who prefer the beach to the town.

from east the railway line and the SS131 highway.

The streets of the city center are still similar to their original construction, made of narrow and irregular streets, poorly adapted to an orderly flow of vehicles and inadequate for pedestrian and cycling infrastructure and parking spaces.

The city of Oristano, in general, lacks areas dedicated to pedestrians and cycling. There are two pedestrian zones; one in the historical center and the other in the village of Torre Grande, each with an extension of about 12,000 square meters (approximately 0.75 square meters per inhabitant). There is also a restricted traffic zone of about 16,500 square meters (about 0.50 square meters per inhabitant).

The public transport network consists of six radial bus lines that connect the suburbs to the city center, and two circular lines that run through the city center, travel around the old town, and reach the main attractors of the city.

The diagnosis of the current transport network in Oristano highlights the inadequacy of the traditional public transport system to satisfy the mobility demands inside the city. The traditional public transport service appears adequate only for the suburban connections (high demand during rush hours) and not for urban connections (low demand during the whole day).

The City Mobil2 project is an excellent chance to test the possibility of providing an urban mobility solution that is flexible, user-friendly (especially for elderly and disabled people), low emission, modern, affordable - due to low operation costs, and compatible with the existing road network especially with the narrow streets in the historic city center.

Oristano is not directly connected to the peninsula. The connections by sea are through the ports of Cagliari, Olbia and Porto Torres and by air through the airports of Cagliari, Olbia and Alghero. The land connections with the major cities of Sardinia are by rail network and the regional main road SS 131/E25. The three main cities of Sardinia; Cagliari, Sassari and Nuoro are respectively 90, 130 and 80 kilometers away from Oristano. An industrial port specialized in transporting dry bulk cargoes is located in the Gulf of Oristano.

Oristanoâ&#x20AC;&#x2DC;s urban form and expansion through the years is strongly influenced by the natural and infrastructural constraints that surround the city: from north the river Tirso, from west the coast, from south the lagoon and 71

Testing a driverless transport system in Torre Grande Eleven potential sites were identified for the implementation of the innovative transport system.

Some of these were chosen to intercept the vehicle traffic entering the city center from the surrounding areas, while others were chosen to support the regeneration process of historic city center and to connect intermodal points such as the railway station, the port and the airport. Two sites along the Marina of Torre Grande were chosen for touristic reasons.

The site selected for the test drive is located along the coast, next to the beach where there is a wide section consisting of the pedestrian promenade. The wide street section is well suited for creating a dedicated path, without creating excessive inconveniences, and ensuring safety of passengers. The automated vehicles travel on a dedicated guideway along the waterfront pedestrian street for a total distance of about 1300 meters between the two terminal stops. At the center of the route, in the square overlooking the old tower, space is sufficient to build a temporary structure that can house an information desk to provide information about the project. Near the tower square, a city bus

stop guarantees a good level of interconnectivity with the driverless vehicles.

The average width of the waterfront street is 16 meters between the seafront buildings and the beach, however the profile available for the infrastructure has an average width of about 7 meters, the rest of the area is utilized for paved walkways set at different heights from the asphalted strip and occupied by trees, street lighting, benches and flowerpots. However, stop shelters can be built over the walkways.

The waterfront is crowded especially in the summer months but also during the rest of the year, on sunny weekends. Typical users include those who go to the beach for a walk, to meet up with friends, and to eat or drink something at kiosks or restaurants. Driverless vehicles are expected to satisfy the travel demand for recreational trips, mainly concentrated in the evening and night hours, with little or no time constraints. They should satisfy these same recreational travel demands, mainly concentrated in the evening and night hours, with very few or no time constraints.

Catchment zones around driverless stations - Torre Grande One of the eleven identified areas for testing the automated network.

N 5 4 T 2 1

S 72

Tested scenarios for the driverless route.

The selected location of the project was Torre Grande area.

Dimensioning of the internal service GRT Preliminar dimensioning of the service based on driverless shuttles' data from producer companies.

Number of vehicles on service

Frequency (minutes)

Combination per (minutes)

Passengers per vehicle

Travel supply (pphpd)

2

2

One per direction

10-12 seated

86-106

2

2

One per direction

20-24 seated + standing

172-206

4

4

Two per direction

10-12 seated

172-206

4

4

Two per direction

20-24 seated + standing

344-412

4

3.5

Two per direction

10-12 seated

172-206

4

3.5

Two per direction

20-24 seated

244-412

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Chapter 5

NEW DEVELOPMENT Infrastructure projects typically have 30 to 50 year life spans, and some of them might become obsolete in half of that time, therefore while designing new developments and their related infrastructure it is crucial to consider some degree of flexibility in order to make sure they are adapted to the upcoming changes in mobility. Futureproofing that new development is a challenging task considering all the uncertainties around time and the type of changes faced, but at the same time, it guarantees fewer costs and faster adjustments in the future.

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RETROFITTING vs The driverless revolution will provide cities with a unique opportunity to rethink their infrastructure, streets, and public spaces. During the past century, cities were auto-oriented in their design. With the driverless revolution they have the chance to become more people-oriented, providing safe, reliable and inclusive urban mobility, dedicating more space to public amenities and reducing their environmental impact. Retrofitting cities to adapt them to the upcoming changes in mobility trends is a gradual and ever-evolving process that will vary based on the distinctive characteristics of each city in terms of mobility dynamics, social, cultural and economic aspects.

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Pilot Driverless Zone in Riyadh

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LOCATED NORTH OF RIYADH, IN SAUDI ARABIA, THE PROJECT IS AN 8 SQUARE KILOMETER MIXED-USE DEVELOPMENT CONSISTING OF RESIDENTIAL, COMMERCIAL, MEDICAL, EDUCATIONAL FACILITIES, AS WELL AS A THEME PARK. THE DRIVERLESS ZONE IN A PLANNED NEW DEVELOPMENT IS A PILOT PROJECT THAT ATTEMPTS TO DEFINE AN IMPLEMENTATION PLAN FOR DRIVERLESS VEHICLES. THE DEVELOPMENT AIMS TO BECOME A REGIONAL DESTINTION AND A PLEASANT PLACE TO LIVE FOR THE RESIDENTS. THE PROJECT SEEKS TO CREATE A HIGH-QUALITY PUBLIC REALM, AS WELL AS, ENCOURAGE WALKABILITY THROUGH APPROPRIATE PLANNING, ADEQUATE SHADING, AND PASSIVE COOLING STRATEGIES.

from the district. Defining peak hours allows for a constantly moving fleet capable of matching demand during most of the day while also maintaining some reserved vehicles ready to cover the demand surge in the right moment.

IIn the near future driverless vehicles will be in operation. The planning for this new technology is underway in many parts of the world and the planned development is at the forefront of this changes.

Designing a guiding plan with consideration as to how driverless vehicles can operate with design schemes and other land use considerations will provide the City with the flexibility to ensure as this technology begins to deploy itâ&#x20AC;&#x2122;s done so efficiently and within the vision of the district.

The driverless zone implemented in the development is mainly a residential district. This area will only be accessible by driverless vehicles, or by walking.

Only four main roads may be accessed by all types of vehicles, while the remaining blocks may only be accessed by public transport or by driverless vehicles.

The availability of different functions inside the district and the short distance from the transit hub are crucial for the implementation of this pilot project. Travelling to and from Riyadh is possible using public transport through the transit hub, which is accessible using personal or shared driverless vehicles. Residents will also be able to use driverless vehicles to move around the development.

Limiting the pilot project to a smaller area allows for the redesign of urban infrastructure in order to adapt to new mobility solutions and technologies. For example, the street sections become narrower as the need for on-street parking is drastically reduced in favor of driverless drop-off points. The freed space is expected to provide more comfortable walkways with trees and adequate urban furniture.

As the project moves forward and driverless technology begins to debut, strategic decisions such as the number of driverless vehicles in operation will be determined by passenger demand. In turn, the location of remote parking structures will have been strategically determined based on time and distance

The driverless zone diagram The diagram shows the driverless zone and the go-through arteries.

The ellipse is the transit hub.

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Fleet estimation A methodology to estimate the number of driverless vehicles In order to determine the number of vehicles required to accommodate travel demand within a district with a population of approximately 100,000 users, it is crucial to first understand the usersâ&#x20AC;&#x2122; behavior.

It is expected that driverless vehicles will be used by residents, employees and visitors.

The following table shows the mobility profile used for each category. The values of car occupancy, car turnover and internal capture are the basic assumptions that reflect the expected behavior of users based on current trends and expected changes.

These assumptions are used to estimate the fleet and respond to the mobility demand of the user population.

For instance, the car turnover value is based on the travel time needed to reach the transit hub as a portion of residents living in the Driverless zone are expected to travel to Riyadh using public transport. .

MODAL SHARE PT

30%

70%

Cars

Fleet estimation results The peak of travel demand is at 17.00 and consists in 20,758 trips. The necessary fleet to accommodate the travel demand is 5,288 driverless vehicles.

The number of vehicles required during other times of the day is lower, for example during midday offpeak hours 3,572 vehicles are required to match travel demand. In order to avoid an overestimate of driverless vehicles from traveling around, it is essential to develop strategies to manage the fleet during the entire day. .

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Car Occupancy

Car Turnover

Internal Capture

Residents

1.5

2

10%

Employees

1.1

3

7%

Visitors

1.2

3

8%

Project masterplan The whole south residential district is used for driverless pilot project.

Driverless offer and trips demand curves The diagram shows the hourly curves of demand and offer.

25000,00

20000,00

15000,00

10000,00 5288,78 5000,00

0,00

3110,83

07:00

08:00

4240,09

09:00

4371,11 4119,23 4561,63 4257,94 3602,54 3571,75 3983,01

10:00

11:00

12:00

13:00

14:00 CAV

15:00

16:00

5227,94 4997,03 5126,15 3213,43 2835,86

17:00

18:00

19:00

20:00

21:00

1525,71 1150,11 22:00

23:00

Trips

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A dynamic mobility system During the day, the travel demand curve will change constantly, as is shown in the following chart.

In order to properly accommodate the varying travel demand levels throughout the day the supply of driverless vehicles will dynamically adapt through a centralized dispatching and control system.

Two different strategies are identified for matching demand. The first type consists of a fleet that will be dispatched during peak hours, while the second type will be dispatched in response to unpredictable travel demand surges throughout the day.

The first type will respond to the typical predictable peaks, mainly the peak hours determined by commuters traveling to work places or homes during the morning and evening hours. Given the predictable nature of this fleet, parking structures in which the fleet is stored can be located remotely. Remote parking structures will free the development from unnecessary parking areas.

The second type will consist of a fleet which requires a lower response time that allows it to match demand almost in real time. This requires that parking areas of this fleet type are closer to the built areas, where travel demand is produced. While the first fleet type requires a single consolidated large parking structure, this second fleet type requires smaller parking areas that are spread throughout the city or Master Plan as vehicles need to be dispatched upon a very short notice and need to reach their destinations in a short time.

Drivrless fleet exercise curves Different parking strategies are used to create a dynamic system able to sustain peaks and off-peaks demands.

6000

Remote parking

5000

Diffused parking

4000

Always moving fleet

3000 2000 1000 0

07:00

08:00

09:00

10:00

11:00

12:00

13:00

14:00

-1000

Moving fleet

80

On-site parking

Remote parking

15:00

16:00

17:00

18:00

19:00

20:00

21:00

22:00

23:00

On-street parking and drop-off points The moving fleet will not require on-street parking space, however, this fleet will require an adequate number of drop-off points to ensure efficiency of services and the safety of boarding and alighting passengers. The density of drop-off points depends on the adjacent land uses as roads with active frontage, such as those including commercial facilities, will generate a higher turnover and consequently require more drop-off points and area.

In order to calculate the density of dropoff points, the AM peak hour demand is used as the basis. The fleet required for accommodating the AM peak hour consists in 5,289 vehicles, traveling within an 8.9-kilometer area inside the driverless zone.

Assuming a boarding/alighting time of 60 seconds, it is possible to estimate a baseline need of one drop-off point every 100 meters.

On streets that are more active, this value should be adjusted to one drop-off point every 50 meters to match the higher turnover.

100m

More active blocks

More active blocks

Drop-off points density One drop-off point per block could both satisfy the demand and provide a capillary accessibility to the driverless system.

Driverless zone demand index Expected travel demand is correlated with population density and facilities density. In the streets with higher demand more drop-off points are provided.

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Flexible curbsides Urban curbsides are one of the most valuable and underutilized spaces of cities, especially in the urbanized core. Today, curbs are often used for parking, in addition to other functions such as cycling infrastructure, deliveries, passenger pickup areas, green stormwater infrastructure and small commercial areas.

Efficient and smart curb management is essential for rethinking the streets of the future.

Future technology, based on vehicle to infrastructure connectivity will make it possible to communicate changes to roadway and curb functions in real time to driverless vehicles making it possible to be flexible with curbside management. The following shows how functions of curbs can be changed depending on the land use of the neighborhood and time of the day.

There are several advantages of flexible curb management: • • •

Reducing the number of zombie driverless cars (circulating without passengers) during off peak times Reducing the necessity to build several new remote parking structures Better and more efficient service especially for pick hours as cars don’t have to travel long distances from remote parking structures

Typical curbside in Riyadh

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Commercial areas Commercial districts of cities are areas composed of a mixture of commercial buildings and have designations such as, downtown, central business district, financial district or commercial strips. These areas are mainly used during working hours of the day (generally 8 AM to 8 PM). The curbs in commercial areas can be dedicated to functions such as passenger pickup, parking for transit modes such as bikes and scooters, delivery points for local businesses, and when space is freed after the peak-hours, it can be turned into parking for driverless vehicles.

Residential areas Dense residential areas are less crowded during the weekdays as workers commute to jobs in commercial districts. During this time curbs can be turned into parking for driverless vehicles. In the early afternoon the space should be given back to inhabitants for functions such as kids playing areas, commercial activities, passenger pickup areas, and parking for sustainable and personal transit modes. Later in the evening as the space frees up from people it can become driverless parking once again, significantly reducing the waiting time for pick-up in the morning peak hour and reducing the fuel consumption as cars don’t have to travel from remote parking structures.

Adaptive street design Today we have to design roads anticipating tomorrow's needs, hence allow for gradual and effective changes.

Today Every street today should be planned keeping in mind that tomorrowâ&#x20AC;&#x2122;s traffic congestion could drastically decrease and therefore streets have the potential to be narrowed, with a lower number of lanes.

In order to accommodate future changes, utilities should be carefully planned in order to allow sections to be converted into green and walkable areas in the future.

Areas to be kept free from utilities

Tomorrow Automated vehicles could increase traffic efficiency and road capacity, therefore allowing for shorter safety distances between vehicles. This would lead to the narrowing of some infrastructure without reducing the overall performance of the road network: external lanes should therefore be dedicated to soft modes and converted in order to accommodate for pedestrians and cyclists with the Right of Way.

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Dubai automated humanized

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THE TRANSITION TOWARDS AUTOMATED MOBILITY WILL NOT BE THE SAME IN EVERY CONTEXT: SOME AREAS IN THE US AND NORTH EUROPE ARE ALREADY TESTING DRIVERLESS VEHICLES ON PUBLIC ROADS, WHILE OTHERS ARE OPTING FOR A MORE CAUTIOUS APPROACH WHERE THE INTRODUCTION OF THESE VEHICLES WILL BE MORE GRADUAL. THIS IS THE CASE IN DUBAI. In any context, there will be a transition phase where automated vehicles and traditional cars will coexist.

This phase is probably the most decisive as every strategic decision taken from government, public bodies and legislators will dramatically affect the adoption curve of this technology.

accommodate dedicated bus lanes.

A pilot study for Dubai Business Bay area was carried out to prove this point. Designing a BRT on a reserved lane is an intervention capable of producing beneficial effects both in the near future and in a long term scenario. A BRT system is generally very cost effective, with a good balance between investment and results. In fact it is the preferred choice for mass mobility in many emerging countries. Public transport on a reserved lane today can be easily retrofitted as a driverless lane in the not so distant future, so that, during the first phase of the driverless vehicles introduction, the issue of coexistence can be seamlessly addressed.

This phase is also the most critical from the perspective of road safety: the urban environment, designed for traditional car needs might suddenly become outdated and inefficient. If we instead look at road design, there will be an insufficient amount of some road aspects (like drop-off areas) and redundancy of others (like onstreet parking) and others.

Enacting policies oriented at transforming the infrastructure gradually should be put in place and Dubai is moving in this direction. While investing in new automated vehicles for RTA taxi fleet, the transport authority announced the possibility to test driverless cars on dedicated bus lanes, which are becoming more and more popular.

Temporary pilot projects of driverless zones or corridors like this one are the very beginning of the transition; once the effects of the automated mobility paradigm are understood in more detail, additional changes can be made in a more permanent way, like the removal of vehicle lanes, sidewalks widening, etc.

An important factor to take into account at this stage is the relation between driverless and traditional vehicles. To express this true potential of automation, the driverless vehicles should be the only vehicles on the road. The presence of non-connected vehicles compels the driverless system to consider human behaviours, so it has to be â&#x20AC;&#x153;more carefulâ&#x20AC;? and therefore slower than its potential.

To ease this phase, pilot projects with driverless vehicles only areas or lanes should be implemented.

Restricting part of an infrastructure to the driverless vehicles only, would allow a quicker diffusion of the new technology, as well as a broader consensus from the public. This strategy could serve very well as a best practice for extensive road retrofitting, and it could start immediately in a context like Dubai, with ample availability of infrastructure large enough to

Dubai is moving toward a driverless future through several investments and innovations

5+design | BUSINESS BAY | BUS LANES STUDY | 27 September 2017

17

Driverless pilot project in Business Bay In 2018, driverless vehicles were tested in three pedestrian areas of Dubai. Photo Courtesy: Gulf Business

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From Marasi Drive to Marasi Driverless The fast-pace growth of Dubai was initially centered around the automobile, with a lot of investment in large road infrastructure. In the past decade, this has shifted toward a more sustainable paradigm, with investments in multiple dimensions (e.g. Dubai metro lines) and developers became more and more interested in delivering a class-A public realm.

The automation era will be the perfect (and maybe only) moment to fully rethink Dubai' streets, providing models and examples in line with other international best practices. In other words, talking about automation in Dubai means focusing on the positive outcome for pedestrians.

The example of Business Bay is emblematic: a first class development in a prime central location, with investment in retrofitting its public realm after the opening of the new canal that connects to the old creek.

The proposed strategy solves current issues, mainly related to the car-oriented character of the site, while preparing the development for the upcoming shift toward automation. In this particular case, the implementation of dedicated bus lanes along Marasi Drive, in line with RTA strategy, would allow for the immediate improvements of public transport infrastructure and, at the same time, would turn Business Bay into the very first development in Dubai to have 8 Km of infrastructure ready to accommodate pilot projects of driverless vehicles.

While working on this initiative, we found it very challenging to include this strategy in a standard Traffic Impact Study. While professionals are working to create the right laws for the implementation of driverless vehicles, we should at the same time question how this strategy should also affect the current planning and approval processes of long-term developments.

Pedestrian Level of Service The diagram shows the different levels of pedestrian comfort in Dubai Business Bay and Downtown.

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2020 - Driverless in pedestrian only environment or on reserved driverless lanes

2025 - Reserved lanes for driverless / buses / taxis

2040 - Driverless sharing the roads with all other vehicles

2050 - 100% driverless vehicles

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Credits: EXPOfagola

Milan Innovation District (MIND)

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EXPO 2015 WAS A UNIVERSAL EXPOSITION HOSTED

BY THE CITY OF MILAN WITH THE THEME, “FEEDING THE PLANET, ENERGY FOR LIFE". THE EXPO SPANNED SIX MONTHS FROM MAY TO OCTOBER IN 2015 AND BROUGHT MILLIONS OF VISITORS TO MILAN. The event put Milan at the center of international attention. The average number of daily visitors during the last months of the Exhibition were around 140,000, with more than 250,000 people during weekends and other special exhibition days resulting in more than 20 million visitors over the course of the entire event. The Exhibition involved 138 official participants, representing 86% of the world population, 67 enterprises and organizations of civil society, more than 60 Heads of State and Government, and around 270 delegations. The Expo 2015 site is located 15 kilometres northwest of Milan and covers an area of 1.1 square kilometres.

With respect to site location, within the infrastructural system, the level of accessibility that characterizes the Expo 2015 site within the regional and metropolitan context is directly related to the strategic location of Milan. Milan is emerging as a node of European importance within the articulated framework of Italy, Europe, as well as, international connections.

The site is easily accessible by line 1 of the Milan subway system, as well as greater connections with railway systems, including high-speed rail, regional trains, and metropolitan services. It also benefits from its proximity to strategic road corridors, such as the A8/A9 “Milano - Laghi”, the A4 “Torino – Venezia” motorways, and the West and North stretches of the ring motorway of Milan.

The Milan Innovation District, or MIND, was established in 2011 with the purpose of acquiring and converting the site that hosted Expo 2015 into a new urban reality. To this end, in 2017 a tender was launched for the urban regeneration process of the site.

Since then, Systematica has been part of the international team, selected by Arexpo, to reimagine the former site of Expo 2015. The Master Plan, developed by Lendlease, will provide Milan with a new paradigm for the city of the future.

The Milan Innovation District area will be transformed into a science, knowledge and innovation park with the purpose of creating a place open to the world. This redevelopment will promote territorial excellence by investing in technology, innovation and education , add value to the investments already sustained and build upon the Expo legacy. The science and technology park

will be a hub for excellence in the fields of Life Sciences and Healthcare, Biotech and Pharma, Agri-food and Nutrition, and Data Science and Big Data. The site will be redesigned with modern architectural features to attract investment and generate economic benefits that will proliferate throughout the country with scientific, residential, and cultural functions. The site will include amenities to support activities in sport and leisure to benefit the greater community.

From an urban perspective, one of the main aspirations of MIND is to explore and test future models of living as well as future paradigms of people movement. With this respect, MIND is planned to act as international test-bed for some of the most disruptive and pioneering technologies and solutions on mobility.

The MIND mobility plan is focused on principles of walkable user-centric development and is shaped by an effective Mobility as a Service (MaaS) model, including the provision of e-mobility solutions, demand-responsive systems, intelligent-cognitive infrastructures, and future proofing-adaptive transport assets.

Mobility plays a key role in future proofing new developments, especially in order to optimize time and resources. The strategic mobility plan specifically devised for MIND includes a well-integrated and calibrated set of innovative transport systems and services.

As part of the internal mobility model, a highly efficient Group Rapid Transit (GRT) system operated by autonomous shuttles is proposed to connect the railway station of Rho-Fiera station with East Gate of MIND. This GRT system will provide effective and convenient last-mile connections as well as facilitate an internal transport system within the MIND Park district and along the “Decumano”.

Over time, this system will have the potential to evolve into point-to-point on-demand mobility service operated by robotaxi.

The potential driverless GRT system is considered complementary to traditional public transport. In the first phase, the vehicles will operate similar to a bus line, traveling along the central boulevard in both directions and stopping at fixed stops strategically planned along the route. The two-kilometre route will be covered in 6 minutes. The development of the site has been phased to accommodate future technologies and regulations that will spur growth and continually make the district an attractive site for investment. As investment and transport demand rise, the GRT System can extend beyond the boundary of the district and connect to other transport systems.

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Operational scheme of the driverless GRT system A preliminary operational scheme was developed as part of the MIND mobility plan. Specific shuttles were not chosen since the technology is still evolving.

However, most autonomous shuttles have an average capacity of 15 passengers with a maximum operating speed of 25 km/h.

Based on the operations of vehicle charging, GRT service is planned to be continuous for 24 hours a day, with a frequency of every 3 minutes during AM peak and every 12 minutes during night hours. This frequency would provide reliability to travellers and a great benefit to the MIND Park.

An estimate in the number of vehicles was forecasted based on the route length, number of stops. The forecast took into consideration 8 working hours, 5 charging hours, and the possibility that one of the vehicles may be stopped for maintenance, and one must be available in case of breakdown or exceptional demand conditions (events, concerts, etc.). Therefore, the plan estimated 8 vehicles and 4 charging stations will be required.

0m

Frequency (veh/h/direction)

5

6

10

12

Cadence terminus (min)

12

7.5

6

5

Cadence by direction (min)

12

7.5

6

5

Minimum number of circulating vehicles

1

2

2

3

Capacity/h/direction

75

120

150

180

Capacity/h

150

240

300

360

Dimensioning of the internal service GRT Preliminar dimensioning of the service based on driverless shuttles' data from producer companies.

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500 m

Stablished path for the Group Rapid Transit system The scheme shows the localization of the route in the central area of the project, in combination with other modes of transport.

In service

Recharge

Maintenance

Available/reserved

Proposed driverless system schedule The diagram shows the driverless fleet schedule throughout the day.

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Looking Ahead The driverless revolution has begun. The harbinger are the vast number of start-ups and technology firms innovating vehicle designs and ideas. The estimate is by 2021 fully autonomous vehicles will be available. This prediction relies heavily on the approval of regulation, however, as driverless vehicles deploy, how will cities respond? Cities with established policies to properly guide the transition will likely be the most successful.

This is evident with the deployment of the e-scooter.

When BIRD launched in Santa Monica in 2017, it was done by rolling out scooters in an impromptu manner.

Soon sidewalks and streets were flooded with scooters and city officials were left with citizen complaints, injuries, and policy that didn’t offer regulations.

However, when mobility companies work with cities to launch with permission and have the necessary permits it can be the difference between success and failure.

While this technology has some time before deemed a success, there is no arguing with the demand from people using scooters for last-mile type trips. The same will be true of driverless vehicles. The technology is progressing faster than regulations and guidance, and there will be early adopters that will embrace the technology, even if there are some risks with the technology. In the Milan Driverless perspectives article, we created a scenario that would consider the impact of driverless vehicles on a city such as Milan where the city is defined by historic architecture. This type of planning will be necessary for all cities as they begin to envision their communities and cities with a new mode of transport that will disrupt everything from planning methods to infrastructure design.

When we have a new technology, we take it on very quickly. Consider the telephone took nearly 75 years to be in 50% of households but just 10 years for 80% of households to use smartphones. We adopt technologies faster in part because the infrastructure for newer technologies takes less time to construct. The question remains in how a technology like driverless vehicles will be adopted and if the infrastructure will be in place to support the cascading effect the technology will cause. In the United States, it’s estimated there are between 1 and 2 billion parking spaces the cost of which can range between $5,000 to $10,000 for surface parking and $25,000 to $50,000 per space for structured parking. A number of studies show we need 10%-15% of this parking. So how will parking change if AV’s change how we move? In Chapter 3, Parking Demand, we illustrated various parking strategies 92

based on vehicle ownership models. No longer in need of parking, we’ll be able to build more densely and repurpose infrastructure. This is a complete shift in how our land functions in urban areas and just one of the cascading effects the driverless revolution will bring. If our current rate of adoption to new technology holds true, when driverless vehicles become available, we will take it on very quickly.

At the time of this writing, the COVID-19 virus was spreading across the world infecting millions of people.

There was no immunity from the virus, major cities were forced to lockdown to limit the spread, at the same time stymieing economies and causing dramatic decreases in transportation movement. The effects of the virus are still being felt and many cities are discussing how we return to the new normal and raising questions on what the future of transportation looks like amidst the COVID-19 virus, future viral infections, as well as, climate change. Early evidence suggests mode choice will change among those who have the option to use private vehicles, rather than using bus or subway for their trip, as people consider their health and are more conscious of social distancing. The virus outbreak reinforces the need for planning methods and tools to account for disease outbreaks among the considerations of resiliency and driverless vehicle forecasting. The ideas presented throughout this book consider the technologies, methods, and design considerations the driverless revolution will bring, but now, we’d be remiss to discount the impacts COVID-19 has brought forth in our future transportation planning and the changes that will occur over the coming months and years. However, this next revolution in technology and focus on health will be the time to reimagine our cities together, in partnerships that consider the reshaping of the landscape and the creation of a better transportation future.

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Systematica Established in 1989, Systematica is a transport planning and mobility engineering consultancy with its main office in Milan (Italy) and subsidiary offices in Beirut (Lebanon) and Mumbai (India).

Systematica operates at multiple scales and provides a wide array of integrated consultancy services in the sectors of transport and urban planning, including national, urban and development scale transport planning, strategic advisory and due diligence for infrastructure investments, traffic analysis and management, mobility engineering in complex buildings and events venues with a special focus on pedestrian flows, parking design, vertical transportation, and application of advanced infomobility systems and technologies.

Systematica is committed to its company statement and mission to deliver highly ethical and professional invest in Research and Development for seeking new approaches and solutions for the ever-changing issue of mobility and transport planning; put social inclusion on top priority, and; search for sound engineering solutions to support sustainable growth. www.systematica.net

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Transform Transport Transform Transport is a research unit focused on innovative mobility solutions.

While mobility and transport related technologies are emerging with increasingly fast paced, Transform Transport explores how they can have positive impacts on our cities, neighborhood and buildings.

Founded by Systematica, it grounds on 30 years of experience in the field of transport planning and mobility engineering, investigating the future of Milan and other cities worldwide. www.transformtransport.com

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Credits Driverless: more or less?

Second Edition ISBN: 978-88-944179-3-7 Team: Filippo Bazzoni, Filippo Bregola, Rawad Choubassi, Diego Deponte, Benedetta Fagioli Marzocchi, Jonelle Hanson, Federico Messa, Claudia Ponti, Dante Presicce, Nicola Ratti, Anahita Rezaallah, Antonela Sborlini, Alessandro Vacca A special thanks to all collaborators of Systematica who contributed to this book.

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