CPB

Corporate Partnership
Board

Urban Mobility System Upgrade

How shared self-driving cars could change city traffic
What if all trips in a city were carried out by a ﬂeet of self-drivingcars shared by users? This study explores the potential outcomes ofsuch a radical upgrade in an urban mobility system. It concludes thatup to 9 out of 10 conventional cars could become redundant undercertain circumstances. Vast amounts of public space would be freed
for other uses in such a scenario. However, the total volume of travelincreases in most scenarios and the net beneﬁt of such an urban
mobility system upgrade decisively depends on the choice of vehicle
type, the level of penetration and the availability of high-capacity
public transport to complement the shared self-driving car ﬂeet.
The work for this report was carried out in the context of a project
initiated and funded by the International Transport Forum’s Corporate
Partnership Board (CPB). CPB projects are designed to enrich policy
discussion with a business perspective. Led by the ITF, work is carriedout in a collaborative fashion in working groups consisting of CPB
member companies, external experts and ITF researchers.

Urban Mobility System
UpgradeHow shared self-driving cars
could change city traffic

Corporate Partnership Board
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Urban Mobility System
UpgradeHow shared self-driving cars
could change city traffic

Corporate Partnership Board
ReportAbout the International Transport ForumThe International Transport Forum at the OECD is an intergovernmental organisation with 54 member
countries. It acts as a think tank for transport policy and organises the Annual Summit of transportministers. ITF is the only global body that covers all transport modes.
ITF works for transport policies that improve peoples’ lives. Our mission is to foster a deeper understandingof the role of transport in economic growth, environmental sustainability and social inclusion and to raise
the public profile of transport policy.
ITF organises global dialogue for better transport. We act as a platform for discussion and pre-negotiationof policy issues across all transport modes. We analyse trends, share knowledge and promote exchange
among transport decision makers and civil society.
Our member countries are: Albania, Armenia, Australia, Austria, Azerbaijan, Belarus, Belgium, Bosnia andHerzegovina, Bulgaria, Canada, Chile, China (People’s Republic of), Croatia, Czech Republic, Denmark,Estonia, Finland, France, Former Yugoslav Republic of Macedonia, Georgia, Germany, Greece, Hungary,
Iceland, India, Ireland, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Mexico,
Republic of Moldova, Montenegro, Netherlands, New Zealand, Norway, Poland, Portugal, Romania, RussianFederation, Serbia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, Ukraine, United
Kingdom and United States.

Disclaimer
This report is published under the responsibility of the Secretary-General of the International Transport Forum. Funding for thiswork has been provided by the ITF Corporate Partnership Board. This report has not been subject to the scrutiny of International
Transport Forum member countries. The opinions expressed and arguments employed herein do not necessarily reflect the official
views of member countries. This document and any map included herein are without prejudice to the status of or sovereignty overany territory, to the delimitation of international frontiers and boundaries and to the name of any territory, city or area.

www.internationaltransportforum.org

TABLE OF CONTENTS – 3

Table of contents
Executive summary....................................................................................... 5
Introduction ................................................................................................. 7
1.

Research review: Shared and self-driving car fleets .................................. 9
Mixing shared autonomous with traditional car fleets ..................................................... 9
Automated mobility-on-demand for Singapore .............................................................. 10
Autonomous taxi system for New Jersey....................................................................... 10
Taxi pooling for New York City ...................................................................................... 11
Transforming personal mobility: Three regional cases .................................................. 112.

Case study: The city of Lisbon............................................................... 13

3.

Model description ................................................................................ 15
Demand generation ...................................................................................................... 15
Trip generation by users ............................................................................................... 16
Car configurations ........................................................................................................ 16
Role of the mobility dispatcher ..................................................................................... 16

4.

Testing shared-mobility scenarios .......................................................... 18
Modal shares in different scenarios............................................................................... 18
Impact on fleet size ...................................................................................................... 19
Impact on travel volume ............................................................................................... 20
Vehicle fleet requirements at peak hours ...................................................................... 24
Impact on parking and street space .............................................................................. 25
Impact on vehicle use................................................................................................... 26
Distribution of vehicle types ......................................................................................... 27
Impact of a fully electric vehicle fleet ........................................................................... 28
Changes in waiting and travel times ............................................................................. 28
Distribution of detour distance and time values ............................................................ 31
Occupancy levels and efficiency of matching ................................................................. 31

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4 – TABLE OF CONTENTS

5.

Policy insights ..................................................................................... 33

Bibliography ................................................................................................ 34

Tables1.
2.
3.

Infrastructure and public transport provision in Lisbon.......................................................................... 14
Share of transport modes for Lisbon................................................................................................... 14
Mode share distribution for different TaxiBot and AutoVot scenarios ....................................................... 19

4.

Fleet size for different TaxiBot and AutoVot scenarios ........................................................................... 19

5.

Weekday travel volumes under different TaxiBot and AutoVot scenarios ................................................. 20

6.

Peak-hour travel volumes under different TaxiBot and AutoVot scenarios ................................................ 217.

Road occupancy by road class during morning peak for selected TaxiBot and AutoVot scenarios ................ 23

8.

Number of cars travelling during morning peak in selected TaxiBot and AutoVot scenarios ........................ 25

9.

Ratio of cars travelling in-peak and off-peak in selected TaxiBot and AutoVot scenarios ............................ 25

10.

Maximum number of parked vehicles for different TaxiBot and AutoVot scenarios .................................... 26

11.

Shares of idle time for different TaxiBot and AutoVot scenarios ............................................................. 27

12.

Share of travel by vehicle type for different TaxiBot and AutoVot scenarios ............................................. 27

13.

Average waiting and travel time for different TaxiBot and AutoVot scenarios ........................................... 29

Figures1.

Flowchart of the agent-based model for trip generation ........................................................................ 16

2.

Visualisation of shared self-driving car simulation for Lisbon.................................................................. 173.

Time distribution of travel volumes for selected TaxiBot and AutoVot scenarios ....................................... 21

4.

Time distribution of travel volumes by road class for selected TaxiBot and AutoVot scenarios .................... 22

5.

Spatial distribution of the variation of peak hour travel volumes for TaxiBot system in Lisbon ................... 24

6.

Sales of new cars in Lisbon by car type............................................................................................... 28

7.

Distribution of waiting times for TaxiBot scenario with high-capacity public transport ............................... 29

8.

Distribution of daily travel time under different TaxiBot and AutoVot scenarios ........................................ 30

9.

Statistical distribution of detour for the TaxiBot scenario with high-capacity public transport ..................... 31

10.

Matching efficiency for shared rides in TaxiBot scenario with high-capacity public transport ...................... 32

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EXECUTIVE SUMMARY – 5

Executive summary
This report examines the changes that might result from the large-scale uptake of a shared and self-driving
fleet of vehicles in a mid-sized European city. The study explores two different self-driving vehicle concepts,
for which we have coined the terms “TaxiBot” and “AutoVot”. TaxiBots are self-driving cars that can beshared simultaneously by several passengers. AutoVots pick-up and drop-off single passengers
sequentially. We had two premises for this study: First, the urban mobility system upgrade with a fleet of
TaxiBots and AutoVots should deliver the same trips as today in terms of origin, destination and timing.
Second, it should also replace all car and bus trips. The report looks at impacts on car fleet size, volume oftravel and parking requirements over two different time scales: a 24-hour average and for peak hours only.

What we found
Nearly the same mobility can be delivered with 10% of the cars
TaxiBots combined with high-capacity public transport could remove 9 out of every 10 cars in a mid-sized
European city. Even in the scenario that least reduces the number of cars (AutoVots without high-capacity
public transport), nearly eight out of ten cars could be removed.
The overall volume of car travel will likely increase
A TaxiBot system with high-capacity public transport will result in 6% more car-kilometres travelled than
today, because these services would have to replace not only those provided by private cars and traditional
taxis but also all those provided by buses. An AutoVot system in the absence of high-capacity public
transport will nearly double (+89%) car-kilometres travelled. This is due to repositioning and servicing trips
that would otherwise have been carried out by public transport.
Impacts on congestion depend on system configuration
A TaxiBot system in combination with high-capacity public transport uses 65% fewer vehicles during peak
hours. An AutoVots system without public transport would still remove 23% of the cars used today at peak
hours. However, overall vehicle-kilometres travelled during peak periods would increase in comparison totoday. For the TaxiBot with high-capacity public transport scenario, this increase is relatively low (9%). For
the AutoVot car sharing without high capacity public transport scenario, the increase is significant (103%).
While the former remains manageable, the latter would not be.
Reduced parking needs will free up significant public and private space
In all cases examined, self-driving fleets completely remove the need for on-street parking. This is asignificant amount of space, equivalent to 210 football fields or nearly 20% of the kerb-to-kerb street spacein our model city. Additionally, up to 80% of off-street parking could be removed, generating newopportunities for alternative uses of this valuable space.
Ride sharing with TaxiBots replaces more vehicles than car sharing with AutoVots
An AutoVot fleet requires more vehicles than a TaxiBot system to provide the same level of mobility.
AutoVots also require considerably more repositioning travel to deliver that mobility.
The size of the self-driving fleet needed is influenced by the availability of public transport
Around 18% more TaxiBots and 26% more AutoVots are needed in scenarios without high-capacity public
transport, compared to scenarios where shared self-driving vehicles are deployed alongside high-capacity
public transport. Without public transport, 5 000 additional cars are required for the TaxiBot system andanother 12 000 in the AutoVot system. Car-kilometres travelled would increase by 13% and 24%
respectively.
Managing the transition will be challenging
If only 50% of car travel is carried out by shared self-driving vehicles and the remainder by traditional cars,
total vehicle travel will increase between 30% and 90%. This holds true irrespective of the availability of

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6 – EXECUTIVE SUMMARY

high-capacity public transport. Looking only at traffic during peak hours, the overall number of carsrequired increases in all but one scenario, namely TaxiBots with high-capacity public transport.

Policy insights
Self-driving vehicles could change public transport as we currently know it
For small and medium-sized cities it is conceivable that a shared fleet of self-driving vehicles could
completely obviate the need for traditional public transport.
The potential impact of self-driving shared fleets on urban mobility is significant. It will be shaped by policy
choices and deployment options
Transport policies can influence the type and size of the fleet, the mix between public transport and sharedvehicles, and ultimately, the amount of car travel, congestion and emissions in the city.
Active management is needed to lock in the benefits of freed space
Shared vehicle fleets free up significant amounts of space in a city. Prior experience indicates that thisspace must be proactively managed in order to ensure these benefits are fully reaped. Management
strategies can include restricting access to this space by allocating it to specified commercial or recreational
uses, such as delivery bays, bicycle tracks or enlarged footpaths. Freed-up space in off-street parking could
be used for urban logistics purposes, such as distribution centres.
Improvements in road safety are almost certain. Environmental benefits will depend on vehicle technology
The deployment of large-scale self-driving vehicle fleets will likely reduce both the number of crashes andcrash severity, despite increases in overall levels of car travel. Environmental impacts remain tied to perkilometre emissions and thus will be dependent on the adoption of more fuel-efficient and less polluting
technologies. TaxiBots and AutoVots are in use 12 hours and travel nearly 200 kilometres per day,compared to 50 minutes and 30 kilometers for privately-owned cars today. More intense use means shorter
vehicle lifecycles and thus quicker adoption of new, cleaner technologies across the car fleet.
New vehicle types and business models will be required
A drastic reduction in the number of cars needed would significantly impact car manufacturer businessmodels. New services will develop under these conditions, but it is unclear who will manage them and how
they will be monetised. The role of authorities, both regulatory and fiscal, will be important in guiding
developments or potentially maintaining market barriers. Innovative maintenance programmes could bepart of the monetisation package developed for these services.
Public transport, taxi operations and urban transport governance will have to adapt
Shared self-driving car fleets will directly compete with urban taxi and public transport services, as
currently organised. Such fleets might effectively become a new form of low capacity, high quality public
transport. This is likely to cause significant labour issues. Yet there is no reason why current public
transport operators or taxi companies could not take an active role in delivering these services. Governanceof transport services, including concession rules and arrangements, will have to adapt.
Mixing fleets of shared self-driving vehicles and privately-owned cars will not deliver the same benefits
as a full TaxiBot/AutoVot fleet - but it still remains attractive
In all fleet-mixing scenarios, overall vehicle travel will be higher. Also, vehicle numbers will increase inthree out of four peak hour scenarios. Improved traffic flow of automated cars could mitigate congestion up
to a point. However, the public policy case for self-driving fleets alone (without high-capacity public
transport) may be difficult to make based solely on space and congestion benefits, due to the increase in
overall travel volumes. Nonetheless, even in mixed scenarios, shared self-driving fleets could be a costeffective alternative to traditional forms of public transport, if the impacts of additional travel are mitigated.
“All in” deployment of shared self-driving fleets may be easier in circumscribed areas such as businessparks, campuses, islands, as well as in cities with low motorisation rates.

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INTRODUCTION – 7

Introduction
For this study, we examined the potential outcomes of a radical change in urban mobility configuration thatwould result from the implementation of a shared and fully autonomous vehicle fleet.
To perform this assessment, we developed a new agent-based model to simulate the behaviour of all
players of this system: First, the travellers, as potential users of the shared mobility system. Second, thecars, which are dynamically routed on the road network to pick-up and drop-off clients, or to move to,from, and between stations. Third, a dispatcher system tasked with efficiently assigning cars to clients while
respecting the defined service quality standards, e.g. with regard to waiting time and detour time.
We based this analysis on a real urban context, the city of Lisbon, Portugal. We selected Lisbon as a casestudy due to the availability of data required to develop an agent-based simulation and because of its
relative comparability with other European urban contexts.
This report is structured as follows: In chapter 1, we review similar research work to that which we carriedout. In chapter 2 we characterise the Lisbon case study and highlight the main mobility-related attributes ofthat city for comparison with the outputs of our modelling exercise. Chapter 3 then briefly describes thesimulation model and chapter 4 investigate several iterations of our basic shared-mobility scenarios.
A discussion of policy implications from the results obtained concludes this study in chapter 5.
The work for this report was carried out in the context of a project initiated and funded by the International
Transport Forum’s Corporate Partnership Board (CPB). CPB projects are designed to enrich policy discussion
with a business perspective. They are launched in areas where CPB member companies identify an
emerging issue in transport policy or an innovation challenge to the transport system. Led by the ITF, workis carried out by in a collaborative fashion in working groups consisting of CPB member companies, external
experts and ITF researchers.
The principal author of this report was Luis Martínez of the International Transport Forum who was also
responsible for undertaking the modelling and analytical work upon which the report is based, some ofwhich was completed during his time at the University of Lisbon. Special thanks to José Viegas who
instigated and supervised this work. Substantial inputs were provided by Philippe Crist, who contributed tothe project design and edited the final report, and to Maël Martinie who undertook valuable research insupport of the work. Participating Corporate Partners in this report were Michelin and Nissan.
The project was coordinated by Philippe Crist and Sharon Masterson of the International Transport Forum.

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1. RESEARCH REVIEW: SHARED AND SELF-DRIVING CAR FLEETS – 9

1. Research review: Shared and self-driving car fleets
Cars are underused assets. They are mainly active during peak hours and rarely for more than 10% of theday – in fact, most are used for less than one hour a day. Much of their capacity is also underused sincecars typically display low levels of occupancy in each trip – often with only one occupant. And despite this,they are highly valued assets – so highly valued that households put up with such levels of inefficiency inorder to derive specific benefits relating to comfortable, door-to-door and schedule-less travel. Could thisinefficiency be reduced while retaining these benefits?
Our work investigates the convergence of shared transport services, including car sharing/ride sharing andself-driving vehicle technology. The former has traditionally concerned largely informal and ad-hoc sharing(household car sharing, car pooling, etc.) but, starting in the 1980s, new models of co-operative-based andcommercial car sharing emerged. These forms of car sharing allowed individuals to subscribe to shared
fleets whose vehicles they reserve, access and use only when they need them. Pricing for these services istypically calculated on a per-hour or per-kilometre basis (or both). These services are situated somewhere
between traditional car rental services and taxis and have proven popular in many urban areas since they
allow individuals to have access to cars without necessarily owning one. With the arrival of ubiquitousinternet access and dedicated app-based services, car sharing has quickly grown in popularity andsophistication and numerous successful services have been deployed around the world. At the same time,
there has been an analogous development in terms of technological sophistication with ride-sharing
services – especially for app-based on-demand services. These can take the form of taxi-like services or
peer-to-peer real-time ride sharing. As with app-based car sharing, these forms of ride sharing have provento be tremendously popular and pioneering companies in this field have generated billions of dollars inmarket capitalisation.
All of these services currently require a driver and so it seems interesting to examine what might be thenext step in these services’ evolution, namely, their integration with self-driving technology. This is notnecessarily just a theoretical exercise – both Google and Uber have signalled both explicitly and implicitlythat they see great potential for shared and autonomous vehicle fleets in both the car-sharing andride-sharing modes. Several researchers have also examined the comprehensive impacts of the deploymentof shared and self-driving vehicle fleets in various contexts. We focus on five of these in this section.

Mixing shared autonomous with traditional car fleets
A scenario developed by Fagnant and Kockelman (see box on p. 12) presents a model of a fleet of Shared
Autonomous Vehicles (SAV) in a city of a size similar to that of Austin, Texas. The model has the following
characteristics: each SAV travels autonomously, i.e. without human intervention, with at least one
passenger to its final destination. In this model, there are no stops between origin and destination to board
additional passengers, and no deviation occurs from the initial trip.
After each trip, the SAV moves on to the next traveller or repositions itself to a more favourable location forlower cost parking and faster future passenger service. This implies that there are no fixed stands thattravellers have to reach to start their trips since the SAV comes to them. The fleet is comprised of
traditional petrol-fuelled SAV sedans, i.e. no hybrid, electric or alternative-fuel vehicles were modelled.
Finally, the authors consider only 3.5% of all trips as making use of the SAV network, the rest being madewith conventional human-driven vehicles.
The modelling results suggest that each SAV would serve 31 to 41 persons per day, with an average
waiting time below 20 seconds. Each SAV would replace nearly 12 conventional vehicles, and would lead tothe elimination to 11 parking spaces per SAV in operation.

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Overall distance travelled increased by 11% compared to a traditional human-driven self-owned fleet. This
increase in travel distance was largely due to the relocation of the SAVs and the distance travelled to collectthe next passenger. However, environmental impacts of the implementation of such a fleet are positive,
with 5.6% less greenhouse gas emissions, 34% less carbon monoxide emitted, as well as a 49% reduction
in volatile organic compound emissions, among others, compared to the traditional US light duty fleet.
Emission reductions could be further reduced by considering a more intensive use of the SAV which would
lead to a shorter life cycle for each vehicle (-1.5 to 2 years), hence an earlier replacement by more recent
and less polluting vehicles. The use of an electric fleet could even further reduce emissions.
Among the identified limitations to this modelling exercise is the lack of a real-world context. Future
modelling should be based on the geographical characteristics of a real urban area to provide more precise
results capturing heterogeneous land use and travel patterns, seasonality and weekends. Other changes
could include incorporating car-pooling options to improve the use of the SAVs as well as reducing overall
distances travelled and associated environmental impacts. SAV impact on congestion is not measured inthis paper.

Automated mobility-on-demand for Singapore
A 2014 study by Spieser et al. (see box on p. 12) explores the effect of a complete removal of the entire
private vehicle fleet in Singapore, and its replacement by a shared self-driving fleet. The findings suggestthat such a fleet could remove two thirds of the vehicles currently operating in Singapore while still
delivering all of the trips currently made by private vehicles. The authors note several benefits ofautonomous driving, such as better safety performance, an increase in the convenience and optimisation of
trips, a decrease in congestion, lower overall costs, lower parking space requirements, etc.
While the case study focuses on shared self-driving vehicles, the authors note the findings could be
extended to more general situations, such as shared vehicles with human drivers. However, the paper
concludes that the most cost and time-effective option would be that of an automated mobility-on-demand(AMoD) system, as the shared self-driving model appears almost 50% cheaper than the model based onhuman-driven cars. However, such a system increases the overall distance travelled, as well as vehicle-use
intensity, which may erode benefits linked to travel times and congestion.

Autonomous taxi system for New Jersey
Zachariah et al. (see box on p. 12) model the implementation of a fleet of autonomous taxis (ATaxis) in
New Jersey, based on origin-destination trips derived from travel surveys. These trips approximate the real
trips made by people in New Jersey every day. Passengers go to a station and take an ATaxi, which thenbrings them to the station nearest to their final destination. Other passengers can join the ride, provided
that their destinations are located not too far from the destination of the first passenger.
Results suggest that there is significant ride-share potential. This potential is sensitive to relaxing the travel
scheduling constraint away from the original trip. Average vehicle occupancy increases along with theincrease of the waiting time at the station (to increase the chance that another passenger joins). It also
increases when destinations of passengers are close to each other. The simulation shows that demandvaries temporarily and spatially: The potential for ride sharing increases during peak hours, for example,
and in locations such as railway stations. Taking that into account could make it possible for such a system
to contribute to significant reductions of congestion in heavy-traffic areas, alongside a corresponding
reduction in pollution.

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1. RESEARCH REVIEW: SHARED AND SELF-DRIVING CAR FLEETS – 11

Taxi pooling for New York City
This modelling work by Santi et al. at MIT’s SENSEable City Lab (see box in p. 12) looks at the potential
impact that the sharing of taxi rides could have on taxi fleet operation in New York City. It does so bylooking at the detailed origin, destination and timing of every single taxi trip taken in the city over thecourse of a year and investigates which of these trips could have been shared, because riders weretravelling from roughly the same areas to roughly the same destinations at approximately the same time.
A shared fleet is constructed in such a way that every real trip taken occurs in the model with no more thana five-minute delay to the real arrival time. Results suggest that the total number of kilometres driven by ataxi in New York City could be reduced by 40% with such a shared taxi system. This would consequently
lead to large cuts in service costs, traffic congestion and emissions, as well as a reduction in fares paid by
individual travellers. The authors conclude that it would be possible and efficient to implement a shareabletaxi service in New York City.
The study also indicates that, even though the base case accounts for 150 million trips undertaken by
13 000 taxis in a large city, the model can be replicated in smaller cities up to a quarter of the size of themodel city. The model does not take into account changes in the behaviour of passengers, who could
respond to lower fares by increasing their use of the system. Also not fully addressed is the potential
segmentation of the market, with a low end offering shared rides and a high end offering single passenger(or party) rides.

Transforming personal mobility: Three regional cases
This model exercise carried out by Burns et al at Columbia University (see box on p. 12) examines ashared, self-driving and centrally dispatched fleet of vehicles in three different environments: A mid-sized
US city (Ann Arbor, Michigan), a low-density suburban development (Babcock Ranch, Florida) and a large
and densely-populated urban context (Manhattan, New York). It uses travel survey-based data on average
trip distances, trip-making rates (e.g. trips per hour) and travel speeds to help characterise travel in theregions studied. A combination of queuing, network and simulation models is used to calculate travel
patterns and vehicle requirements. The modelling system generates trips to be serviced by a fleet of sharedself-driving vehicles via a centralised dispatching systems that keeps track of the locations of all vehicles.
The origins and destinations of trips are generated randomly over the whole of the region. Trips arerequested at a constant average rate, times between requests are exponentially distributed and the single
class of vehicle used in the model operates at a constant travel speed.
The study finds that for the 120 000 residents of Ann Arbor who travel less than 70 miles a day, the shared
fleet could provide near instantaneous access to a vehicle servicing their request, but with only 15% of thevehicles currently needed to carry out these trips. However, overall travel would increase due to the needfor repositioning vehicles. Similar findings emerge from the Babcock Ranch case study (3-4 000 vehicles fora projected population of 50 000 people). In the case of Manhattan, the study finds that a fleet of
9 000 taxis could replace all of the trips taken today by over 13 000 taxis with average waiting times of lessthan one minute, much lower than today.
The Columbia study also looked at costs from the consumer perspective in all three contexts. These
increase with both travel and trip-making rates and differ depending on whether the vehicles used mimic
traditional cars or are purpose-built for the shared and self-driving task. In the case of Ann Arbor, theauthors estimate that the combined ownership, operating, parking and time value costs (linked to driving
and searching for parking) are approximately USD 1.60 per mile for a conventional personally-owned car
operated 10 000 miles per year. In contrast, they find that a shared and self-driving service using
conventional cars would cost users about USD 0.41 per mile. Using a small, purpose-built 1-2 occupant car,reduces costs per trip mile even further, to USD 0.15 per trip mile. These are very significant cost

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reductions. Shared self-driving fleets are found to cost more in the Babcock Ranch case study (USD 0.46
per trip mile) and in the Manhattan case study (USD 0.50 per trip mile) than in the Ann Arbor case study but in both instances significantly less than the current usage of a conventional car fleet in those contexts.
This sample of recent publications emphasises the interest and relevance of research in shared andautonomous mobility services. It also provides an indication that findings in this research area are wellaligned, pointing to significant potential for reducing the number of vehicles and the parking space needed
without reducing the level of mobility and service. Building on this work, the study presented in chapter 2
goes further by presenting detailed results of a full-scale simulation for a mid-size European city.

Studies reviewed in this section
L.D. Burns, W.C. Jordon, B.A. Scarborough (2013), “Transforming personal mobility”, Earth Island
Institute, Columbia University, 2013.
D. J. Fagnant, K. M. Kockelman (2014), The travel and environmental implications of shared autonomousvehicles, using agent-based model scenarios , Transportation Research Part C: Emerging Technologies, 40,
March 2014.
P. Santi, G. Resta, M. Szell, S. Sobolevsky, S.H. Strogatz, and C. Ratti (2014), Quantifying the benefits ofvehicle pooling with shareability networks, Proceedings of the National Academy of Sciences of the UnitedStates of America (PNAS), 111(37), September, 2014.
K. Spieser, K. Ballantyne , K. Treleaven, R. Zhang, E. Frazzoli, D. Morton, M. Pavone (2014), Toward asystemic approach to the design and evaluation of automated mobility-on-demand systems: A case studyin Singapore. MIT Open Access Articles (Forthcoming in Road Vehicle Automation, Springer Lecture Notes
in Mobility series), 2014.
J. Zachariah, J. Gao, A. Kornhauser, T. Mufti (2013): “Uncongested mobility for all: A proposal for an areawide autonomous taxi system in New Jersey”, in: Proceedings of Transportation Research Board Annual
Meeting, Washington D.C.

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2. CASE STUDY: THE CITY OF LISBON – 13

2. Case study: The city of Lisbon
In order to provide a frame of reference for our modelling exercise a detailed view of our test city’s existing
mobility patterns is necessary, along with its specific topology and other characteristics. This of course
constrains the direct transferability of the results of the modelling exercise as they are firmly embedded in aunique local context. Nonetheless, insights derived from this exercise can provide an indication of the scaleand direction of the expected impacts of the deployment of the proposed urban mobility system upgrade.
Our simulation exercise was based on data from the municipality of Lisbon, Portugal. Lisbon is the capital
city of Portugal and is the largest city in the country with approximately 565 000 inhabitants in an area of
84.6 km². The city is the centre of the Lisbon Metropolitan Area (LMA), which has approximately 2.8 million
inhabitants, representing roughly 25% of the Portuguese population, with an area of about 3 000 km2
formed by 18 other municipalities.
The Lisbon Metropolitan Area generates over 5 million person-trips each day, of which 55% are commuting
trips to go to work or study. Of this total activity in the LMA, about 1.2 million trips take place within theadministrative boundaries of the Lisbon municipality. It is this set of trips that are examined in our model.
The inhabitants of the Lisbon municipality display a relatively low car ownership rate of 217 cars per
1 000 inhabitants and a low number of daily trips per inhabitant (1.9). Parking space is very scarce in theold heritage areas of the city, which deters car ownership. The low daily trip rate is related to thedemographic profile of the city centre population - in some traditional city boroughs, more than 50% of the
population is aged 65 years or older. Nevertheless, these figures align more or less with other similar
European cities when controlling for wealth measured in GDP per capita.1 Lisbon’s main characteristics withregards to transport infrastructure and public transport services provision are outlined in Table 1.
The Portuguese capital has a well-established underground network that plays in important role in the daily
mobility of inhabitants and workers of the metropolitan area, and especially within the municipality of
Lisbon. The underground system was inaugurated in 1959 and has been growing ever since, currently
covering a significant part of the Lisbon area, with some short links to suburban areas. The Lisbon Metro isa medium-sized network comprised of four lines covering about 43 kilometres of linear distance with52 stations, transporting a total of 176.7 million passengers per year. Additionally, four main commuter raillines connect the greater Lisbon area with the city centre. The relevance of these lines for the present study
is limited, since only 13 stations are located within the city centre.
Recent data indicates that over 60 000 cars, 400 buses and 2 000 taxis circulate simultaneously during
peak traffic periods in Lisbon, resulting in an average density of 60 vehicles per road kilometre (Martínez etal., 2014). This value is relatively high, especially when considering that a significant part of the road
network is comprised of narrow streets.

1

Based on analysis of UITP Millennium Cities Database; Mobility Observatory created by the Development Bank for Latin America.

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14 – 2. CASE STUDY: THE CITY OF LISBON

Table 1. Infrastructure and public transport provision in Lisbon
Infrastructure provision
Road
Road network density (km road/km2)
Public transport

Parking

12.58

Motorway network density (km road/km2)
Underground network density (km line/km2)

0.24
0.51

Underground stations density (stations/km2)
Priority lanes for public transport (km road/km2)

0.65
0.22

Street parking capacity

153 000

Off-street parking capacity
Parking availability (parking space/inhabitant)

50 0000.38

Public transport services provision
Bus (thousands vehicle-km)

329.7

Trains (thousands vehicle-km)
Underground (thousands vehicle-km)

27.0
6 495.7

Source: Câmara Municipal de Lisboa, 2005.

Recent values estimated for the city indicate a maximum of 160 000 cars parked simultaneously, resulting
in a very high utilisation rate of 78% of the available capacity (Câmara Municipal de Lisboa, 2005). Thisparking constraint has constrained car ownership rates in the heart of the city resulting in a more balanced
mode share distribution in the city centre than in the whole of the metropolitan area (Table 2). While thelatest census data reveals that 60% of all trips within the greater Lisbon metropolitan area are undertaken
in a private car, this percentage drops to 40% in the city centre with 20% of trips there taking place by
non-motorised means, principally by walking.

Table 2. Share of transport modes for Lisbon(in %, 2011)

Private car

35.6

LisbonMetropolitan
Area
59.3

Motorcycle
Taxi

2.2
1.6

1.2
0.4

Bus
Walking/cycling

25.1
11.1

13.8
3.1

High-capacity public transport (underground or rail)
Car and high-capacity public transport

19.8
1.1

10.6
4.0

Bus and high-capacity public transport

3.5

7.7

Modes

City ofLisbonURBAN MOBILITY SYSTEM UPGRADE - © OECD/ITF 2015

3. MODEL DESCRIPTION – 15

3. Model description
In this work we developed an agent-based model that simulates the daily operation of a hypotheticalshared mobility system in Lisbon. The model is based on real trip-taking activity, and the simulation takes
place on Lisbon’s real road network. The model is set up in such a way that the fleet of shared and selfdriving vehicles deliver the same trips (in terms of origin, destination and timing) as those in a full-scale
synthetic population generated on the basis of the Lisbon Travel Survey (Câmara Municipal de Lisboa,
2005), generalised to a grid of cells measuring 200 metres by 200 metres. A dispatcher system manages
the centralised task of assigning mobility requests to cars using the location of shared self-driving vehicles,
their current occupancy level and the location of clients as its main inputs. The model estimates trip routing
on the basis of an algorithm that generates the lowest-cost path between any pair of nodes of the network.
Our model addresses the interaction between clients and vehicles, simulating their connection and how, interms of timing and location, the services are performed. It does not include a dynamic traffic model which
would simulate vehicle-level interactions amongst each other and with their environment. Our approach isbased on a static representation of the traffic environment where origin-destination flows are allocated to asimple, topologically correct road network representation that accounts for per-link occupancy (and thus for
speeds), by time of day.

Demand generation
On the basis of the Lisbon Travel Survey, we created a synthetic population of trips within the city,
aggregated into the aforementioned grids. The synthetic travel simulation model we used was developed
and calibrated for the LMA in previous studies, and its output contains all the trip origins and destinations
spatially allocated at the census block level, as well as their timing for a synthetic weekday (Viegas andMartínez, 2010).
Each trip is characterised not only by its time of occurrence, origin and destination, but also by trip purpose
and traveller’s age. For all modes considered – shared vehicles (either ride sharing or car sharing), walking
or cycling, and underground or suburban train – the trip is characterised by access time, waiting time,
travel time and number of transfers between grids, where applicable.
We adopted a rule-based approach in order to specify a simplified and restricted mode choice process. All
short trips under one kilometre were considered to be undertaken by foot or by bicycle (in reality, almost allof these would be taken by foot). Other trips were either assigned to the underground or to the shared andself-driving mode alternative. Trips where both the origin and destination were in proximity to anunderground station, and where the total trip required no more than one transfer within the undergroundnetwork, were assigned to that mode. The remaining trips not well served by underground connections orby walking were then assigned to the shared and self-driving alternative considered for a variety of
scenarios. In those scenarios where we considered that no underground services were available, all trips ofmore than one kilometre in length were assigned to the shared and self-driving mobility option.
When the model chooses the shared and self-driving option, a new user (agent) is generated in thesimulation environment, with a departure node, an arrival node and a starting time. Currently, one user isequivalent to one trip, i.e. users do not cluster in parties at the outset of their trip, though they do sharevehicles once the ride-sharing simulations are underway.

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16 – 3. MODEL DESCRIPTION

Trip generation by users
In the simulation environment, a trip is generated when a user requests a departure from a point towards
another point. The model accounts for the simulation parameters (ride sharing or car sharing) and accounts
for waiting time, detour time and arrival time tolerances that are defined for the model run. The dispatcher
then finds, in real time, the best possible routing and assigns one of several available vehicle types to carryout the trip in either a car-sharing or ride-sharing mode.
The user then waits for the car or accesses a specified pick-up location and boards the vehicle. When thevehicle arrives at its destination, the user exits the system and a set of indicators are generated in a trip log
so that they can be used for ex-post system evaluation.

Figure 1. Flowchart of the agent-based model for trip generation
Send car request
Coordinates origin
Coordinates destination
Car preference

Receive assignment
Car code
Action: Wait or walk to car

Ride the car
Travel time
Travel distanceDrop-off at
destination

Car configurations
Idle cars are located in 60 stations around the city. Whenever a car is empty and not immediately
dispatched to a new trip, it relocates itself to a station (in the TaxiBot ride-sharing system) or parks itself(in the AutoVot car-sharing system). Active cars follow the shortest path and minimise travel time for theirroute assignment, taking into account hourly link-based road speeds.
Our approach assumes a fleet comprised of three types of vehicles, based on available passenger capacity:
two-passenger, five-passenger and eight-passenger cars. We did not specify the type of propulsion
technology - this is of minor relevance for the present study, since emissions or energy use were notmodelled. We did however test the sensitivity of the system to added recharging time and fleet
requirements for battery electric vehicles.

Role of the mobility dispatcher
The mobility dispatcher is an entity that defines a set of rules for matching cars to users, centralising all
real-time information required to produce and monitor these trips. The choice of which car to match with auser request takes into account a time-minimisation principle that applies not just to the requesting user
but also to those already underway.
We defined a number of parametric constraints on the runs that must be satisfied for each route proposedby the mobility dispatcher. These include constraints such as the requirement that the generated trips
commence with no more than a five-minute delay on the standard “base case” trip derived from the original
trip data (Viegas and Martínez, 2010). Other constraints relate to the maximum number of users that canshare a vehicle (eight people maximum) and a maximum allowable increase in time (no more than an
additional 20% travel time compared to the original trip, capped at a maximum of ten minutes) anddistance (maximum of 20% of current travel distance, capped at 2 kilometres) when compared with thedirect connection. This means for instance that in the worst case, a trip that took ten minutes in the basecase would take seven minutes more in the simulation – a five-minute waiting period for the trip tocommence plus an additional two-minute travel time (20% of the original ten minute travel time).

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3. MODEL DESCRIPTION – 17

In reality, simulated travel times for the shared and self-driving scenarios are often lower, especially fortrips formerly taken by bus, and are rarely significantly longer than trips in the base case. Furthermore,travel times in the base-case scenario do not account for time required to access vehicles and time spentsearching for parking for car users. Both would lengthen car travel times for the base case and reduce thegap with travel times in the simulated shared and self-driving system.
The simulation has a dynamic graphical display to visualise the model workflow during the simulation(Figure 2). This component allows inspecting the dynamic variables of each client and car during thesimulation.

Figure 2. Visualisation of shared self-driving car simulation for Lisbon

full vehicle

almost full vehicle

URBAN MOBILITY SYSTEM UPGRADE - © OECD/ITF 2015

half-full vehicle

car with 2 users

car with 1 users

empty car

18 – 4. TESTING SHARED-MOBILITY SCENARIOS

4. Testing shared-mobility scenarios
We devised several scenarios for the deployment of shared and self-driving fleets in Lisbon. These scenarios
were generated by varying four principal parameters in our simulation:


The mode of shared and self-driving operation for the simulated fleet, namely either car sharing or
ride sharing.



The availability of high-capacity public transport or not.



The penetration rate of the shared and self-driving fleet – either 100% or only 50% of all trips.



The time period considered for the simulation – either all day on a weekday or only during peaktravel periods.

We investigated two system configurations for this fleet, both of which consist of fully self-driving vehicles:A ride sharing system, where travellers share time and space resources by travelling in the same
car simultaneously up to the capacity limit of the vehicle. The cars may either be privately owned
by one rider or from a car fleet company. We labelled this a “Taxi Robot” system, or TaxiBot.



A car sharing system, where travellers share time resources by travelling in the same car
sequentially. In this case, car fleets are normally owner by a car fleet manager, although there arealso some incipient peer-to-peer experiments. We labelled this an “Automated Vehicle Robot”
system, or AutoVot.

The decision to simulate the absence of a high-capacity public transport option was motivated by the needto see how well the shared and self-driving option could absorb these trips. In the present case, the highcapacity public transport option was an underground but other high-capacity public transport solutions suchas commuter rail, Bus Rapid Transit (BRT) and Light Rail Transit (LRT) could also be used if they present asimilar level of station density as Lisbon (0.65 stations/km2).
We also modelled scenarios that account for only a 50% penetration rate for the shared and self-driving fleets.
In these scenarios, we randomly assigned 50% of the trips not undertaken by high-capacity public transport
or walking to traditionally operated conventional cars and 50% to the shared and self-driving fleet. All ofthese scenarios were run under the generic waiting time, detour time and distance and vehicle capacity
constraints outlined above.
A single simulation run of a regular day was performed to analyse the mobility outcomes for the different
scenarios. Although these results may present some variability between trials, the stability for the main
extracted indicators is high.

Modal shares in different scenarios
As the modal-choice model is encompassed in a rule-based decision model, the obtained shares for thedifferent modes are very stable for all tested scenarios. The results suggest an increase of high-capacity
public transport use when compared with the current pattern of travel in Lisbon. This deviation results fromcurrent bus users being assigned to the underground, even though they may prefer the former to the latterin reality. The one-kilometre threshold we set for assigning all trips to walking is low, as the real share forwalking in Lisbon is considerably higher than for comparable cities.
In the scenarios where all motorised trips were previously carried out by non-high-capacity public transport,
the TaxiBot/AutoVot fleets reach a share of 70% (if complemented by high-capacity public transport) and
92% (in absence of the latter), as shown in Table 3.

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4. TESTING SHARED-MOBILITY SCENARIOS – 19

Table 3. Mode share distribution for different TaxiBot and AutoVot scenarios(based on Lisbon, 24-hour average, weekday)
Mode share (%)
Transport mode
Baseline
100%
sharedself-driving
fleet

50% private
car use formotorised
trips

PublicTransport

Walkingand cycling

Cars

15

18

48*

Ride sharing(TaxiBot)

No high-capacity public transport

0

8

92

With high-capacity public transport

22

8

70

Car sharing(AutoVot)

No high-capacity public transport

0

8

92

With high-capacity public transport

22

8

70

Ride sharing(TaxiBot)

No high-capacity public transport

0

8

46

With high-capacity public transport

22

8

35

Car sharing(AutoVot)

No high-capacity public transport

0

8

46

With high-capacity public transport

22

8

35

* Baseline assessed from share of private cars

Impact on fleet size
The impacts on fleet size on the different scenarios are set out in Table 4. It shows that shared self-driving
fleets have the potential to drastically reduce the number of vehicles necessary to deliver the same travel
as today’s fleet. Under a ride-sharing TaxiBot configuration supported by high-capacity public transport andmodelled over a 24-hour weekday, 90% of vehicles could be removed from the streets while still delivering
nearly the same level of mobility as before in terms of travel origins, destinations and length of trip.
Even more striking is that only approximately 5 000 shared self-driving TaxiBots would be required tohandle all of the trips currently modelled for the Lisbon Metro in our exercise. A car-sharing AutoVot
operation displays less potential for fleet reduction than a ride-sharing TaxiBot system, since the former
requires more vehicles and much more repositioning travel to deliver the same level of service. If there isno high-capacity public transport option such as an underground system available, the 50% penetration
scenarios for TaxiBots or AutoVots result in more vehicles being required than in the base case. Even in thepresence of an underground, there is only a limited reduction of the number of private cars. This suggeststhat transition scenarios may not lead to expected levels of car fleet reduction in the short term.
Table 4. Fleet size for different TaxiBot and AutoVot scenarios(% of current Lisbon car fleet, 24-hour weekday average)
Fleet
size

100%
sharedself-driving
fleet

50% private
car use formotorised
trips

% of
baseline

Baseline

203 000

Ride sharing(TaxiBot)

No high-capacity public transport

25 917

12.8

With high-capacity public transport

21 12010.4

Car sharing(AutoVot)

No high-capacity public transport

46 24922.8

34 082

16.8

Ride sharing(TaxiBot)

No high-capacity public transport

13 265 + 194 537*

102.4

With high-capacity Public transport

10 900 + 147 767*

78.2

Car sharing(AutoVot)

No high-capacity Public transport

22 887 +194 275*

107.0

With high-capacity Public transport

18 358 +148 050*

82.0

With high-capacity public transport* = shared + private cars

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20 – 4. TESTING SHARED-MOBILITY SCENARIOS

Impact on travel volume
Shared and self-driving fleets hold much promise for reducing the number of cars in our cities. The samecannot be said for reductions in car travel, however. Table 5 looks at the impacts on cumulative travel
volumes under the various shared and self-driving fleet scenarios over the duration of a weekday.
Total distance travelled increases under all of the scenarios we studied. Overall car kilometres travelledover the course of the day rose 6% in the TaxiBot system with high-capacity public transport and nearly
doubled (+89%) in the AutoVot scenario with no public transport.
This increase can be explained by several factors. In all scenarios, we assume that shared self-driving fleetscompletely replace current bus travel in the city. In the base case, average bus occupancy over the courseof the day is low (20%), and it is plausible that these passengers could be better served by a fleet ofshared self-driving cars. The diversion of bus passengers accounts for approximately 30% of the final carkilometres travelled in the ride-sharing scenarios and nearly 50% of the car-kilometres travelled in the carsharing scenarios. The remaining travel increment is due to the repositioning of empty cars in all scenarios,
as well as to detours for passenger pick-ups and drop-offs in the ride-sharing scenarios.
The mixed-fleet scenarios with 50% shared self-driving vehicles and 50% conventional cars result in a verysignificant increase in car travel – up to 90% in the case of an AutoVot system without public transport.
These results reiterate the challenges for operating a shared-mobility system in a hybrid configuration.

Table 5. Weekday travel volumes under different TaxiBot and AutoVot scenarios(for 24-hour weekday)
Car-kilometers(millions)100%
sharedself-driving
fleet

50% private
car use formotorised
trips

% of
baseline

Baseline

3.8

Ride sharing(TaxiBot)

No high-capacity public transport

4.62

122.4

With high-capacity public transport

4.01

106.4

Car sharing(AutoVot)

No high-capacity public transport

7.15

189.4

With high-capacity public transport

5.44

144.3

Ride sharing(TaxiBot)

No high-capacity public transport

6.04

160.2

With high-capacity public transport

4.90

129.8

Car sharing(AutoVot)

No high-capacity public transport

7.20

190.9

With high-capacity public transport

5.69

150.9

Similarly to all-day travel, car-kilometres travelled at peak hours increase compared to the base case in all
scenarios considered (Table 6). For the TaxiBot scenario with high-capacity public transport, this increase isrelatively low (9%). For the AutoVot scenario without high capacity public transport, the increase issignificant (103%). While the former increase remains manageable, the latter would definitely not be.
Figure 3 shows the distribution of car-kilometres travelled over a 24-hour weekday for selected scenarios. It
shows that peak-hour travel measured in car-kilometres increases in the TaxiBot and AutoVot scenarios. It
also shows that the morning peak shifts from a maximum around 8 a.m. in the base case to a maximum
around 9 a.m. in shared self-driving scenarios. The afternoon peak displays a similar but weaker shift.

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4. TESTING SHARED-MOBILITY SCENARIOS – 21

Table 6. Peak-hour travel volumes under different TaxiBot and AutoVot scenarios(weekday morning peak, 7 a.m. to 10 a.m.)
Car-kilometers(millions)100%
sharedself-driving
fleet

50% private
car use formotorised
trips

% of
baseline

Baseline

1.04

Ride sharing(TaxiBot)

No high-capacity public transport

1.30

125.3

With high-capacity public transport

1.13

108.8

Car sharing(AutoVot)

No high-capacity public transport

2.11

203.2

With high-capacity public transport

1.60

154.6

Ride sharing(TaxiBot)

No high-capacity public transport

1.67

167.5

With high-capacity public transport

1.36

135.8

Car sharing(AutoVot)

No high-capacity public transport

2.04

197.0

With high-capacity public transport

1.62

155.7

The increase in peak-hour car-kilometres travelled is neither uniform throughout the city nor on all roadclasses. We investigated how car-kilometres travelled are allocated across the city and among road classes
by time of day. This was calculated for both the base case and some of the TaxiBot and AutoVot scenarios.
The base case assessment we undertook covers the movement of private cars, motorcycles and taxis. It
does not account for bus travel, since the spatial allocation of vehicle-kilometres was not readily available.
This may have an impact, since bus travel represents 13% of total vehicle-kilometres travelled in Lisbon.

Thousands

Vehicle kilometres in the city by 5 minutes
periods

Figure 3. Time distribution of travel volumes for selected TaxiBot and AutoVot scenarios(for 24-hour weekday)
70
60
50

40
30
20
10
0
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Hours of the day
Base case

TaxiBots with high-capacity PT

URBAN MOBILITY SYSTEM UPGRADE - © OECD/ITF 2015

AutoVots without high-capacity PT

21

22

23

24

22 – 4. TESTING SHARED-MOBILITY SCENARIOS

Figure 4. Time distribution of travel volumes by road class for selected TaxiBot and AutoVot scenarios
300000300000
Trunk roads

Motorways
250000Base case

250000

TaxiBot ridesharing with high capacity PT
200000

AutoVot carsharing without high capacity PT

200000150000150000100000100000

50000

50000

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

3000000300000Local traffic distributors roads

Local road network

250000250000200000200000150000150000100000100000

50000

50000

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

Figure 4 compares the distribution of car-kilometres travelled over the course of a typical weekday forvarious scenarios by road class. It shows that the modest rise in travel volume in the TaxiBot scenario withhigh-capacity public transport does not contribute significantly to peak hour travel on all but the local road
network. The increase in activity on local road networks is due to pick-up and drop-off movements, which
seem more frequent during the morning peak. The figure also shows that there is some peak travel
spreading on all road networks, which is a result of vehicle relocation to stations after peak demand
periods. This is a relevant result from an operational perspective, as it implies that strategies will be needed
to handle relocation following periods of intense use in anticipation of the next peak. In comparison to the
TaxiBot scenario, the AutoVot scenario without high-capacity public transport shows an increase in travel
volumes as well as a combined spreading and shift of the peak period on all road networks.
The fact that both the TaxiBot and AutoVot scenarios lead to an increase in travel on local arterials and the
local road network may imply changes in the performance and characteristics of those networks. In termsof performance, current road occupancy at peak hours (in terms of percentage of technically feasible roadcapacity used) is generally below 40%, and less than half of that for the local road network (Table 7). In
the TaxiBot plus high-capacity public transport scenario, peak-hour road occupancy barely changes for all
but local roads, and even for the latter the increase is relatively modest at 23%.
This is not the case for the AutoVot scenario without high-capacity public transport. In this scenario, roadoccupancy increases by 40 to 50% for all road classes, with the strongest growth occurring on local road
networks. This implies poorer performance and possibly congestion. It may also mean a change in the

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4. TESTING SHARED-MOBILITY SCENARIOS – 23

nature of local street traffic resulting from the presence of additional traffic on what were otherwise quieter,
less-used roads. Such an increase in traffic could have a negative impact on the attractiveness and livabilityof local streets, as it reduces their availability for non-transport use.

Table 7. Road occupancy by road class during morning peak for selected TaxiBot and AutoVot scenarios(8 a.m. weekday, in % of technical capacity used)
Base case

TaxiBots withhigh-capacity
public transport

AutoVots withouthigh-capacity
public transport

Highways

35.8

36.9

58.6

Trunk roads

42.5

44.5

68.9

Local traffic distributor roads

34.3

38.7

60.3

Local road network

16.7

21.9

36.0

Road hierarchy

Figure 5 depicts the spatial distribution of trips during the morning peak for the TaxiBot plus high-capacity
public transport scenario in comparison to the base case. It shows that some roads and certain areas of thecity may even see a drop in traffic during peak hours in the TaxiBot scenario, while others will experience aslight increase. The visualisation reinforces our two main findings regarding the traffic impacts of theTaxiBot scenario: It shows a marginal negative impact in congestion at certain bottlenecks, but also that
overall traffic fluidity is largely preserved. It equally reveals the increase in traffic on local networks wheretraffic was largely absent previously, and thus the potential increase of traffic conflicts with walking andcycling in these areas. It should also be noted that the areas registering the largest increases in traffic
volumes are those least well connected to high-capacity public transport. This underscores thecomplementarity of these two systems in our scenarios.

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24 – 4. TESTING SHARED-MOBILITY SCENARIOS

Figure 5. Spatial distribution of the variation of peak hour travel volumes for TaxiBot system in Lisbon(weekday 8-9 a.m., TaxiBot plus high-capacity public transport scenario, vehicle-kilometres)

Vehicle fleet requirements at peak hours
In order to assess how the TaxiBot and AutoVot fleets performed at peak travel periods, we carried out anaggregate assessment of the number of vehicles circulating in the city during the morning peak. This part of
the analysis does not factor in capacity limitations at link levels, but allows us to gauge peak-hour fleet
requirements and travel volumes, and to compare these with the baseline (see Table 8).
Even at peak hours, the car reduction effect of shared self-driving fleets is important. We found thatsignificantly fewer cars than today would be travelling at peak hours, while service levels are largely
maintained compared to the base case. In the TaxiBot plus public transport scenario, 65% fewer cars are
circulating at peak hours. Even in the least car-reducing scenario – AutoVots without public transport –23% fewer cars were required.
Testing of the mixed-fleet scenarios indicates that ride-sharing systems may lead to reductions in peakloads when combined with high-capacity public transport. Under these scenarios, increase of relocation
operations of car-sharing systems may produce a significant increase of traffic that may be absorbed withdifficulty in already very congested urban areas.

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4. TESTING SHARED-MOBILITY SCENARIOS – 25

Table 8. Number of cars travelling during morning peak in selected TaxiBot and AutoVot scenarios(weekday morning, Lisbon)
Cars travellingat peak hours

100%
sharedself-driving
fleet

50% private
car use formotorised
trips% of current
max. flow

Baseline

60 000

Ride sharing(TaxiBot)

No high-capacity public transport

25 867

43.1

With high-capacity public transport

21 10535.2

Car sharing(AutoVot)

No high-capacity public transport

46 01176.7

33 975

56.6

Ride sharing(TaxiBot)

No high-capacity public transport

13 173 + 57 499*

117.8

With high-capacity public transport

10 890 + 43 675*

90.9

Car sharing(AutoVot)

No high-capacity public transport

22 768 + 57 421*

133.6

With high-capacity public transport

18 305 + 43 759*

103.4

With high-capacity public transport* = shared + private cars

These results suggest that shared self-driving fleets may face some transition issues due to the presence oflegacy fleets. Unless increases in travel at peak hours are managed, it may be difficult to make a public
policy case for self-driving fleets based solely on freed space and congestion benefits in the presence oflegacy conventional cars. Nonetheless, even in these scenarios, these fleets could potentially represent a
cost-effective alternative to public transport if the impacts of additional travel are mitigated.
Another relevant output of the scenarios we tested is the peak travelling ratio, assessed as the ratio
between the number of cars travelling at 8 a.m. and at 3 p.m. (Table 9). The values for this ratio indicatethat car sharing under an AutoVot system leads to a greater number of cars on the streets at peak hours
compared to ride sharing with a TaxiBot system. The peak travelling ratio is exacerbated when considering
mixed-fleet scenarios, suggesting greater concentration of vehicles during peak periods and consequently
greater congestion levels for respective peak travel periods.

Table 9. Ratio of cars travelling in-peak and off-peak in selected TaxiBot and AutoVot scenarios(Weekdays, 8 a.m. to 3 p.m.)
Peak/Off-peakfactor100%
sharedself-driving
fleet

50% private
car use formotorised
trips

Baseline

3.30

Ride sharing(TaxiBot)

No high-capacity public transport

2.65

With high-capacity public transport

2.69

Cars haring(AutoVot)

No high-capacity public transport

3.01

With high-capacity public transport

3.17

Ride sharing(TaxiBot)

No high-capacity public transport

2.56

With high-capacity public transport

2.53

Car sharing(AutoVot)

No high-capacity public transport

3.04

With high-capacity public transport

3.16

Impact on parking and street space
Vehicles use a considerable amount of space in cities, both when moving and when parked. Accordingly, we
examined the impacts that shared self-driving vehicles would have on parking space requirements. Table 10

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26 – 4. TESTING SHARED-MOBILITY SCENARIOS

presents the maximum parking requirements implied by each scenario. It shows that there is an extremely
large potential for reducing both on-street and off-street parking spaces, and that this is the case for all
shared and self-driving fleet scenarios.
Considering that off-street parking represents 50 000 spots in the baseline case and that the most parkingintensive scenario (car sharing without public transport) would require 25 621 spots, on-street parking
spots could be totally removed from the streets, whatever the scenario considered. This would allow thereallocation of 1 530 000 m² to other public uses2, equivalent to almost 20% of the kerb-to-kerb street
area in Lisbon or 210 football fields. This freed-up space could be dedicated to non-motorised transportmodes, delivery bays, parklets or other recreational and commercial uses.
In the most favourable scenario for parking, a maximum number of 8 901 parking spots would benecessary. Taking 10 000 as a proxy of the total number of required parking spaces, 40 000 off-street
parking spaces could then be re-allocated, i.e. 1 200 000 m² or the equivalent of almost 170 football fields.
This space could be used in innovative ways, e.g. as centrally located urban logistics distribution centres.

Table 10. Maximum number of parked vehicles for different TaxiBot and AutoVot scenarios(for 24-hour weekday)
Max. Parking
requirements

100%
sharedself-driving
fleet

50% private
car use formotorised
trips

Ride sharing(TaxiBot)
Car sharing
Ride sharing(TaxiBot)
Car sharing

Baseline

160 000

No high-capacity public transport

11 563% ofbaseline

7.2

With high-capacity public transport

8 901

5.6

No high-capacity public transport

25 621

16.0

With high-capacity public transport

17 11010.7

No high-capacity public transport

5 928 + 153 122*

99.4

With high-capacity public transport

4 622 + 116 689*

75.8

No high-capacity public transport

12 705 + 153 330*

103.8

With high-capacity public transport

9 561 + 116 467*

78.8

* = shared + private cars

However, when testing the scenarios with mixed fleets of TaxiBots/AutoVots and traditional, privatelyowned cars, the results are less promising. The reductions in parking space requirements are small in the
TaxiBot system (up to 25%) and even negative in the AutoVot system. These findings suggest that sharedand self-driving fleets operating in parallel with private conventional car fleets may lead to even higher
parking requirements than today in the absence of bus services.

Impact on vehicle use
Another relevant aspect of shared and self-driving systems is their potential impact on more efficient
vehicle operation – and in particular their potential to reduce unproductive down-time for vehicles.
Currently, cars in the city of Lisbon are used for approximately 50 minutes per day, which means that theylie unused for 95% of the day.

2

Assuming 10m² for each on-street parking space and 30m² for each off-street parking space (including lanes, ramps, etc.).

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4. TESTING SHARED-MOBILITY SCENARIOS – 27

Table 11 displays how much time vehicles are not being used productively.3 In all scenarios we see adecrease in the amount of unproductive idle time for vehicles. Unproductive vehicle downtime drops from
95% to less than 40% in all scenarios, and down to 27% in some scenarios.

Table 11. Shares of idle time for different TaxiBot and AutoVot scenarios(cars parked, driving to depot or to initial client; for 24-hour weekday)
Idle time(in %)
96

Baseline
100%
sharedself-driving
fleet

50% private
car use inmotorised
trips

Ride sharing(TaxiBot)

No high-capacity public transport

27.1

With high-capacity public transport

27.2

Car sharing(AutoVot)

No high-capacity public transport

39.3

With high-capacity public transport

35.2

Ride sharing(TaxiBot)

No high-capacity public transport

22.8

With high-capacity public transport

23.6

Car sharing(AutoVot)

No high-capacity public transport

39.4

With high-capacity public transport

38.6

Distribution of vehicle types
One of the outcomes that we tracked was the distribution of vehicle types used to carry out the shared andself-driving trips (see Table 12). The results suggest that the shared self-driving fleet composition will bedominated by 3-5 passenger vehicles followed by small vehicles carrying one to two passengers.

Table 12. Share of travel by vehicle type for different TaxiBot and AutoVot scenarios(24 hours, weekdays)
Share of total car-kilometres (%) for:100%
sharedself-driving
fleet

50% private
car use inmotorised
trips

Cars for 1-2
passengers

Cars for 3-5
passenger

Cars for 5-8
passengers

Ride sharing

No high-capacity public transport

34.4

60.3

5.3

(TaxiBot)

With high-capacity public transport

35.2

58.7

6.1

Car sharing(AutoVot)

No high-capacity public transport

100

0.0

0.0

With high-capacity public transport

100

0.0

0.0

Ride sharing(TaxiBot)

No high-capacity public transport

31.8

48.5

19.7

With high-capacity public transport

33.6

47.1

19.3

Car sharing(AutoVot)

No high-capacity public transport

100

0.0

0.0

With high-capacity public transport

100

0.0

0.0

Figures for car sales in Lisbon in 2009 (Figure 6) indicate that many city dwellers own small cars. Thissuggests that the expected makes and models present in the Portuguese market, in terms of vehicle size,
would not change drastically under shared and self-driving scenarios.

3

In the sense that they are not providing work, e.g. that they are parked, are driving to a depot or to an initial client. We do notconsider an empty vehicle repositioning itself to pick up a client as unproductive or idle.

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28 – 4. TESTING SHARED-MOBILITY SCENARIOS

Figure 6. Sales of new cars in Lisbon by car type(2009)
45.00%
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
City

Utility

FamilySUV

Source: Portuguese Car Trade Association (ACAP), 2009.

Impact of a fully electric vehicle fleet
We noted earlier that shared and self-driving fleets would likely lead to an increase in overall volumes oftravel. Emissions of greenhouse gases and pollutants generated by this travel could be mitigated throughthe deployment of low-emission and energy-efficient drivetrain technologies. In the extreme case, fully
electric fleets would eliminate tank-to-wheel emissions from all of the car travel in our scenarios. Withcurrent technologies, however, full battery-electric vehicles have limited range compared to conventional
fossil-fuelled cars and would require additional off-line time for recharging.
We modelled additional vehicle requirements required by shared self-driving fleets consisting of electricvehicles to gauge the impact of re-charging times and reduced travel range. We assumed a fast battery
recharging time of 30 minutes and vehicle autonomy of 175 kilometres. We found that the impact on fleet
size of the deployment of a shared self-driving fleet of fully electric vehicles was minimal (+2%).

Changes in waiting and travel times
We assessed the average waiting and travel time resulting from shared mobility services via TaxiBots and
AutoVots, and compared these with the baseline scenario for public transport and private car use. We foundthat this resulted in a significant reduction in average waiting and travel times (Table 13). These reductions
are the result of the more personalised door-to-door services offered by TaxiBots and AutoVots, notably for
those trips previously taken by bus, and improved travel times, especially in peak periods.
Relaxing our constraint on maximum waiting time to ten minutes (from five minutes in the initial model)
leads to an 11% reduction of the required car fleet. Travel volume, measured in car-kilometres, would dropby a mere 3% in the best scenarios, since part of the additional time available would be used to pick-up
and drop-off passengers in further-away locations. However, relaxing the waiting-time constraint would also
result in a 27% increase of average party size in ride-sharing systems. It is therefore conceivable that such

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4. TESTING SHARED-MOBILITY SCENARIOS – 29

a configuration would lead to a price reduction for clients if the service price for each user were linked to
the number of riders sharing a vehicle.

Table 13. Average waiting and travel time for different TaxiBot and AutoVot scenarios(variation to current public transport waiting time and private car travelling time in Lisbon)
Urban density
Lisbon density (x1) (7 400 inhab./km2)
Waiting time
(min)

100%
sharedself-driving
fleet
50%
privatecar use formotorised
trips

Change(%)

Travel time(min)

Change(%)

Baseline

26.37

No high-capacity public transport

3.73

-85.9

15.11

-17.4

With high-capacity public transport

3.79

-85.6

15.93

-13.0

Car sharing

No high-capacity public transport

3.60

-86.4

11.36

-37.9

(AutoVot)

With high-capacity public transport

3.03

-88.5

11.43

-37.5

Ride sharing

No high-capacity public transport

3.85

-85.4

18.08

-1.2

(TaxiBot)

With high-capacity public transport

4.05

-84.6

19.63

7.3

Car sharing(AutoVot)

No high-capacity public transport

3.31

-87.5

11.46

-37.4

With high-capacity public transport

3.38

-87.2

12.61

-31.1

Ride sharing(TaxiBot)

18.30

The statistical distribution of waiting times across all clients obtained from the dispatching algorithm for a
maximum waiting time of five minutes is presented in Figure 7. It shows that the most frequent waitingtime is 3.3 minutes and the average 3.7 minutes for the TaxiBot scenario with high-capacity public
transport. The shape of the distribution reveals two different branches of the dispatching algorithm: one
branch associated with the assignment of initial passengers to an empty TaxiBot with a peak at
3.3 minutes, and another branch associated with the optimal allocation of a partially occupied vehicle to aclient with a peak at 4.2 minutes.

Figure 7. Distribution of waiting times for TaxiBot scenario with high-capacity public transport(curve adjusted and smoothed)

URBAN MOBILITY SYSTEM UPGRADE - © OECD/ITF 2015

30 – 4. TESTING SHARED-MOBILITY SCENARIOSIn the base case, door-to-door travel times for bus and underground users include time to access the stopor station as well as some waiting time to reflect schedules. For car travel, no waiting or access time wasassumed, even though parking may not be available in proximity of the point of departure or arrival. Searchtime for parking spaces was also not included. In this respect, our base case underestimates car travel
times. Because of these assumptions, it is reasonable to expect that users of public transport will see an
improvement in their door-to-door travel time in TaxiBot/AutoVot scenarios, while some car drivers may
experience an increase in door-to-door travel times. The overall impacts on travel times would therefore be
dependent on the share of car drivers relative to public transport users.

Figure 8. Distribution of daily travel time under different TaxiBot and AutoVot scenarios(in periods of five minute over the course of a weekday)

Mobility time budget per 5 minutes periods(person.hour)

6000

5000

4000

3000

2000

1000

0
0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

Hour of the day
Base case

TaxiBots with high-capacity PT

AutoVots without high-capacity PT

Figure 8 shows the overall travel time budget (i.e. the amount of time spent travelling), logged for periodsof five minutes over the course of a weekday. It shows that both ride-sharing systems with TaxiBots andcar-sharing systems with AutoVots reduce the amount of travel time compared to the base case, with the
exception of peak hour AutoVot trips. For TaxiBots complemented with high-capacity public transport, thetotal daily travel time required to deliver all trips is reduced by 30%. For AutoVots without high-capacity
public transport, this reduction is lower (-18%). Although both TaxiBots and AutoVots save time on
average, this is not necessarily true for all travellers and all times. Peak-hour AutoVot travel, for instance,
will take more time than in the base case. For car drivers, however, switching to an AutoVot (without highcapacity public transport available) reduces the total door-to-door travel time by an average of 2%,
assuming they spent three minutes per ride searching for parking space with their own cars. Car drivers
who switch to ride sharing with TaxiBots with high-capacity public transport, on the other hand, would
increase total door-to-door travel times on average by 8%.

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4. TESTING SHARED-MOBILITY SCENARIOS – 31

Distribution of detour distance and time values
As noted earlier, ride sharing in the TaxiBot system implies acceptance of slight deviations from theshortest travel path for each passenger. The statistical distributions of the distance and time required forthese detours are presented in Figure 9. The curve shows that the delays introduced by the detours are
small, with less variability for the time detour. This suggests that additional travel time may not be aconcern for ride-sharers in TaxiBots even during peak hours.

Figure 9. Statistical distribution of detour for the TaxiBot scenario with high-capacity public transport(Top=distance, bottom=time; probability density function)

Occupancy levels and efficiency of matching
We investigated the efficiency of matching users for shared rides, measured by average number ofoccupants, and how demand intensity and its patterns influence performance of TaxiBot ride sharing.
Analysis of the average party size occupying TaxiBots throughout the day (Figure 10) reveals that peaks indemand in the morning and early evening enhance the ability to form larger ride-sharing pools. Even inperiods of low average demand such as late evening or early morning, concentrated demand in popular
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32 – 4. TESTING SHARED-MOBILITY SCENARIOS

geographic areas - neighbourhoods with a high number of bars and clubs, for instance - still produce
satisfactory average occupancy. Significantly, the concentration of boarding at origin, which prevents large
waiting times (greater than five minutes in the tested scenarios), proved to be a more decisive component
to ensure high occupancy than the density of alighting locations.
The overall daily elasticity of party size with respect to the aggregate city demand was 1.07. This meansthat by each 1% increase in overall demand the average party size is expected to increase 1.07%,
indicating the elastic behaviour of this relation.

2.9

12000

2.7

10000

2.5

8000

2.3
6000
2.1
4000

1.9

2000

1.7
1.5

0
0

1

2

3

4

5

6

7

8

9

Ridesharing demand (5 minutes intervals)

Average party size

Figure 10. Matching efficiency for shared rides in TaxiBot scenario with high-capacity public transport

10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hour of the day

Average party size

Ridesharing demandThis study did not look at the costs of shared self-driving urban mobility, nor did it assess the costs totravellers of the base case trips. As such, the model is agnostic with respect to overall welfare impacts ofthe scenarios examined. In reality, travel demand is of course linked to costs, and further work could
examine the extent to which savings in travel time and other potential reductions of costs for consumers
might translate into additional travel demand. Part of that analysis could be the extent to which added
demand might erode some of the potential benefits of the deployment of shared self-driving urban mobility
systems. Our discussion also did not touch upon labour issues, which are likely to be significant, nor uponquestions of equity that could be raised by such systems and that will have to be examined in further work.

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5. POLICY INSIGHTS – 33

5. Policy insights
Transport policies can influence the type and size of the car fleet, the mix between public transport andshared vehicles and, ultimately, the amount of car travel, congestion and emissions in a city. For small andmedium-sized cities, it is conceivable that a shared fleet of self-driving vehicles could completely obviate
the need for traditional public transport.
Shared vehicle fleets free up a significant amount of space both on and off-street. However, priorexperience indicates that this space must be pro-actively managed in order to lock in benefits. Management
strategies could include restricting access to this space by allocating it to commercial or recreational uses,
delivery bays, bicycle tracks or enlarging sidewalks. Freed-up space in off-street parking could be used forlogistics distribution centres.
Despite increases in overall levels of car travel, the deployment of large-scale self-driving vehicle fleets will
likely reduce crashes and crash severity. Environmental impacts are tied to per-kilometre emissions andthus will be dependent on the degree to which shared self-driving car fleets employ more fuel-efficient andless polluting technologies.
The deployment of shared self-driving car fleets in an urban context will directly compete with the way inwhich taxi and public transport services are currently organised. These fleets might effectively become a
new form of low capacity, high quality public transport. Labour issues will likely be significant, but there is
no reason why public transport operators or taxi companies could not take an active role in delivering these
services. This could alleviate these issues to a certain extent. Governance of transport services, including
concession rules and arrangements, will need to adapt.
The drastic reduction in the number of cars resulting from a shift to shared self-driving cars will significantly
impact car manufacturers' business models. New services will develop under these conditions, but it isunclear who will manage them and how they will be monetised. The role of authorities, both regulatory andfiscal, will be important in guiding developments or potentially maintaining market barriers.
Under all of our scenarios, vehicles are used much more intensely than before. From currently 50 minutes
and 30 kilometres, daily use would increase to 12 hours and nearly 200 kilometres. This increase in use will
require different car models than are currently on the market today. It will also mean shorter lifecycles andthus a quicker adoption of new, cleaner technologies. Shared use will also require different and much morerobust interior fittings, although weight savings could potentially accompany a reduction of crash risk.
Innovative maintenance programmes could be part of the monetisation package developed for these
services.
Overall, vehicle travel will be higher in all fleet-mixing scenarios and vehicle numbers will increase in 3 outof 4 of our peak hour scenarios. It is likely that improved traffic flow could mitigate congestion up to apoint. In the most extreme scenarios, however, it may be difficult to make a public policy case for selfdriving fleets alone (without high-capacity public transport) based solely on space and congestion benefits.
Nonetheless, even in mixing scenarios, these fleets could represent a cost-effective alternative to traditional
forms of public transport if the impacts of additional travel are mitigated. “All in” deployment of shared selfdriving fleets may be easier in circumscribed areas such as business parks, campuses, islands, as well as incities with low motorisation rates.

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34 – BIBLIOGRAPHY

Bibliography
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Columbia University, 2013.
Câmara Municipal de Lisboa (2005) Lisboa: O desafio da mobilidade. Câmara Municipal de Lisboa Licenciamento Urbanístico e Planeamento Urbano.
D. J. Fagnant, K. M. Kockelman (2014), The travel and environmental implications of shared autonomousvehicles, using agent-based model scenarios , Transportation Research Part C: Emerging Technologies, 40,
March 2014.
L. M. Martinez, G. H. A. Correia, J. M. Viegas (2015), “An agent-based simulation model to assess theimpacts of introducing a shared-taxi system: an application to Lisbon (Portugal)”, Journal of Advanced
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P. Santi, G. Resta, M. Szell, S. Sobolevsky, S.H. Strogatz, and C. Ratti (2014), Quantifying the benefits ofvehicle pooling with shareability networks, Proceedings of the National Academy of Sciences of the UnitedStates of America (PNAS), 111(37), September, 2014.
K. Spieser, K. Ballantyne , K. Treleaven, R. Zhang, E. Frazzoli, D. Morton, M. Pavone (2014), Toward asystemic approach to the design and evaluation of automated mobility-on-demand systems: A case study in
Singapore. MIT Open Access Articles (Forthcoming in Road Vehicle Automation, Springer Lecture Notes in
Mobility series), 2014.
J.M. Viegas, L.M. Martínez (2010): “Generating the universe of urban trips from a mobility survey sample
with minimum recourse to behavioural assumptions”, in: Proceedings of the 12th World Conference on
Transport Research, Lisbon.
J. Zachariah, J. Gao, A. Kornhauser, T. Mufti (2013): “Uncongested mobility for all: A proposal for an areawide autonomous taxi system in New Jersey”, in: Proceedings of Transportation Research Board Annual
Meeting, Washington D.C.

URBAN MOBILITY SYSTEM UPGRADE - © OECD/ITF 2015

CPB

Corporate Partnership
Board

Urban Mobility System Upgrade

How shared self-driving cars could change city traffic
What if all trips in a city were carried out by a ﬂeet of self-drivingcars shared by users? This study explores the potential outcomes ofsuch a radical upgrade in an urban mobility system. It concludes thatup to 9 out of 10 conventional cars could become redundant undercertain circumstances. Vast amounts of public space would be freed
for other uses in such a scenario. However, the total volume of travelincreases in most scenarios and the net beneﬁt of such an urban
mobility system upgrade decisively depends on the choice of vehicle
type, the level of penetration and the availability of high-capacity
public transport to complement the shared self-driving car ﬂeet.
The work for this report was carried out in the context of a project
initiated and funded by the International Transport Forum’s Corporate
Partnership Board (CPB). CPB projects are designed to enrich policy
discussion with a business perspective. Led by the ITF, work is carriedout in a collaborative fashion in working groups consisting of CPB
member companies, external experts and ITF researchers.

Urban Mobility System
UpgradeHow shared self-driving cars
could change city traffic

Corporate Partnership Board
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75775 Paris Cedex 16
FranceT +33 (0)1 45 24 97 10
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