CPB

Corporate Partnership
BoardAbout the International Transport ForumThe International Transport Forum at the OECD is an intergovernmental organisation with 57 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. It is administratively integrated with
the OECD, yet politically autonomous.
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. ITF’s Annual Summit is the world’s largest gathering oftransport ministers and the leading global platform for dialogue on transport policy.
Our member countries are: Albania, Argentina, Armenia, Australia, Austria, Azerbaijan, Belarus, Belgium,
Bosnia and Herzegovina, 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, Israel, Italy, Japan, Korea, Latvia, Liechtenstein, Lithuania, Luxembourg,
Malta, Mexico, Republic of Moldova, Montenegro, Morocco, Netherlands, New Zealand, Norway, Poland,
Portugal, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Spain, Sweden, Switzerland,
Turkey, Ukraine, United Kingdom and United States.

Disclaimer
Funding for this work has been provided by the ITF Corporate Partnership Board. This report is published under the responsibilityof the Secretary-General of the ITF. It has not been subject to the scrutiny of ITF or OECD member countries, and does notnecessarily reflect their official views or those of the members of the Corporate Partnership Board.

www.itf-oecd.org

Foreword
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 in a collaborative fashion in working groups consisting of CPB member companies, external
experts and ITF staff.
Many thanks to the members of the Corporate Partnership Board companies involved in this work: Brisa,
Ford, Google, INRIX, PTV Group, SNCF, Transdev, Toyota, Uber.
The principal authors of this report were José Viegas and Luis Martinez of the International Transport
Forum. Many thanks to Sharon Masterson and Philippe Crist for comments and edits, to Liv Gudmundsonfor copy-editing, and to Olga Petrik and Dimitrios Papaioannou for their contributions on graphical content.
The project was co-ordinated by Sharon Masterson and Philippe Crist of the International Transport Forum.

TABLE OF CONTENTS – 5

Table of contents
Executive summary....................................................................................... 7
1. Introduction ............................................................................................ 10
2. Scaling up: Evolving from city to regional level ............................................ 12
Description of the modelling framework ....................................................................... 12
Scaling up to the greater Lisbon Metropolitan Area ....................................................... 14
Overall modelling results of shared mobility for the Lisbon Metropolitan Area............... 23
Accessibility levels to jobs in the Lisbon Metropolitan Area ........................................... 24
Performance measures for shared mobility solutions
for the Lisbon Metropolitan Area ................................................................................... 27

3. How to deploy shared mobility services? Lessons from transition and market
structure scenarios ...................................................................................... 37
Targeted early adopter pathways ................................................................................. 37
Alternative policy scenario: Access restrictions of private cars to the city of Lisbon ...... 41
Market structure scenarios ........................................................................................... 42

4. Can shared mobility serve people with special mobility needs? ...................... 46
On costs and their impact on breakeven prices ............................................................. 50

5. Conclusions ............................................................................................. 51
Bibliography ................................................................................................ 53
Notes ......................................................................................................... 53

Tables1.

Specifications adopted for the shared mobility services ............................................................... 12

2.

Comparison of Lisbon City and Lisbon Metropolitan Area ............................................................. 15

3.

Transport mode share (Passengers) ......................................................................................... 17

4.

Mode share by operation area compiled from corridor-based analysis: Reference case (2011) .......... 17

5.

Corridor names, typologies and respective share of total number of trips in the LMA ....................... 19

6.

Trips listed by order of consideration for modal transfer .............................................................. 22

7.

Analysis of mode diversion (whole LMA) – Passengers/day .......................................................... 23

8.

Overall impacts of full implementation of shared mobility at the scale of the LMA ........................... 24

9.

Quantitative distribution of access to jobs ................................................................................. 25

10. Percentage of LMA jobs in the 10th and 90th percentiles of grid cells for the reference
and the shared mobility scenarios ........................................................................................... 26

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6 –TABLE OF CONTENTS

11. Distribution of waiting times for Shared Taxis and Taxi-Buses (by corridor type) ............................ 28
12. Daily passenger boardings at the stations of high capacity public transport modes .......................... 34
13. Targeted early adopter scenario: Baseline adoption scenario ....................................................... 38
14. Targeted early adopter scenario: Double baseline adoption scenario ............................................. 39
15. Targeted early adopter scenario: Triple baseline adoption scenario ............................................... 39
16. Synthetic indicators for gradual adoption of shared mobility ........................................................ 40
17. Performance indicators for the scenarios of action only by the Lisbon municipality .......................... 42
18. Performance indicators for a scenario with three dispatchers ....................................................... 44
19. Performance indicators for a scenario with a single dispatcher and the possibility of exclusive
occupancy in Shared Taxi services ........................................................................................... 45
20. Characterisation of tested scenarios for accessible Shared Taxi services ....................................... 49

Figures1.

Extent of Lisbon and Lisbon Metropolitan Area ........................................................................... 15

2.

Population density for Lisbon Metropolitan Area ......................................................................... 16

3.

Job density for Lisbon Metropolitan Area ................................................................................... 16

4.

Functional areas and operational corridors used for the greater LMA analysis ................................. 19

5.

Rail, ferry and metro lines and stations used at the feeder service specification .............................. 20

6.

S-curve representing the attraction decay for accessibility calculations (using public transport) ....... 25

7.

Ratios between the numbers of effectively accessible jobs with shared mobility solutions
and in the reference case ...................................................................................................... 27

8.

Number of vehicles (Shared Taxi) in operation .......................................................................... 29

9.

Number of vehicles (Taxi-Bus of capacity 8 and 16 pax) in operation ............................................ 29

10. Average occupation per vehicle type ....................................................................................... 31
11. Passengers upgraded from Taxi-Bus to Shared Taxi ................................................................... 32
12. Modal shares throughout the day (in PKM) – Reference case ....................................................... 33
13. Modal shares throughout the day (in PKM) – Shared mobility ...................................................... 33
14. How feeder services affect total boardings at different stations along a suburban railway line ........... 35
15. City of Lisbon cordon and park share locations .......................................................................... 42

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EXECUTIVE SUMMARY – 7

Executive summary
What we did
This report examines how cities can manage the challenges of geographical scale and of transition to sharedmobility services. It expands on two earlier studies that looked at the impact of replacing private cars in acity with shared services, which did not address the questions of implementation and of expanding these
services to a wider metropolitan area. Based again on mobility data for the city of Lisbon, Portugal, thisreport assesses issues around the scaling up of shared mobility services to the whole of the Metropolitan
area and of their stepwise introduction. It also explores how shared mobility can improve accessibility forusers with impairments and analyses the impacts of these services on the use of existing high-capacity
public transport and on access to jobs across the whole study area.
For this study, different configurations of shared mobility solutions were simulated using advanced
computer models that were fed with detailed mobility data for the Lisbon Metropolitan Area (LMA). Specificquestions examined were: How do the impacts of shared mobility services on the core city and the wider
metropolitan area compare? How could a phased deployment of shared mobility services be organised andwhat results could be expected for each phase? How would different approaches to organising the
dispatcher role influence outcomes? How would a shared mobility system provide equitable access for all inan efficient way and deliver high-quality access also to citizens with disabilities?

What we foundThis study found that the reduction of traffic volumes, emissions and also prices as the result of a full-scale
implementation of shared mobility in this metropolitan area is even more significant than for the core city
itself. For example, total vehicle.kilometres in peak hours are reduced by 55% (compared to 2011) for themetropolitan area, while the reduction for the city alone was 44%. CO2 emissions are reduced by 62% for
the wider agglomeration and 53% for the city.
In wider metropolitan areas, new types of shared services such as Shared Taxis or Taxi-Buses can serve as
feeders to existing high-capacity public transport networks such as metro or rail lines. This facilitates theintroduction of shared services and increases the use of those high-capacity services and the overall
positive impacts in terms of congestion reduction, more equitable access and gains in public space.
Shared mobility makes access to jobs and other public services easier and more equitable. This was alreadydemonstrated for the core city in earlier analyses; this study now finds this is also the case for the larger
conurbation. While levels of accessibility differ from area to area, major improvements in access and strong
reductions of inequity resulted from the introduction of shared mobility across all of them.
Shared mobility also releases massive amounts of parking space. This study finds that the effect on parking
spaces is roughly the same for the city centre and the LMA, with total parking space needed reduced by
95%. The remaining 5% would be primarily used as depots for the Shared Taxis and thus need to be
strategically positioned, but the remaining 95% could be reallocated for other public uses.
Regarding implementation, the examination of a phased pathway (starting with potentially uncontroversial
measures targeting early adopters) helped to identify a minimum initial push needed to quickly achieve
publicly visible results and high levels of satisfaction by the users of the shared services, as a basis for the
next implementation phase aiming to achieve the full benefits of shared mobility.

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8 – EXECUTIVE SUMMARY

An alternative scenario with differentiated implementation of measures only in the core city but not thesurrounding municipalities showed that in the core city implementation of the measures considered for the
first phase of the deployment pathway can deliver positive results for the core city when accompanied by ascheme of partial access constraint for private cars of non-residents.
The modelling results also showed that the way dispatch services for shared vehicles are organised strongly
affects the overall efficiency of the scheme.
With regard to shared mobility as a tool to improve accessibility for citizens with physical and cognitive
disabilities, the simulations for this study indicate a small price increase per passenger.kilometre if theentire fleet of Shared Taxis serving all clients were adapted to accept wheelchairs, compared to a scenario
with no service for clients with impairments. However, the resulting price is still much lower than thecurrent equivalent price of regular taxis and even of public transport.

What we recommend
Start to integrate shared mobility solutions into existing urban transport plans
Shared mobility can contribute greatly to achieving key objectives for urban mobility. With today’s
technologies, shared mobility can deliver significant improvements quickly and with relatively low risk. In all
indicators analysed for this study, the impacts are positive in the city core as well as in the wider
metropolitan area.
Leverage shared mobility to increase use of existing high-capacity public transport
High-capacity public transport services offer great value to users, but are often underutilised because ofdifficulties for users to reach stations in an efficient way. Linking them with demand-responsive Taxi-Buses
acting as feeders improves access and increases use. The efficiency of the shared feeder services is alsoenhanced since they benefit from a common destination for all travellers. In order for the feeder services tooperate optimally, the design of the public spaces adjacent to the metro and rail stations must be adapted.
Generally, delivering better service through connecting shared mobility solutions with high-capacity public
transport requires a holistic approach to planning involving all key stakeholders.
Deploy shared mobility services in a phased way that maximises public acceptancePhasing in shared mobility should be done in steps that are based on goal-oriented policy measures,delivering at each step sufficient progress on core objectives such as reducing congestion and emissions, sothat an easy perception of collective benefits can be associated with a positive experience by those who
shift to the shared modes. This alignment facilitates public acceptance and can have a strong influence on
adoption rates.
The measures for each implementation phase must focus as much on managing private car use as onproviding a high-quality service to those who try the shared mobility solutions.
Because urban characteristics and political environments differ for each city, the feasible deployment
scenarios could be very different from those tested in this report. The scenarios described here havefar-reaching impacts beyond those linked specifically to transport and mobility. Managing the transition
described in this report will therefore require addressing a broader range of considerations and preparations
beyond the strict transport-related measures described above.
Optimise overall efficiency while assuring a healthy level of competition in the market.
Market structure scenarios indicate these choices have an impact on efficiency for Shared Taxis andTaxi-Buses. The single dispatcher model resulted in the greatest efficiency gains but the exact relationshipbetween dispatcher and operator may follow different models and these relationships should be carefully

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EXECUTIVE SUMMARY – 9

assessed in advance. The dispatcher could be organised as a public monopoly or a regulated, time-boundprivate concession selected through a competitive process. In order to encourage healthy competition in themarket, multiple operators of shared services could be accepted and societal gains achieved through theircapacity to innovate and differentiate themselves in pursuit of more clients, but routing decisions would
remain the purview of the dispatcher.
Limit exclusive occupancy of shared vehicles to avoid the erosion of traffic reduction and CO2 emissions
benefits
Responding to exclusive occupancy requests for Shared Taxis by clients has a natural market and should bepossible. However, the price difference to shared use should be calibrated to avoid large-scale exclusive
occupancy of vehicles that would considerably hamper the system efficiency both in reducing traffic
volumes and CO2 emissions. In a simulation with a small percentage (4.1%) of trips requesting exclusive
occupancy, the loss of reductions of traffic volumes and of CO2 emissions in relation to the scenario with noexclusive trips was amplified by a factor of 1.7 (i.e. led to 6.9% loss of those reductions).
Leverage the significant potential of improved territorial accessibility created by shared mobility
Shared mobility systems allow large improvements in equitable territorial accessibility. It is imperative thatcity planners understand the impact of shared mobility on spatial accessibility and make use of its potential
to deliver equitable access. Taxi-Bus trips typically only take half the travel time compared to traditional
public transport because transfers are not required (except where shared vehicles feed existing metro orrail lines) and smaller vehicles need to stop less often to fill up with passengers. This radically increases
range and hence access to the number of jobs and social services, especially for citizens in areas that arepoorly served by traditional public transport. City planners need to be aware of this possibility and be partof the dialogue preceding the deployment of shared mobility services.
Make shared mobility services fully accessible to citizens with reduced mobility
Every citizen has a right to good access to personal or professional opportunities and transport services toreach them. For those with impairments and special mobility needs, Shared Taxis that are fully accessible,
e.g. for wheelchair-bound clients, would significantly improve the range of reachable opportunities andoverall quality of life. Results indicate that this benefit could be achieved with minimal impacts on operating
costs.

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10 – INTRODUCTION

1. Introduction
The nexus of digitalisation, new business models, ubiquitous computing and platform-based supply/demandmatching is already beginning to have an impact on many aspects of modern life, including the way inwhich people and goods move about cities. Going forward, these trends are likely to lead to a drasticallydifferent mobility landscape in cities – one that has the promise, but not the certainty, of delivering better
access, traffic and environmental outcomes than what is possible today. But there is also much uncertainty
about how technology and business models may evolve and under what configuration mobility services will
be delivered in cities of the future. This uncertainty rests not just on drivetrain technologies and fuels, buton the future evolution of public transport and on where the balance between individual and shared mobility
may finally settle. The future of mobility in cities is likely to be driven partly by technological innovationfrom the private sector and by the framework and guidance put in place by authorities responsible forregulating public space, transport services and delivering better outcomes for all citizens. It is in that
context – of helping authorities understand and possibly guide future developments – that the ITF throughits Corporate Partnership Board, has engaged in detailed modelling of what innovative mobility services
built on optimised sharing of transport assets may bring to future cities.
This work has resulted in two reports – Urban Mobility System Upgrade: How shared self-driving cars couldchange city traffic (ITF, 2015) and Shared Mobility: Innovation for liveable cities (ITF, 2016a) that havedemonstrated the potential for optimised, shared mobility systems to deliver much more efficient transport
services, reduced congestion and lower environmental impacts while delivering better spatial accessibility
and being generally cost-competitive with current options. This work has investigated replacing all car- andbus-based trips in a mid-sized European city (Lisbon) with two new mobility options: on-demand,door-to-door and dynamically dispatched Shared Taxi services, in which up to six people get in and out of aroomy vehicle along trajectories that are optimised in real-time; and a street corner-to-street cornerTaxi-Bus service that requires 30-minutes advanced reservation and that provides transfer-less trips using
minibuses along dynamically defined routes. Other trips are carried out by high-capacity public transport orby walking or cycling. The analysis consists of running an agent-based simulation allocating the entire
synthetic trip population which plausibly represents the actual number and characteristics of all trips taken
within the city on a normal week day to one of the travel options outlined above with constraints on
minimal detour distances and times.
The results of the modelling exercise are unequivocal – the shared mobility system, as configured andmodelled for the centre of the Lisbon Metropolitan Area (LMA), delivers much better outcomes than today’stransport system. However, the outcome of this work should not be seen as an accurate prediction of what
the future will bring to that (or other) cities or as a definitive indication of the potential for shared mobility.
It is a detailed and robust “what if” exercise that highlights the order of magnitude and scope of changes
that might result from the full deployment of shared mobility systems in cities. One reason the results are
only an indicative predictor of the future is because the model does not include iterative feedback loops thatwould otherwise capture changes in behaviour induced by the types of changes described in the scenarios.
For example, if transport costs were to reduce dramatically as suggested by the simulation model –including the reduction of the generalised cost of transport implied by a congestion-less future – people
could make different choices regarding home and work locations and develop alternative (possibly longer)
travel patterns than those they have today.
Another reason that these “all-in” scenarios say little about the final potential for such shared use mobility
systems is that it isn’t clear how these systems might operate in different geographic contexts, urban scales
or during the transition to whatever the final penetration level for these services amongst the overall
transport offer might finally settle. It also isn’t clear if these systems can meet the mobility needs of those

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INTRODUCTION – 11with physical or cognitive impairments at least as well or better than what is possible today. These areimportant questions that the present report (and other related work at the ITF) tackles.
In terms of testing the results of initial shared use model runs for Lisbon in other geographic contexts ofidentical scale, the ITF has initiated similar modelling of shared use systems in three other medium-sized
cities (so far Auckland, Dublin and Helsinki) with an eye to testing the model in geographically, culturally
and morphologically distinct cities around the world.
In addition to the elements outlined above, this report looks at:What are the impacts of urban scale compared to the initial findings of the modelling exercise
undertaken in previous reports for the central Lisbon Municipality? How does the performance of
optimised shared mobility services change when the larger Lisbon Metropolitan Area as a whole isconsidered and not just the urban core.

What might be the impact of graduated deployment of these services along a plausible early
adopter pathway?

What is the impact of different market structure configurations on the efficiency of shared use
mobility systems?

Can shared optimised mobility services deliver high-quality accessibility to those with reduced
mobility and cognitive difficulties and, if so, under what configuration?

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12 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

2. Scaling up: Evolving from city to regional level
Description of the modelling framework
The agent–based modelThis report and previous work is based on an agent-based model that simulates the daily mobility of various
cities – in this case the city of Lisbon in Portugal. The model is built around three main agents that interact
in a common environment: users, vehicles and the dispatching entity. The model works with a synthetic
population of trips that serves as a plausible proxy for every trip taken on a normal weekday. These trips
occur across a spatial context where transport networks are present (for road, rail and ferries) and where
public transport supply is represented by existing routes (bus, metro, rail, and ferry services across theriver). The model addresses the interaction between clients and vehicles, simulating their connection andhow, in terms of timing and location, the services are performed. The approach is based on a static
representation of the traffic environment where origin-destination flows are allocated to a rather complete,
topologically correct road network that accounts for per-link occupancy (and thus for speeds), by time ofday. Travel time is attributed to each arc and intersection using a dynamic traffic assignment procedurethat updates travel time estimates based on flow capacity ratio every five simulation minutes.
In the simulation for shared use services, the dispatcher system manages the centralised task of assigning
trip requests to vehicles using the location of shared vehicles, their current occupancy level and the locationof clients as its main inputs. The model estimates trip routing on the basis of an algorithm that generates
the lowest time cost and insertion cost path between any pair of nodes of the network.
The city centre is divided into a homogeneous grid of 200 metre (m) x 200m cells, whereas the greater
Lisbon Metropolitan Area is segmented into a grid of 500m x 500m cells. All trip origins and destinations arelinked to the closest node of the road network.
Modelled shared use options
Two shared use modes serve as the basis for the shared mobility modelling exercise: a ride-sharing system(Shared Taxi), which emulates a taxi-like system where customers accept small detours from their original
direct path and share part of their ride with others, and a dynamic bus-like service with minibuses(Taxi-Bus), where customers pre-book their service at least 30 minutes in advance (permanent bookings
for regular trips could represent most requests) and walk short distances to a designated stop. In all cases
travellers get a transfer-free trip from origin to destination. Table 1 presents a brief characterisation ofthese two modes.
Table 1. Specifications adopted for the shared mobility services
ModeBooking

Access timeMax. wait timeMax. total time loss

Vehicle type

SharedTaxi

Real time

Door-to-door

5 minutes (for atrip ≤ 3 km), up
to 10 minutes (fora trip ≥ 12 km)(detour time + waitingtime) from 7 minutes(for a trip ≤ 3 km), up
to 15 minutes (for a trip
≥12 km)Minivan currently
seating 8 rearranged
to seat only 6,
providing easy entry
and exit

TaxiBus30 minutes
in advance

Boarding and alighting up
to 300m away from door,at points designated in realtime (street corner-tostreet corner)

Tolerance of 10
minutes frompreferred boardingtime

Set by the minimum
linear speed from origin
to destination (15 km/h)

Minibuses with 8 or 16
seats.
All passengers seated no standing

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 13

The specifications for these services were designed for a high level of acceptance of modal transfer bycurrent car drivers, by providing them with the three quality attributes of flexibility, comfort andavailability. The Shared Taxi and Taxi-Bus services were designed to fully replace current motorised roadtransport alternatives (car, motorcycle, taxi and bus), implying a modal shift of users to the new transport
alternatives or to the previous options that remain available (walking/cycling, metro, rail and ferry).
Demand and mode choice
Based on an extensive mobility survey conducted in Lisbon Metropolitan Area (LMA) (Câmara Municipal deLisboa, 2005) the model input is a synthetic population of people and their trips that contains logs of every
single trip disaggregated at the census block level and in time (e.g. recording different trip departure andarrival times) for a normal week day in the reference year of 2011.1
Each trip is characterised not only by its time of occurrence, origin and destination, but also by trip
purpose, composition of the trip party in terms of age and by whether or not its members have a public
transport pass or not. Additionally, based on census data and other mobility surveys, each trip party
member is further characterised by the traveller’s gender, income and by whether or not the traveller has adriving license, car, motorcycle, parking spaces at home and at work. The purpose of the trip determines
the activity duration, which can be used to calculate parking costs.
The destination grids are characterised by parking cost and parking pressure (ratio of demand to supply),both depending on time of day, linking the available demand data with the statistics and pricing of Lisbon’s
parking.
For all modes currently available – car, motorcycle, taxi, walking, bus or tram, cycling, metro or suburban
train, and ferry, combination between low and high capacity transport modes – the trip is characterised byaccess time, waiting time, travel time, cost and number of transfers (if applicable).
A procedure for modal choice, similar to that adopted in the earlier Corporate Partnership Board (CPB)
studies, was adapted to the LMA case, as described below. When the model chooses a shared option, a newuser (agent) is generated in the simulation environment, with a departure node, an arrival node and astarting time. Currently, one user is equivalent to one trip, i.e. users do not cluster in parties at the outsetof their trip (though they do share vehicles whilst underway in the Shared Taxi and Taxi-Bus simulations).
Users
In the simulation environment, a trip is generated when a user (or party of users) requests a service fromone point towards another point. The model accounts for the simulation parameters (resulting from thespecification of each shared mode) and accounts for waiting time, detour time and arrival time tolerancesthat are defined for the model run. The dispatcher then finds, in real time or with the pre-booking, the best
possible routing and assigns one of several available vehicle types to carry out the trip in a shared mobility
solution.
The user then waits for the vehicle (Shared Taxi) or walks to a specified pick-up location (Taxi-Bus) andboards the vehicle. When the vehicle arrives at its destination, the user exits the system and a set ofindicators are generated in a trip log so that they can be used for ex-post system evaluation.
Cars and minibuses
Idle cars are located in stations (depots) spread across the city and whenever the car is empty and notdispatched to a new trip, it returns to the nearest station. Active cars follow the shortest path and minimise
travel time for its route assignment taking into dynamically updated link travel times and intersection
delays (every five minutes).

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14 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Taxi-Buses are located at the departure stop on each generated route by relocation from the last performed
service. The location of all these “pop-up” stops was defined at the outset, constrained by minimum
distance between stops (300m) and the selection of the road node with greater connectivity in theneighbouring area in order to ensure flexible routing for the vehicles (e.g. by avoiding streets with traffic
only in one direction or left-turning blocking).
The fleet of cars and minibuses is an output of the simulation by measuring the number of vehicles that are
required for the simulation and their relocation dynamics between stations during the day. The minibuses
required are differentiated between 8- or 16-seated passenger minibuses, and are allocated according to
demand.
The dispatcher
The dispatcher acts according to 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 Shared Taxi or Taxi-Bus tomatch with a user’s request takes into account a time-minimisation principle that applies not just to therequesting user but also to those already underway in the same vehicle.
Several parametric constraints have been defined that must be satisfied for each trip route solution
proposed by the dispatching system as described in the service specification section.
The model defines in parallel the dispatching of Taxi-Buses and Shared Taxis when both systems areoperating. Users launch their requests; their preferences are already pre-recorded in the system. Taxi-Bus
requests are processed 30 minutes in advance. Shared Taxis are processed in real time. The dispatcher
runs a local search algorithm that tries to maximise the number of passengers assigned complying with theusers constraints at each step (best match in Taxi-Bus service that warrant at least 50% occupancy (atleast for some part of the trip) and an average distance based occupancy rate greater than 25% of thevehicle capacity). The users who are not assigned to Taxi-Buses because of these constraints are thenre-assigned (upgraded) to the Shared Taxi system as real-time requests, following the Shared Taxi
real-time booking system automatically performing door-to-door services. This upgrade option was enacted
since it could provide higher quality for the user at a lower supply cost.
The dispatcher also controls the vehicle movements when idle, ensuring efficient vehicle movements tostations that may require additional fleet in the near future.

Scaling up to the greater Lisbon Metropolitan Area
The previous two CPB studies (ITF, 2015; 2016a) modelled shared mobility systems for the municipal areaof Lisbon (referred to as the “city” in this study) – the central core of the greater Lisbon Metropolitan Area
(LMA). It wasn’t clear, however, whether the findings would hold at a much larger scale – in this context,
for the whole of the LMA. In order to test the model and impacts of shared mobility services at this scale, anumber of significant adaptations to the model were required. These adaptations include changes andimprovements in the inputs, adaptations in the methods to deal with the change of scale, and thediversification of the shared services provided and their integration with metro- and rail-based public
transport services.
The LMA has approximately 2.8 million inhabitants, representing roughly 25% of the Portuguese population,with an area of about 3 000 km2 formed by other 18 municipalities (officially just 17 as Azambuja is notconsidered part of the region). Figures 1, 2 and 3 show the extent of the LMA as compared to the city of
Lisbon proper, as well as the population and employment densities for the region.
The LMA generates 5.03 million person-trips each day, corresponding to 4.64 million trip parties. The
difference corresponds to trips of parents with their children or to accompanied seniors, only counted as

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 15

one trip in the model (but with the correct party size). Of this total activity in the LMA, about 1.1 million
trips take place within the administrative boundaries of the city.
Table 2. Comparison of Lisbon City and Lisbon Metropolitan Area
Indicator

Lisbon City

Lisbon Metropolitan Area (LMA)

Area

84.6 km2

2,957.4 km2 (x35)

Population

547 733

2.8 million (x5)Trips

1.1 million

4.6 million (x4.2)

As in many urban areas, the city of Lisbon has a greater incidence of non-individual car-only based trips(61%) as compared to the part of the LMA outside of the city centre (53%) (see Table 3). Looking at the
breakdown of trips by major operational zone in the Lisbon area as compiled from individual corridor-based
analysis, car-based mobility is highest for trips going to and from central Lisbon from the outer zones (76%of these trips are by car) as shown in Table 4.
Figure 1. Extent of Lisbon and Lisbon Metropolitan Area

Source: ITF, Map tiles by QGIS.

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16 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Figure 2. Population density for Lisbon Metropolitan Area

Note: 500m grid, quartile/equal count breaks.
Source: ITF, Map tiles by QGIS.
Figure 3. Job density for Lisbon Metropolitan Area

Note: 500m grid, quartile/equal count breaks.
Source: ITF, Map tiles by QGIS.

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 17

Table 3. Transport mode share (Passengers)
Current mobility
LMA(%)

Lisbon city(%)

Outside of Lisbon
city (%)

Private car (PC)

45.9

39.5

47.8

Motorcycle

1.1

1.0

1.1

Taxi

2.1

1.7

2.1

Bus

20.3

18.3

20.9

Walking

18.5

24.0

17.0

High capacity public
transport (rail, metro, ferry)

6.9

12.7

5.3

PC + High capacity modes (rail, metro, ferry)

1.3

1.1

1.4

Bus + High capacity modes (rail, metro, ferry)

3.9

1.8

4.5

Table 4. Mode share by operation area compiled from corridor-based analysis: Reference case (2011)
Per corridor
Operation area
Internal to Lisbon
Radial from/

Modal shares (Whole day)

PKM whole PKM morning
day (%)
peak (%)

Bus
(%)

Metro
(%)

Rail
(%)

Light Rail
(%)

Ferry
(%)

Motorcycle Private car
(%)
(%)

Walk
(%)

Taxi
(%)

9

8

15

10

1

0

0

1

57

12

3

46

42

8

2

7

0

1

1

76

1

4

31

35

14

1

6

0

0

1

68

6

4

15

15

18

0

2

1

0

1

66

8

4

100

100

12

2

5

0

0

1

70

5

4

to Lisbon
Between North Bank
Municipalities
Between South Bank
Municipalities
Total LMA

Note: PKM = passenger.kilometre
The total daily number of trips considered in this study (all corridors, all modes) is 4 606 330. The morning
peak represents 18% of these trips.
Transport challenges and solutions emerging from the change of scale
The expansion of the model from the city to the LMA required several adaptations to the model inputs andsimulation model. The most relevant are:

The development of a new activity-based model that incorporates all information at the household
level and the connections between the activities of different households.

The optimisation model for Taxi-Bus operations had to be adapted to improve calculation speed forthe whole LMA. In order to do this the LMA was split into different service areas linked by corridors.



The mode choice model developed for the city was adapted and expanded to the LMA. This
procedure allows estimating the preferred mode under the most extreme configuration of fullshared mobility deployment in the LMA. Additionally, through an indicator of early adoption, it sets
the order of customers in adopting shared mobility.



The change of scale required modifying the spatial resolution in the model for the LMA area outsideof the city to ensure that the available memory to run the model was sufficient.

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18 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVELThe expansion of the design of shared services for the LMA requires a deeper integration withpublic transport infrastructure and services. The shared services, especially the Taxi-Bus, areorganised to provide feeder services at origin or destination to and from high capacity public
transport (rail, ferry and metro stations). These services make more sense at a metropolitan level
where the savings on travel time and cost to connect the high efficiency public transport networks
can greatly enhance accessibility.

New activity-based model approach
The development of the model for the LMA also required important changes to the way in which synthetic
trips were generated. While previously a synthetic trip dataset was created inside the city using aprobability specification of each origin-destination (OD) pair along the day, the new LMA model generates
information on the household composition, the activity of each individual member and the resulting mobility
considering the connection within the household in common activities and use of the household private
vehicles.
The current model encompasses an activity-based model that has three main components: thedevelopment of a synthetic population (calibrated to match the 2011 Portuguese Census), the creation of
the structural activity for all household members over 13-years old to generate habitual trips (work, school,etc.), and the generation of discretionary trips (access to shops, services, recreation, culture, family, etc.)
in the activity time available.2
The new model generates a set of data covering all activity and trip-making behaviour for individual
synthetic residents of the LMA. It does not include non-residents or visitors to the area. This change inmodel specification from a mobility probability by OD pair with no reference to persons and household,alongside the use of more recent data, resulted in 40 000 fewer trips than in previous estimates (from anearlier total of 1.1 million). The increasing spatial spread of the LMA helps to explain this since it reduces
the relative weight of trips occurring within the city along with the fact that some trips that previously
occurred across the border of the city (and were excluded from analysis) are now included in their entirety
for the greater LMA analysis. The peak period assessment for the LMA also shows some change whencompared to the city in that the LMA morning and evening peaks are of very similar intensities while in thecity the morning peak is clearly stronger.
Conceptualisation of operational corridors
As noted previously, in order to optimise simulation speed, the entire synthetic trip population for thegreater LMA was split into five functional areas and seven bi-directional corridors (or 14 uni-directional
corridors) where, due to geographic proximity or infrastructure availability, travellers would have thegreatest probability of being able to share their rides – particularly for Taxi-Bus services. Optimisation oftrip allocation was carried out in parallel in each separate corridor. The results from this corridor-based
analysis are then aggregated into four broad service areas: within the North Bank outside the city (A),within the South Bank (B), between the city and the external municipalities (C), and within the city (D).

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 19

Figure 4. Functional areas and operational corridors used for the greater LMA analysis

Source: ITF, Map tiles by QGIS.
Table 5. Corridor names, typologies and respective share of total number of trips in the LMA
Corridor number(code)

Corridor nameCorridor typology

Share
(%)

Between municipalities in North Bank outside(A) Mobility to/from Lisbon from external

the citymunicipalities

2 (B)South Bank

(B) Mobility with the South Bank

20.18

3 (C1)

Cross-river (towards the north)(C) Mobility in the North Bank outside the city

3.61

4 (C2)

Cross-river (towards the south)(C) Mobility in the North Bank outside the city

3.61

5 (D)

Inside Lisbon city(D) City

22.2

6 (C3)

Sintra Corridor to Lisbon (towards Lisbon)(C) Mobility in the North Bank outside the city

2.85

7 (C4)

Sintra Corridor to Lisbon (towards Sintra)(C) Mobility in the North Bank outside the city

2.85

8 (C5)

Cascais Corridor to Lisbon (towards Lisbon)(C) Mobility in the North Bank outside the city

1.74

9 (C6)

Cascais Corridor to Lisbon (towards Cascais)(C) Mobility in the North Bank outside the city

1.75

10 (C7)

Vila Franca Corridor to Lisbon (towards Lisbon)(C) Mobility in the North Bank outside the city

3.20(C) Mobility in the North Bank outside the city

3.20

1 (A1)

11 (C8)Vila Franca Corridor to Lisbon (towards Vila
Franca)12 (A2)Inside Vila Franca Corridor

13 (A3)Inside Sintra Corridor

14 (A4)Inside Cascais Corridor

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(A) Mobility to/from Lisbon from externalmunicipalities(A) Mobility to/from Lisbon from externalmunicipalities(A) Mobility to/from Lisbon from externalmunicipalities

5.96

11.39

9.88

7.50

20 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Specification of new feeder services
Looking at the characteristics of the current mobility landscape in Lisbon – especially with regards topossibilities for integrating shared services with ferries, regional rail services and the metro – a new,composite, transport mode choice was introduced, based on high quality feeder services to high capacity
public transport. The implementation of this new service class builds on Taxi-Bus optimisation routines andextends them to include connections to rail- and ferry-based public transport such that travellers can easily
and comfortably be brought to their destination with time- and cost-efficient solutions.
Figure 5. Rail, ferry and metro lines and stations used at the feeder service specification

Source: ITF, Map tiles by QGIS.

The feeder service is specified as a pre-booking system in the model similar to Taxi-Bus services. Feeders
use the same minibuses and have the same walk access constraints (no more than 400m at either end of
the feeder service). Furthermore they serve those high capacity public transport trips where the traveller iswithin easy walking distance to the intended final trip destination (less than 10 minutes walking), or
vice-versa. Differential weights are assigned to walking (three times more) and the connecting Taxi-Bustime (1.5 times more) as compared to the time spent travelling in the rail-based public transport or ferry.
The feeder service specification in the model implies the following:

The feeder service only serves one end of the trip. An OD pair badly served at both ends may leadto a direct Taxi-Bus or a Shared Taxi service.

The walking segment linking the high capacity mode with the extreme (origin or destination) pointof the trip will be less or equal to 10 minutes.

High-capacity public transport trips of less than 5 km do not accept any transfers. Trips longer than
5 km may involve one intra-rail or intra-metro transfer.

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 21

The total distance in the feeding part of the trip (walking + shared mobility) should always be
shorter than the direct distance between trip origin and destination. This ensures that feeder
services do not result in long detours from the most efficient path.

The dispatcher always considers a Taxi-Bus service running at the design speed (15 km/h linear speed). As
with regular Taxi-Bus services implemented in the model, if a Taxi-Bus meeting minimum load factor
constraints cannot be matched to the rider, they will be upgraded to a Shared Taxi instead. This occurs
much less often for the feeder services than for other random point-to-point services because of theconcentration of users at the high capacity mode station. When actually performing the service, the speedof Taxi-Buses is adjusted to the simulated speed on the road links they travel on, according to traffic
volumes.
Allocation of trips to modes in the simulation
As this is a modelled scenario, and we do not have information on passenger preferences through, forexample, a stated preferences survey focusing on the shared mobility services, this study uses analternative approach to estimate mode choice under a radically new scenario. For this purpose, an expert
analysis procedure was used, generating a rule-based lexicographic choice process based on
socio-demographic and mobility attributes of the user. The envisaged selection process tries to incorporate
the individual rationale in mode choice while preserving some room for the diversity of individual
preferences (stochasticity). The mode choice process has been adapted from the previous study toincorporate the new available modes and a higher acceptance regarding the number of transfers in highcapacity public transport (rail and metro) by people living further away from the city centre.
For trips in modes that continue to exist (walking and high capacity modes [metro, rail, ferry]), the mode
choice is the same except when the reference situation was very penalising (i.e. very high access time, or
two or more transfers in public transport solutions).
Trips in transport modes that are suppressed in the configuration with full adoption of shared mobility
solutions are assigned to the Taxi-Bus or the Shared Taxi, following the rules indicated below. In trips forwhich a shared feeder to high capacity public transport leads to a shorter travel time, with no more than
one transfer and walking access time not higher than 10 minutes, the user switches to the feeder service.
The evolution to a fully adopted shared mobility solution follows the market penetration of the new sharedservices. Consideration of possible migration to the shared services is made sequentially, with six classes as
specified in the Table 6. The sorting within each of these six classes is made according to the number oftrips per day and the total travelled distance (higher modal transfers for the higher number of trips ortravelled distance).
In each of these classes, taken in sequence, users will compare their current option (if it remains active in aparticular configuration being simulated) with the new options and decide based on the following rules:

Private car and motorcycle users: Car users by default are migrated to the Shared Taxi
alternative with some notable exceptions. Very short trips (less than 1 km), faced with the newcontext, migrate to walking; cars users owning a public transport pass select Taxi-Bus 70% of thetime, but the trips with a faster solution based on a feeder service than on a Taxi-Bus aretransferred to that option. In the other 30% of cases, they transfer to Shared Taxis.

Taxi users: By default taxi users are migrated to the Shared Taxi alternative with some
exceptions. Taxi users owning a public transport pass or with no car select Taxi-Bus, except where
total travel time is shorter with a feeder service to high capacity public transport.



Pedestrians: Current trips up to 2 km preserve this mode choice. With longer trips, we assumeusers may evaluate and preserve their mode choice or migrate to the modes available in the new

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22 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

transport supply configuration (metro, Taxi-Bus, Shared Taxi or feeder service). The rules of choiceamong the non-pedestrian alternatives are the same as for current public transport users.


Public transport users:a)

Bus and bus + high capacity public transport (metro, rail or ferry): Bus users must change
current mode. They are allocated the Taxi-Bus option with the exception of trips where travel
time is shorter with a feeder service to high capacity public transport.

b)High capacity public transport (metro, rail or ferry): They preserve their modal choice except
when the access time or number of transfers is very penalising (more than 20 minutes accesstime and more than one transfer). In those cases they follow the rule of bus users, being
allocated the Taxi-Bus option except where total travel time is shorter with a feeder service tohigh capacity public transport.

c)

Private car + high capacity public transport (metro, rail or ferry): These trips are allocated tofeeder services into high capacity public transport with the exception of cases involving longer
walking access at origin-destination and more than one transfer, which are transferred toTaxi-Bus.
Table 6. Trips listed by order of consideration for modal transfer

Order of class

Criteria

Percentage of all trips (%)

1

Taxi trips

2.59

2

Bus and bus + high capacity modes (metro, rail or ferry) trips

23.29

3

Very long walking trips (greater than 3 km)

0.18

4

Car commuting trips with paid parking and more than 3 hours
parking

7.48

5

Other private car trips (free parking, or paid parking with less than
3-hour stay) and motorcycle trips

45.10

6

Trips in other modes (mostly high capacity public transport andshort distance walking)

21.35

This procedure is implemented to each generated user (trip), allowing estimation of modal diversion in eachshared mobility configuration. Because Taxi-Bus services are constrained to achieve at least
50% occupancy at some section of the route, the users that select Taxi-Bus and do not have that service
available are diverted (upgraded) to the Shared Taxi option at no extra cost for the user. Since the Shared
Taxi operator should not receive less revenue, in our cost estimates we assumed that the price differentialof the upgrades would be spread across all Taxi-Bus users.
Table 7 indicates how trips in the reference case (2011) were re-attributed according to the lexicographicalrules described earlier for a configuration where all car and bus trips were replaced by Shared Taxis andTaxi-Buses. The results show that individual motorised transport users (car, motorcycle and taxi) switch
mainly to the Shared Taxi and Taxi-Bus options, while walking and metro users mostly remain in theirreference mode, except in cases of long walking distances or bad metro connectivity, in which case they
divert to motorised shared alternatives. Users that previously combined rail and bus modes mainly switch
to shared alternatives, either due to poor rail/metro connectivity or due to long access time to metro andrail.
It is relevant to note that high capacity public transport modes (in direct access plus access by motorised
transport (private car or bus in the reference case, feeder bus or Shared Taxi in the simulated scenario)

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 23

grows from a market share of 12% (7%+1%+4%) to 17% (6%+11%) i.e. a relative growth of about 40%(17% ÷ 12% = 142%).
Table 7. Analysis of mode diversion (whole LMA) – Passengers/day
Mode in reference
scenario (2011)

Mode in simulated configuration – Full adoption of shared mobility
Walk (%)

High
capacitymodes (%)

Shared Taxi(%)

Taxi-Bus(%)

Feederservice (%)

Total in reference
scenario (%)

Private car (PC)

5

0

58

27

9

46

Motorcycle

9

0

46

41

4

1

Taxi

0

0

56

40

4

3

Bus

0

0

0

82

18

20

Walking

72

1

0

23

5

19

High capacity public
transport (metro, rail or
ferry)

0

71

0

2

28

7

PC + High capacity
modes (metro, rail or
ferry)

0

0

0

32

68

1

Bus + High capacity
modes (metro, rail or
ferry)

0

14

0

82

4

4

Total

16

6

28

38

11

Overall modelling results of shared use mobility for the Lisbon
Metropolitan Area
Aggregate impacts (traffic and emissions) for the Lisbon Metropolitan Area (LMA)
The overall, aggregate impacts resulting from the full-scale implementation of a shared mobility service forthe whole of the LMA are generally more positive than the initial results reported for Lisbon Municipality(the city). As mentioned above in the “New activity-based model approach” section, these results for thecity are slightly different from those published in the previous report (ITF, 2016a) due to the changes in theconstruction of the synthesis population and the mode choice model, but those small differences bring nochange to their significance in policy terms.
The following sections present an overview of some indicators that are especially meaningful in terms ofaccess and mobility in urban agglomerations. The set of results presented is a compromise between theneed for a synthetic presentation in order to allow the reader to retain the key messages and theavailability of very large set of outputs from the agent-based simulation.
Table 8 shows the key macro-level results of the scenario involving the full-scale replacement of all bus andcar trips with shared mobility services, while maintaining walking and rail-based public transport. These
results point to higher achieved load factors for Shared Taxis, higher load factors for larger Taxi-Buses(outside of the centre of the LMA), bigger reduction of vehicle.kilometres (VKM) travelled throughout the
day with an even greater reduction at peak hours as well as lower CO2 emissions for the LMA in comparison
with the city.
There are multiple reasons why the simulation at the level of the LMA produces stronger results (in terms of
reduction of VKM and emissions) than the simulation for the city but the main reason lies in the greater
efficiencies derived from the adoption of Shared Taxi and Taxi-Bus feeder services to rail-based public
transport. In the shared mobility scenario for the LMA, rail-based trips accounted for 17% of all trips
whereas in the reference scenario, they only accounted for 12% (Table 7).

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24 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Table 8. Overall impacts of full implementation of shared mobility at the scale of the LMA
Aggregate indicators
Avg. pax on board(Shared Taxis)
Avg. pax on board(Taxi-Bus)

LMA

LisbonOutside Lisbon

2.3(peak 3.0)

2.1
(peak 2.7)

2.4
(peak 3.2)

4.3 (8 seat) / 11.9 (16 seat)

4.8 (8 seat) / 10.7 (16 seat)

4.1 (8 seat) / 13.2 (16 seat)

Peak: 4.8 (8 seat) / 12.9 (16 seat) Peak: 4.6 (8 seat) / 12.3 (16 seat) Peak: 4.8 (8 seat) / 13.6 (16 seat)

VKM (weighted) all-day % reduction compared toreference case (2011)

-48%

-40%

-51%

VKM (weighted) peakhour - % reduction
compared to referencecase (2011)

-55%

-44%

-59%

CO2 emissions

-62%

-53%

-67%

Note: Pax = passenger; VKM = vehicles.kilometres.

In all cases the indicators (except for the occupation of the 8-seater Taxi-Buses in the morning peak) for
the LMA are better than for the city. This is due to multiple reasons including:Private cars are the largest contributor to VKM and to CO2 emissions in the reference case, andtheir part of passenger.kilometres (PKM) globally for the LMA is 63.4 % whereas in the city it is47%. So, the volume (of traffic and of emissions) available for reduction is bigger for the LMA.



Whereas in the city, the transfer from private cars is made to Shared Taxis and Taxi-Buses (which
are also emitters, albeit at lower levels per PKM due to the higher occupation), in the case of the
LMA a substantial part of those transfers are made to the high capacity public transport which ismostly electric rail (we did not count for the emissions in the generation of the electric power), theexception being the ferries with a very small part of that set. In the shared mobility scenario for
the LMA, high capacity public transport trips accounted for 17% of all trips whereas in thereference scenario, they only accounted for 12% (Table 7).



Average occupation of Shared Taxis outside Lisbon is higher because a large part of those trips areinto or coming out of Lisbon, and this (relative) concentration at one of the extremes of the trip
makes ride sharing more efficient.

In the LMA the part of the 16-seaters in the Taxi-Bus fleet is by far larger (contrary to what isfound for the city, where 8-seaters dominate), and the higher occupation they show in relation to
the city is also due to the feeding role they play for high capacity modes and in serving thependular trips into/out of Lisbon. In the LMA the 8-seaters mostly do complementary functions
serving less dense demand flows.

Accessibility levels to jobs in the Lisbon Metropolitan Area
Accessibility based on public transport modes increases very strongly with the introduction of sharedmobility services. While only a few cells and a limited part of the population could access a considerable
share of employment within an acceptable time (distance decay function as an S-curve calibrated for the
LMA in the reference case [2011]), the increased access with shared modes and their mostly direct services
are more a function of spatial distance to the job market than of public transport provision quality.

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 25

Using the S-curve to represent attraction decay, accessibility calculations produce results in terms of“effective access to jobs” (or other opportunities and services), meaning the number of jobs that we should
expect to be considered as accessible by people in an acceptable (to them) timeframe. When data isavailable with the adequate level of geographic precision, this produces results that better represent reality
than the more traditional indicator of “number of jobs within 30 minutes”, as explained in Corporate
Partnership Board report Linking People and Places: New ways of understanding spatial access in cities (ITF,
2017). In this section we use the S-curve calibrated for users of public transport in the LMA.
Figure 6. S-curve representing the attraction decay for accessibility calculations(using public transport)

In all the LMA areas, although with some differences in terms of acceptable waiting time, people can travel
at a linear speed of at least 15 km/h with the Taxi-Bus service. This allows a huge increase in the quality ofaccess and moves the good access locations beyond the city, where currently many jobs and public services
are concentrated.
Table 9. Quantitative distribution of access to jobs
25%

50%

75%

% of LMA population enjoying
certain percentages of accessible
jobs in LMA region

Current
mobility

Sharedmobility

Current
mobility

Sharedmobility

Current
mobility

Sharedmobility

Within the city

4.17%

19.63%

0%19.63%

0%0%

Within the LMA (including city)

4.17%

76.05%

0%57.36%

0%0%

Analysing the very synthetic results in Table 9 it is possible to draw a couple of important conclusions:

In the reference case scenario, the 4.17% of the total population in the LMA, with access to at least
25% of the jobs, are comprised totally of residents of the city.

When shared mobility solutions are introduced, we can see how equity of accessibility significantly
improves, both for the city and the LMA: for the case of the city, all its residents (representing
19.63% of the total LMA population) have effective access to at least 50% of the jobs; for the caseof the LMA as a whole 76% of its residents have effective access to 25% of the jobs in the LMA andstill more than half (57%) have effective access to 50% of those jobs.

This very strong increase in access levels also reduces the spatial asymmetries in the LMA as shown in thefollowing table and maps.

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26 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Table 10 represents the values of P90, i.e. the 90th percentile (grid cell with 10% highest accessibility tojobs) and of P10, i.e. the 10th percentile (grid cell with 10% lowest accessibility to jobs), in the reference
scenario and in the scenario with shared mobility solutions, as well as their ratio. Separate values areshown for the city, the remaining corridors on the North Bank, the total area in the South Bank, and thewhole of the LMA. In all cases, reference is made to the whole set of jobs in the LMA.
Table 10. Percentage of LMA jobs in the 10th and 90th percentiles of grid cells for the reference
and the shared mobility scenarios
Geographical area

P10

P90

P90/P10LMA

Current
mobility(%)
0

Sharedmobility(%)
3

Current
mobility(%)
3

Sharedmobility(%)
69

Current
mobility(%)
112.11

Sharedmobility
(%)
19.80

City

2

64

27

72

13.66

1.12

South Bank

0

3

2

57

60.13

16.63

Sintra corridor

0

11

3

71

52.00

6.65

Cascais corridor

0

16

2

72

18.58

4.44

Vila Franca de Xira
Corridor

0

2

1

58

125.0933.45

While introducing shared mobility solutions in the city delivers remarkable results and reduces asymmetry
significantly (reduction of the P90/P10 ratio). The results for the LMA region are even more striking, suchthat for all corridors outside the city the P90 value with shared mobility is more than two times better thanit was for the city in the reference case scenario.
Figure 7 shows, for each cell, the ratio between the number of effectively accessible jobs after full
deployment of the shared mobility services (using Taxi-Bus and feeders + high capacity public transport)
and in the reference case (using public transport), the darker background colours being associated withhigher ratios. To make it clear, this means the rate by which the number of accessible jobs would grow forresidents in each cell.
While in the city most cells have a ratio between 1 and 10 (with a few between 10 and 20), roughly one
half of the total number of cells in the LMA this ratio is above 40, while about 25% of the cells have a ratio
above 100. In essence, what this means is that the access to jobs by 50% of residents increases by amultiple of 40 and for 25% of people by a multiple of 100. This would represent a radical transformation of
the conditions of access to jobs in the LMA.
By considering the shape of the attraction decay S-curve in Figure 6 it is easy to understand how such highvalues can occur: for instance, changing from a very penalising situation in which travel time by public
transport to a job location is 70 minutes - which corresponds to a willingness to travel of 0.3% - to anotherin which the travel time to that same job is a quite acceptable 30 minutes - corresponding to a willingness
to travel of 49% - leads to a ratio (of willingness to go and thus of accessibility to that job) between these
two situations of 163.
The geographical distribution of the darker coloured cells, mostly far away from the railway lines, conveys avery powerful message of how shared mobility and in particular the feeder services combined with theexisting high capacity public transport (mostly rail) contribute to a radical transformation of access to jobs
across the LMA, and in particular outside the Lisbon city. And it provides a logical explanation to why theusage of private cars currently is so high for people living in those areas.
The analysis presented here only covers access to jobs but the benefits of adoption of shared mobility
solutions would be similar regarding access to essential public services like health and education, as alreadyshown by ITF (2016a).

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 27

Figure 7. Ratios between the numbers of effectively accessible jobs with shared mobility solutions
and in the reference caseNote: (using public transport in both cases).
Source: ITF, Map tiles by QGIS.

Performance measures for shared mobility solutions for the Lisbon
Metropolitan Area
This section deals with some of the key micro-scale aspects directly related to the shared mobility services,
both from the clients’ and operators’ perspective. Values are presented with different types of geographicalaggregation in different tables, depending on what was considered the most relevant for interpretation of
results.
The first indicator analysed is waiting time for Shared Taxis services. Since car drivers value the permanent
availability of their car quite highly, this is a very important attribute to be able to persuade current car
drivers to adopt Shared Taxis, and that is the reason why the parameters in the model specify rather low
maximum acceptable waiting times.
The results in the simulation are quite encouraging: in the city, average waiting times for Shared Taxis(across the whole 24 hours) are less than three minutes, and for all the other corridor types they are justaround five minutes. And the 75th percentile (only 25% of the passengers wait more than this) the values
are below three minutes for the city and below seven minutes for all other corridors. Table 11 shows thesevalues, as well as those for the Taxi-Buses.

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28 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVELIn all categories of service and all corridors, median waiting times are very low, and even the 75th
percentile values are below the current median values for most cities in the developed world.
The observed waiting times show that the performance within the city, which has greater density, ensures
lower waiting times. Some lower-demand corridors have an additional five-minute waiting time tolerance,
because they do not have an associated vehicle depot. So, when a vehicle goes idle there, it goes to a
depot in another corridor and this increases the waiting time and empty kilometres to reach the next clientin that depot-less corridor.
It is also interesting to note that in all cases the average is higher than the median, which reflects astronger presence of low waiting times and a very small number of high waiting times.
Table 11. Distribution of waiting times for Shared Taxis and Taxi-Buses (by corridor type)
Corridor type

A

B

C

D

Waiting time (minutes)
Mode

Percentile 25%(P25)

Median (P50)

Percentile 75%(P75)

Average

Shared Taxi

1.16

2.10

6.80

4.43

Taxi-Bus

6.18

8.49

13.49

10.23

Shared Taxi

1.07

1.93

6.70

5.14

Taxi-Bus

7.05

9.50

14.49

10.55

Shared Taxi

1.30

2.38

6.67

4.56

Taxi-Bus

5.49

9.89

13.52

10.26

Shared Taxi

0.93

1.62

2.88

2.46

Taxi-Bus

1.50

4.39

8.52

5.04

Active fleets
After each simulation run representing the generation of demand, the dispatch of vehicles to serve theclients, and the routing of those vehicles on the road network, another computer model allocates duty
cycles of the vehicles to work shifts that correspond to legally acceptable durations. This is done through an
optimisation program with the objective of minimising the size of the vehicle fleets required and thus
making the best use of those vehicles. Like in earlier studies, two types of shift were considered: four hours
and eight hours, the latter with a one-hour meal break.
Figure 8 shows the numbers of active vehicles in each category along the hours of the day after
consideration of the legal limits of working time of their drivers, and thus including the time spent for meal
breaks in the eight-hour shifts or for changing drivers at the end of the work shifts.
The maximum number of Shared Taxis that could operate is 42 245. This represents 4.1% of the number ofregistered private cars in the LMA, and 9.4 times the current number of licensed taxis in the LMA (notcounting ride service operators, such as Uber and Cabify, which were also not present in the reference yearof 2011).
In the case of Taxi-Buses (Figure 9) the general shape of the curves of fleet size in operation is similar tothat of the Shared Taxis, with strong peaks around 08:00 and 17:00. The fleet size of 8-seater Taxi-Buses
is 5 452 vehicles, and for 16-seaters, the figure is 28 883 vehicles. Thus the total Taxi-Bus fleet size is
34 335 vehicles and its total capacity is 43 616 (in 8-seaters) + 462 128 (in 16-seaters) =505 744 passengers, all seated.
This is much more than the current fleet of 3750 buses, some 9.16 times. If we compare the number ofoffered places the ratio is quite different, as the total capacity of the current bus fleet in the LMA is 286 482

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 29

of which 156 102 (54.4%) is seated. Thus the capacity ratio of the Taxi-Bus fleet to the existing fleet is505 744 / 286 482 = 1.77.
As mentioned earlier, the intensities of the morning and evening peaks are very similar when consideringthe LMA as a whole. One consequence is that a larger share of the Shared Taxis will be doing eight-hour
shifts, with a very stable fleet size active between 07:00 and midnight, with the four-hour shifts essentiallymobilised to cover the peaks.
Figure 8. Number of vehicles (Shared Taxi) in operation

Figure 9. Number of vehicles (Taxi-Bus of capacity 8 and 16 pax) in operation

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30 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVELWith the Taxi-Bus fleets, we can see that the 8-seater fleet has a smaller but quite stable presence,
whereas the 16-seater fleet has a much larger share of the whole supply in this segment and closely follows
the global demand. The reason for this is strongly associated with the use of these larger vehicles in thefeeder services to the public transport high capacity modes stations.
Regarding parking needs, the conclusion is similar to that of earlier studies: some 95% of the total
necessary parking space would be freed up for other uses. In the city, this released space would
compromise all surface parking, plus at least half of the parking supply in parking garages. In the rest of
the LMA, the supply of public parking garages is much smaller, and that would imply possibly still leavingsome parking spaces on the surface for the shared vehicles. But those remaining parking places could easily
be selected in places that would be of less interest for the new public interest uses allocated to the released
areas.
Usage levels of the Shared Taxi vehicles
As a consequence of the above mentioned optimisation program, these Shared Taxis would have an intense
daily use of on average 389.5 km, for an average duration of operations of 8.45 h. For each eight-hour
shift, they would drive on average 313 km and in a four-hour shift on average 131 km.
In terms of average time usage, time in movement represents 82.6% of shift time, and paid time (withpassengers on board) represents 79.8% of the time in movement, the rest being time from and to depot
with no clients on board.
The corresponding average operating speed is relatively high but it reflects the good quality of the road
network in the LMA (with a strong presence of motorways) and their uncongested situation at all hours of
day and night in the simulated scenario with a strong reduction of traffic volumes.
These very high usage levels correspond to a very efficient use of the vehicles and of labour, and are at thebasis of the relatively low operating cost per vehicle.kilometre. This, coupled with the good occupationlevels, leads to very low breakeven prices per passenger.kilometre as will be shown further down.
Average occupation of vehicles throughout the day
High average vehicle occupation levels of Shared Taxis and Taxi-Buses are critical not only for the economic
efficiency of the services but also for the reductions of traffic volumes (and associated congestion) andemissions. This was the reason that led to the concept of “upgrade” from Taxi-Bus to Shared Taxi, taking
advantage of the higher flexibility of the latter, and ultimately leading to stable levels of average occupationin the two Taxi-Bus categories (which include the feeder services).
For the Shared Taxis, Figure 10 shows that average occupation never goes below 2.0 and reaches values at
or above 2.5 for a few hours during the morning and evening peaks. The daily average for the LMA is 2.3,
with the value for the peak at 3.0 as shown in Table 8.
Overall, each of the three types of shared vehicles has quite stable levels of occupation in the simulation,
which suggests that the procedures adopted in the simulation for management of the fleet size are
adequate to ensure good economic performance of those that are “on duty” at any time of the 24-hourcycle.

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 31

Figure 10. Average occupation per vehicle type

Passengers upgraded from Taxi-Bus to Shared Taxi throughout the day
This is an important feature of our concept of shared mobility solutions.It allows using a much more flexible
instrument (the Shared Taxi) to better serve specific mobility requests of clients who do not find enough“partners” with simultaneous and broadly similar travel requests to justify the deployment of a Taxi-Bus.
Since we are suggesting that those passengers pay the same as in the Taxi-Bus service with the same
origin and destination, this scheme would also raise some administrative questions of fare collection andcompensation to the Shared Taxi operator, but the fact that all of these clients and operators will be
digitally connected suggests the technical treatment of those questions manageable.
Figure 11 indicates the incidence of the service upgrade and offers interesting perspectives:

In relative terms, this scheme has a very high incidence from 02:00 to about 04:30, reachingalmost 70% of all passengers asking for a Taxi-Bus service, and rather low incidence between
08:30 and 9:30, coming even a bit lower than 10% of those passengers. Then during most of the
day it is quite stable, around 20%, then moving up and down between 20% and 40% until 02:00.



In absolute terms, we have a different perspective: the lowest numbers are in the night hours
when the percentage is highest, which is also associated with the fact that the high capacity public
transport modes are not operating. And the highest peaks are at moments of low percentage, but
associated with the general mobility peaks in the morning and afternoon periods.



The averages across the whole day are at 23.5% (average weighted by the numbers in eachperiod) and 22 376 passengers/hour.

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Figure 11. Passengers upgraded from Taxi-Bus to Shared Taxi

Note: In percentage of Taxi-Bus clients (left) and in absolute numbers per day (right) throughout the day

Modal shares throughout the day
The evolution of modal shares throughout the day in the reference case, presented in Figure 12, shows aclear dominance of private car use across all hours of the day although with some variation. First, there is amuch stronger share during the night period when there is no offer of scheduled public transport, andsecond there are strong reductions (but still retaining the strongest position and more than 50% of the total
passenger.kilometres [PKM] transported) in the morning and evening peaks. Between the peaks and in theevening after the peak its market share is always between 70 and 80%.
The next modes in terms of PKM are buses and walking, with the former approaching 20% market share at
the morning and evening peaks, and hovering around 10% for the rest of the operating period, and thelatter coming from rather low values before the morning peak to between 6% and 8% for the rest of theday until midnight.
The situation of modal shares under the shared mobility scenario is naturally quite different. Shared Taxis
are the new dominant mode, reflecting the preferences of the former private car drivers, still retaining
shares around 50% for most of the day between 08:00 and 01:00. During the night, the absence of thehigh capacity public transport modes and the greater difficulty of reaching the minimal occupationthresholds defined for Taxi-Buses, because of the lower density of demand, bring the role of Shared Taxis
to values around or even above 80% of the total mobility.
The second strongest mode is the 16-seater Taxi-Bus, which even briefly surpasses the Shared Taxi during
the evening peak. A curious phenomenon can be observed regarding the market shares of Taxi-Buses in themorning and evening peaks: in the morning peak there is a reduction of its role, particularly for the
16-seaters, partly (but not wholly) replaced by the 8-seaters, whereas in the evening peak the 8-seaters
even diminish their presence and the 16-seaters grow beyond the market share of the Shared Taxis. This
probably has to do with the concentration of demand in space and time, but only a more thorough analysis
will allow a full explanation.

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SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL – 33

Figure 12. Modal shares throughout the day (in PKM) – Reference case

Figure 13. Modal shares throughout the day (in PKM) – Shared mobility

Use of high capacity public transport modes
As mentioned above, the introduction of very efficient feeder services into the stations of high capacity
public transport modes makes their usage very attractive and significantly increases their total patronage in
relation to the current situation.
Table 12 presents a synthesis of those impacts in terms of boardings (accessing the stations by walking or
low capacity modes), separated by mode (rail, metro, ferry), with a subsequent division of the rail mode bylines.

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34 – SCALING UP: EVOLVING FROM CITY TO REGIONAL LEVEL

Table 12. Daily passenger boardings at the stations of high capacity public transport modes
Reference caseHigh capacity
line or system

Shared mobility

Access modes (%)

Variationboardings

Access modes (%)

Boardings

Private Car

Bus

Walk

Boardings

Taxi-Bus

Shared Taxi

Walk

Absolute%

33 142

5.8

39.6

54.6

56 315

57.6

17.9

24.5

23 17370

Rail - Barreiro

8 926

3.9

60.2

35.9

19 360

63.3

22.3

14.4

10 434

117

Rail - Cascais

75 291

2.6

44.8

52.6

104 992

60.0

12.1

27.9

29 701

39

Rail - Sintra

88 167

3.2

32.0

64.8

147 270

57.5

12.8

29.7

59 103

67

Rail - Beltway

11 034

4.4

25.3

70.3

15 945

53.8

16.4

29.8

4 911

45

Rail - South Bank

19 901

5.3

36.7

58.0

55 444

79.6

13.9

6.5

35 543179Metro

261 570

5.0

31.0

63.9

331 760

44.4

11.9

43.7

70 19027

Ferry

12 745

8.5

32.0

59.5

11 424

61.2

19.8

19.0

-1 321-10

Rail - Azambuja

Sub-total rail

236 461

3.9

38.1

58.0

399 326

62.1

15.0

22.8

162 865

69

Total

510 776

4.4

36.5

59.1

742 510

59.8

14.8

25.4

231 734

45

The main conclusions that can be drawn from these values are:

There is a very significant increase in the number of boardings in the high capacity public transportmodes system, with an overall gain of 45%, or 231 734 passengers per day. In relative terms thisis even more impressive for the suburban railway system, with overall gains of 69%, and inparticular for the South Bank (179% gain), the Barreiro (117%), Azambuja (70%) and Sintra(67%) lines.



There is some loss on the ferries in compensation of the huge gains made on the South Bank line.
In absolute terms, there is still a net gain of 34 222 passengers per day in favour of the highcapacity modes crossing the river, but the advantage of the rail over the ferry is that it has multiple
stations on either side of the river, thus providing more people convenient access to them, walking
on one side and via Taxi-Bus on the other (only one transfer is allowed in the model).



If we consider the catchment of the high capacity modes in the reference case, walking is thedominant mode with 59% overall and 58% in the sub-total for rail and only the Barreiro line was
below 50%. This contrasts with the scenario of shared mobility in which the dominant mode is theTaxi-Bus, with a market share of just about 60% overall, 62% in the rail sub-system, and above
50% in all systems and lines except in the metro. Walking is reduced to an average of 25% overalland 23% in the rail sub-system, with more than 40% only in the metro system. These numbers area very strong demonstration of how the high capacity modes have been under-utilised because of
the inefficient solutions adopted for bringing people to its stations.

It is interesting to look in greater detail at the cases of two rail lines with somewhat different situations
regarding the growth of their boardings and the role of shared mobility feeding services in the simulated
configuration: the South Bank line and the Sintra line, shown in Figure 14. Each line has its own schematic
linear presentation, showing the sequence of stations with bars proportional to the total number ofboardings in the reference case and in the shared mobility scenario, and the percentage of those boardingsthat are fed byshared mobility services, mostly by the feeder Taxi-Buses.
In the case of the South Bank line, with a total increase of boardings of 179%, quite strong increases canbe seen especially in the stations closer to Lisbon, with (in all stations) a high or very high part of thoseboardings being fed-in with shared mobility services.
In quite a few of these cases the percentage of boardings using shared mobility feeding services is evenhigher than the actual increase in the number of boardings. This is because a significant part of those

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boardings corresponds to modal shifts by people that already use the station but currently walk long
distances, or use Park&Ride or regular buses to get there.
In the case of the Sintra line, with a total increase of boardings of 67%, the percentage of boardings byshared mobility services is lower, in some cases "only" around 60% instead of the values around 90%
found in the South Bank line. The reason for this is that relatively dense housing developments were built
within walking distance of those stations in the period 1950-1990. The percent increase in the number ofboardings occurs precisely in the stations that were opened at later dates, for which there are fewer
residents within walking distance.
Figure 14. How feeder services affect total boardings at different stations
along a suburban railway line

The key conclusion from this analysis is that shared mobility services are highly symbiotic with highcapacity public transport modes (typically rail) in suburban areas thus equipped.. Shared mobility services
provide a much better quality of access to the stations (at reduced cost as seen below) and thus increase
the demand for that high capacity mode; and vice versa, the concentration of demand on one the extremes(start or end) of the shared ride leads to a higher occupation of the shared mobility vehicles (Taxi-Buses
and Shared Taxis) and with it improves the efficiency of their operation.
Moreover, there is an even stronger reduction of road-based vehicle.kilometres and CO2 emissions by
having a part of many trips currently done by private car replaced not by more efficient (shared mobility)
road vehicles but by electrified rail services.

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Breakeven prices for shared mobility modes
Since the general outlook of the operations of the shared mobility system at the scale of the LMA aspresented in this report is quite similar to that presented by ITF (2016a), the breakeven prices for per
passenger.kilometre (PKM) in each of the two new modes are also very favourable. If fact, they are even a
bit lower for the Taxi-Buses in the LMA because of the higher efficiency achieved through the higher
occupation rates with the feeder services.
Since prices per PKM in the current situation were taken from company reports and thus are based on
actual travelled distances, in the earlier reports we were calculating the breakeven price per PKM also using
the travelled distances by the passengers in the simulated scenarios.
But ride sharing will generally lead to a higher ratio between the travelled distance and the direct routedistance (and the travelled distance may change from one day to the next), so in this report we calculate
the breakeven price per PKM in the simulated configurations on the basis of their direct route distance. Thisis the reason why the breakeven prices per PKM (in percentage of current prices per PKM) presented in thisreport are higher than those in the 2016 report. But in fact, comparing strictly on the same basis, thebreakeven prices for the LMA (covered in this report) are a bit lower than those for the city (covered in ITF,
2016a).
Using the same approach for consideration of capital costs, operational costs (including labour), back officecosts and profits as in that earlier report, the average breakeven prices estimated applicable per PKM in the
LMA (which is supposed to be identical across the whole geographical area) are, for the Shared Taxis,
30.5% of the current equivalent price for the single user taxis (and still lower than the current equivalent
price for public transport, at 73% of that price).
For the Taxi-Buses, the breakeven price per PKM is 40.2% of the current equivalent price paid by the public
transport user and 26% of the current equivalent total cost considering the price paid by the user plus thesubsidy paid by the State (Metropolitan Area Authority) to the operators.
These breakeven prices obtained in the simulation, using realistic assumptions on production factor prices,
are very encouraging regarding what could be the public acceptance of these high quality services.
To conclude, it is possible to state that, in spite of the much lower population and job densities, full
deployment of shared mobility solutions for the Lisbon Metropolitan Area, with still very high standards ofquality of service, would have the same kind of macro impacts as those presented in the earlier report (ITF,
2016a) for the Lisbon city centre including:very significant reduction of traffic volumes and CO2 emissions

very significant increases of accessibility both in average and its equitable distribution

quite efficient use of the capacity in the shared vehicles, with the corresponding impacts on theprices per passenger.kilometrevery strong reduction of parking needs across the whole of the LMA, allowing the corresponding
release of the associated public space to other uses of much higher amenity.

On top of these impacts, there would be a significant contribution for a higher usage of the available
infrastructure capacity in the high capacity public transport modes (bringing them to patronage levels ofsome 25 years ago). On the other hand, the availability of these high capacity modes is essential to achieve
the system’s high levels of efficiency in spite of the lower densities, as it creates the natural demand
concentration points that quite naturally lead to high occupation of the (quite small) shared vehicles.

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3. How to deploy shared mobility services? Lessons fromtransition and market structure scenarios
The results discussed previously derive from a scenario that assumes 100% replacement of all car and bus
trips by a mix of new shared modes, rail-based public transport and walking (cycling is included in themodel but was not activated due to extremely low mode shares). As such, it is a scenario that indicates thetypes of changes and the potential magnitude one might see from the adoption of shared mobility services
at the metropolitan scale, rather than a forecast of a real situation. This type of scenario is interesting as aconversation starter around the potential for shared mobility but it is not a blue-print for implementing such
systems. Furthermore, the impacts experienced at full implementation levels are likely to be quite different
from those experienced during the transition as the uptake of these services grows and as new markets
develop around the delivery of these services.
There are multiple potential uptake pathways that could be explored. For the purposes of this work, we
selected two to test, both associated with plausible transport policy approaches: The first looks at gradually
increasing the uptake of shared mobility according to a plausible early adopter pathway, guided by specificstep-wise policy interventions. The second at the impact that changes in market structure (monopoly vs.
multiple service providers operating under different market rules or business cases) have on fleet
requirements and overall travel.
All of these scenarios result in less efficient system performance than the 100% “all-in” scenarios modelledpreviously, but they are worthwhile investigating since system efficiency is only one of a number of valid
criteria that authorities might use to guide the deployment of such services.

Targeted early adopter pathways
An early adopter pathway would deploy shared mobility services in a targeted, policy-based way so thatthey represent an attractive, or even a better alternative, as compared to current types of trips and use
cases. In this section we explore some of these targeted early adopter pathways. For this we made use of
the ranking of users by propensity to consider modal shift (as presented above in the section “Allocation of
trips to modes”).
In the first instance, we test a scenario at the level of the greater LMA where shared mobility services
replace all bus services with Taxi-Bus or feeder services, and single user taxi services with Shared Taxi
services. These are pure supply-side measures than can be enacted by the metropolitan agency in chargeof mobility, necessarily preceded by national legislation to authorise such services as they currently do notexist. Trips corresponding to these types of supply (scheduled buses and single user taxis) account for
approximately 26% of all trips in the LMA and for 21% of the overall passenger.kilometres travelled in the
LMA.
Table 13 shows the results for this targeted trip replacement scenario. In this scenario, all trips previously
taken by bus (77.3% of the trips targeted in this scenario) are allocated to either Shared Taxis or feeder
services to high capacity modes of public transport, whereas slightly less than half of those travellers using
taxis (representing 10% of the trips targeted in this scenario) are allocated to Taxi-Buses or feeder
services. The reason for this partial shift from single user taxis to Taxi-Bus or feeder services is thatcurrently people may have to use a taxi because no suitable public transport service is available at theplace and time they need it. This scenario is designated as “baseline adoption” scenario.

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Table 13. Targeted early adopter scenario: Baseline adoption scenario
Mode inreferencescenario(2011)

Share of each mode (allits trips) in trips targeted
for this scenario (%)

Share of those trips by mode in targeted early adopter sharedmobility scenario
Shared Taxi (%)

Taxi-Bus (%)

Feeder service (%)

Taxi

10.0

56.5

39.7

3.8

Bus

77.3

0.0

82.4

17.6

Bus + highcapacity public
transport

12.7

0.0

95.8

4.2

5.7

79.8

14.5

Total trip mode share for shared mobility
scenario

An interesting perspective of the impacts of this baseline adoption is obtained through the average of travel
time ratios between this scenario (for Taxi-Buses) and the reference case (for buses), equal to 0.42,
meaning that the travel time in this new form of demand-responsive public transport will typically be lessthan half that of the most similar current situation. The main reason for this improvement is that there areno transfers, and to a smaller extent there is less congestion on the network. Some further impact analysis
is developed after presentation of the modal shares in the three successive steps of this adoption pathway.
In a second scenario along this path, we considered stronger adoption of shared mobility services with thesame idea of targeted adoption for other early adopters whose use-cases and trips could benefit from theswitch, aiming at twice the number of transferred trips from the previous scenario. In this scenario, andfollowing the same ranking of users as before, we replaced very long walking trips (greater than 3 km) andtrips taken by car where drivers had to pay for parking at the destination, in addition to the trips replacedin the previous scenario.
Returning to Table 6, by showing the successive user groups in terms of propensity to consider shifting toshared mobility modes, it can be seen that whereas the baseline adoption scenario (25.88% ~ 26% of all
trips) is fully "loaded" with trips from classes 1 and 2, the double baseline adoption scenario fully uses class
3 and 4 (another 7.66%). However, the double baseline adoption scenario still requires another 18.46%(52% - 25.88% - 7.66%) of all trips from class 5, that is 18.46% out of a group of 45.10% of all trips, or
40% of this group of trips in which parking had to be paid for and had stays shorter than three hours.
These shifts seem quite natural: long walking trips (4.5% of total walking trips are longer than 3 km) aremostly done for lack of a suitable public transport service and so would very likely opt for the new TaxiBuses or Shared Taxis; and we could expect that a good proportion of car trips involving the need to pay
for parking are also likely amenable to change to the shared modes given the high quality and low pricesthat are expected there.
To help shift from cars to shared modes in a real situation, public authorities must calibrate interventions,both from the side of disincentives for using private cars (by reducing parking supply, increasing price or
reducing maximum allowed duration) and from the side of quality of supply of the new shared modes. In
this report, the double (and then the triple) baseline adoption scenario are presented as examples of ordersof magnitude of modal shifts, without any need to be very specific about the type and intensity of themeasures necessary to reach the levels of those modal shifts used in the modelling.
Table 14 shows that it is necessary for just under 50% of private car trips to adopt shared modes in orderto double the fraction of total trips adopting the shared mobility options in relation to the baseline adoption
scenario, as slightly more than half (51.3%) of those trips by private car will remain in that mode. Thiswider set of trips accounts for 52% of all LMA trips and 50% of the overall passenger.kilometres travelled in
the LMA. This is designated “double baseline adoption” scenario.

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Table 14. Targeted early adopter scenario: Double baseline adoption scenario
Mode in reference
scenario (2011)

Share of eachmode in trips
targeted for this
scenario (%)

Private car (PC)Remaining share in eachmode of those not yet
adapted to sharedmobility scenario (%)

Walking(%)

SharedTaxi(%)

TaxiBus(%)

Feederservice
(%)

43.20

0.00

63.80

26.10

10.10

51.3

1.00

0.00

48.90

46.50

4.50

49.0

5.00

0.00

56.50

39.70

3.80

0.0

38.60

0.00

0.00

82.40

17.60

0.0

4.50

7.60

0.00

74.50

17.90

87.7

0.00

0.00

7.20

92.80

91.5

0.00

0.00

32.00

68.00

83.0

0.00

0.00

95.80

4.20

0.0

0.30

30.80

55.10

13.70

-

Motorcycle
Taxi
Bus

Share of those trips by mode in targeted
early adopter shared mobility scenario

WalkHigh capacity public
1.10
transport (metro, rail,
ferry)
PC + high capacity
0.30
public transport
Bus + high capacity
6.30
public transport
Total trip mode share for shared mobility
scenario

Finally, the third targeted early adopter pathway, aiming at three times the number of trips of the baseline
adoption scenario, considered for mode change all of the trips taken in the entire LMA except those by car
drivers who had free parking at their destination, and those made on motorcycle. This scenario recognisesthat these travellers might be most resistant to giving up the convenience and (relatively) low cost of
personal mobility that they currently experience. Depending on specific arrangements in place for theparking supply to those car drivers (some of them park in private spaces while other use public parking
garages and have their subscriptions paid by the employer), incentives for the mode shift towards sharedmobility solutions will be different. Specific consideration of the trips by motorcycles would also be required,
particularly if their market share grows as a reaction to the limitations on use of private cars.
Table 15. Targeted early adopter scenario: Triple baseline adoption scenario
Mode inreferencescenario(2011)

Private car (PC)
Motorcycle
Taxi
Bus
Walk

Share of trips
in each modetargeted forthis scenario(%)

Share of those trips by mode in targeted early
adopter shared mobility scenario
Walking(%)

SharedTaxi (%)

Taxi-Bus(%)

Feederservice (%)

55.4

0.0

61.5

28.7

9.7

1.2

0.0

49.9

45.3

4.9

3.3

0.0

56.5

39.7

3.8

25.7

0.0

0.0

82.4

17.6

6.5

4.4

0.0

78.2

17.4

0.0

0.0

6.2

93.8

0.0

0.0

31.5

68.5

0.0

0.0

95.8

4.2

0.4

54.9

72.8

21.9

High capacity
2.6
public transport(metro, rail, ferry)
PC + high capacity
1.1
public transport
Bus + high4.2
capacity public
transport
Total trip mode share for sharedmobility scenario

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Remaining share in eachmode of those not yet
adapted to sharedmobility scenario (%)

6.3
10.8
0.0
0.0
73.5
70.7

0.0
0.0

-

40 – HOW TO DEPLOY SHARED MOBILITY SERVICES? LESSONS FROM TRANSITION AND MARKET STRUCTURE SCENARIOSThe trips considered for mode change in this scenario account for 78% of all trips taken within the LMA and
82% of the overall passenger.kilometres travelled in the LMA. This is designated “triple baseline adoption”
scenario.
Table 16 presents a few synthetic indicators of the mobility performance in each of these targeted early
adopter scenarios.
Table 16. Synthetic indicators for gradual adoption of shared mobility
Indicator

Reference scenario
2011
(%)

Baseline adoption
scenario(%)

Double baseline
adoption scenario(%)

Triple baseline
adoption scenario
(%)

67.0

55.1

27.4

5.2

VKM (weighted)
variation relative toref. case

-

-2.6

-26.5

-33.1

CO2 emissions
variation relative toref. case

-

-15.3

-36.9

-47.9

% of PKM in
individual modes(PC or motorbike)

Note: PC = Private car; PKM = passenger.kilometre; VKM = vehicle.kilometre.

Taking these successive adoption scenarios as part of a transition pathway, it is possible to produce someconclusions:

In the baseline adoption scenario, as shown above, there is a considerable reduction of travel times
for bus users when they convert to Taxi-Buses, but that does not seem to be enough as this
scenario has virtually no impact on traffic volumes and achieves a small reduction of CO2 emissions(15.3%).
There is an apparent reduction of the percentage of passenger.kilometres (PKM) done in individual
modes, but this is associated with the overall increase of PKM (due to shift from long walks to
Taxi-Buses) and to the longer routes taken by Shared Taxis in comparison with single user taxis,
while the absolute number of PKM in those individual modes stays the same.
As such, while this configuration may still have some political cost associated with imposingchanges (in this case from scheduled buses to Taxi-Buses and from single user taxis to Shared
Taxis) it seems to bring no visible benefits to the main social indicators and so should be seen as aless interesting first step in the adoption pathway.



The double baseline adoption scenario has some political costs for acceptance, as it introduces not
only new services but actually significant restrictions on parking. If it comes accompanied by agood supply of Taxi-Bus and Shared Taxi services this may be considered as a socially justifiablemeasure to reduce congestion and emissions. The simulation results are quite encouraging in this
scenario, as some rather visible benefits are already to be expected: Passenger.kilometres inindividual transport modes are half those in the baseline adoption scenario, but more importantly
traffic volumes are reduced by 26.5% (no congestion remaining) and CO2 emissions are reduced by37%.



The triple baseline adoption scenario is much closer to the full deployment in terms of thepenetration of shared mobility solutions and has correspondingly good performance indicators, bothin terms of traffic volumes (33% reduction from the reference case) and CO2 emissions (just above
half of the reference case). But getting there requires political decisions that are only acceptable

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when there is great public recognition of the merits of this kind of solution, which is only to be
expected after the double baseline adoption has shown its merits and the intention to proceedalong that path has been announced.
In synthesis, this analysis shows that it is possible to have a phased implementation of an urban mobility
system based on shared mobility solutions, but also that careful analysis is necessary before such a process
starts: in this example, the results of a three-phase process showed that there is a minimum intensity of
publicly perceptible change in the first step to achieve credibility. Because of that requirement, a two-phaseprocess would be the best option as the first phase shown here would deliver very limited results. But
transition processes must always be carefully designed for each city; this is only presented as an exampleof the need to deliver perceptible results already in the first phase of implementation.
The scenarios described here have far-reaching impacts beyond those linked specifically to transport andmobility. They involve significantly different lifestyle configurations than what people experience at present
and will give rise to new opportunities and constraints for many commercial and public actors, including
many that do not even exist today. Cities themselves will look quite different – especially as concerns thestreetscape and the quantity and quality of public space. Managing the transition described in this reportwill therefore require addressing a broader range of considerations and preparations beyond the strict
transport-related measures described above.
When looking at how these early adopter scenarios might play out in real life, it is important to understandthat early experience with the new services will likely influence later adoption rates (or even whether theservices become commercially viable). If early adopters are disappointed as they face long waiting times,
long travel times and low reliability, then it is likely that their experience will slow or stop further adoption
by new users. Conversely, if early adopters are satisfied and report very low response times and highreliability, uptake will likely accelerate based on those experiences. This suggests that early implementationof shared mobility solutions should be centred on use cases or in contexts where extremely high quality ofservice – especially compared to existing options – is virtually guaranteed. It also suggests that if publicauthorities are invested in ensuring the broader uptake of these services, they should implement policies
that would incentivise their use.

Alternative policy scenario: Access restrictions of private cars to the city of
LisbonAn alternative scenario was also developed, which considered applying a low emission zone within the city(municipality) of Lisbon, with constraints on parking and traffic circulation within the city, leading to
adoption within the city of a similar behaviour to that described in the double baseline adoption scenario.
(For this exercise, we assume that outside of the Lisbon city centre the current [2011] situation still holds).
Additionally, trips by non-residents of Lisbon currently performed in private car or motorcycle and entering
Lisbon are constrained, possibly depending on their licence plate number. We simulated two scenarios: thefirst where there would be an access ban for one weekday, the second where there would be an access ban
for three weekdays.
For those whom entry into the city by private car or motorcycle is not allowed, those trips are served by agroup of park and rideshare stations where private cars and motorbikes can be parked (see Figure 15).
People that use this solution travel to their final destination either by Taxi-Bus services or via metro or rail(six of the stations in Figure 15 have integration with rail and metro). Mobility between other areas of themetropolitan area was kept the same as in the reference scenario.

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42 – HOW TO DEPLOY SHARED MOBILITY SERVICES? LESSONS FROM TRANSITION AND MARKET STRUCTURE SCENARIOS

Figure 15. City of Lisbon cordon and park share locations

Source: ITF, Map tiles by QGIS.

Table 17 presents some of this scheme’s performance results for the city. The two first columns serve as
the framework, the first one related to the reference case and the second related to the full adoption of theshared mobility solutions, as presented in Chapter 2. The two rightmost columns show the values to be
expected with - in both cases - adoption within the city of the measures corresponding to the double
baseline adoption scenario presented in the previous section. The fourth column shows one day ofprohibition of entry of private cars of non-residents into the city and the fifth column three days of suchprohibition.
Table 17. Performance indicators for the scenarios of action only by the Lisbon municipality
Indicator% of PKM in
individual modes

Reference scenario
2011

Full adaption ofshared mobility

Results for double
baseline adoption
scenario

Results for triple
baseline adoption
scenario+1 day access ban+3 day access ban

67.0

0

39.2

16.1

% of VKM (weighted)
variation relative toref. case

-

-50.8

-17.8

-29.9

% of CO2 emissions
variation relative toref. case

-

-63.5

-21.7

-37.2

(PC or motorbike)As defined above, the double baseline adoption scenario includes the following measures:

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shared mobility services replace all bus services with Taxi-Bus or feeder services

Shared Taxi services replace all single user taxi services



trips taken by car where about 40% of the drivers that had to pay for parking at the destination aretransferred to Shared Taxis or Taxi-Busesvery long walking trips (greater than 3 km) are supposed to also get transferred to Taxi-Buses.

While it is obvious that the traffic and emissions indicators under this geographically delimited scheme are
not as good as under the full adoption of the shared mobility solutions across the LMA, the measuresconsidered here can produce quite interesting results for the city.
In fact, in the first of the scenarios tested here (one-day car access ban for non-residents), the percentage
of PKM from individual modes is reduced to 60% (39%/67%) of the reference case, traffic volumes are
reduced by 18% (very little congestion left) and CO2 emissions by 22%.
The second scenario tested, and possible second phase in this process (three-day car access ban for
non-residents) naturally achieves stronger results: the percentage of PKM in individual modes goes down to
24% (16% / 67%) of the reference case, while traffic volumes and CO2 emissions reduce by 30% and 37%
respectively.
These results indicate clearly that a phased approach is possible and can be successful in this scenario of ageographically delimited approach in which the Lisbon municipality decides to move forward by itself
without being accompanied by the other municipalities in the LMA.

Market structure scenarios
Two scenarios with different market structures are run as an alternative to the full implementation ofshared mobility solutions in the LMA under a single monopolistic dispatcher, where more than one
dispatcher (operator) is available in the market and there is some market differentiation.
For this analysis three different market players (dispatchers) were designed, based on preference of clients.
These dispatchers have the following features:Dispatcher 1: Runs (controls) Taxi-Bus services in the LMA as well as a fleet of Shared Taxis which
are called upon in the case that a decision to upgrade a Taxi-Bus client is made. The lexicographicalchoice of Taxi-Bus or feeder services is used to trigger requests to this dispatcher. This operator issimilar to the central dispatcher used in previous exercises, but just with a smaller market size
(71% of the market).

Dispatcher 2: Runs (controls) conventional Shared Taxi services in the LMA. This dispatcher getsthe clients that have selected Shared Taxi in the lexicographical procedure (25.4% of the sharedmobility market).

Dispatcher 3: Runs (controls) higher standard Shared Taxi services in the LMA with a vehicle
capacity of 3 and more demanding specifications in terms of waiting time and time loss tolerance(-50%). This dispatcher gets clients randomly selected from the 30% of car users in the higher
income group (larger than the 75th percentile of incomes), with no public transport pass. The
clients represent 3.4% of the number of trips and 4.1% of the passenger.kilometres.

The obtained market segments by corridor are presented in Table 18. In this scenario, even if the transport
services available are exactly the same as under the full adoption of shared mobility as described in Chapter
2, there is a considerable loss of efficiency resulting from the three-way split of the market. The reductionof traffic volumes from the reference case is only 26.6% instead of 51%, and the reduction of CO2
emissions is 31.3% instead of 64%. We did not calculate the breakeven prices for each of the supply

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segments in this scenario but all of them would inevitably be much less attractive than those computed forthe full adoption scenario.
This happens because of the loss of integration in the supply of Shared Taxis (separate operators for theoriginal clients and for the upgraded clients) so that in many cases, a Shared Taxi would be available invery good conditions of time and place to serve a client. But it was managed by a different dispatcher thanthe one selected by that client, and as a consequence, another vehicle, with a longer route and very likely
with lower occupancy, would need to be mobilised.
Table 18. Performance indicators for a scenario with three dispatchers
Indicator

Reference scenario
2011

Full adaption ofshared mobility with
one dispatcher

Full adaption ofshared mobility withthree dispatchers

67.0

0

0

% of VKM (weighted) variation relative toref. case

-

-50.8

-26.6

% of CO2 emissions variation relative toref. case

-

-63.5

-31.3

% of PKM in individual modes(PC or motorbike)

The question of how many operators of the Shared Taxis or Taxi-Buses was found to be non-critical. The
authority must specify minimum (or compulsory) standards for the vehicles to be used, as well as thenecessary qualifications for the drivers, and the requirements for data exchange between the vehicles andthe dispatcher. Because of the dynamic assignment of new clients, vehicles must follow the route specified
by the dispatcher, as well as return to the depot assigned by the dispatcher when they end a trip and haveno assigned clients or no clients on board. The way ownership of the fleet is distributed by the number ofoperators is of minor importance in this context.
Take for example a situation with one dispatcher and multiple competing operators, each with somedifferentiation of its offer (e.g. vehicle brand, price, loyalty programs), a client would still use the app linked
to the dispatcher and would get information on the “best” offers available for the requested ride, indicating
for each offer the operator brand, the estimated waiting time, the estimated arrival time at destination, andthe estimated price. The client would then choose which of these offers is preferred and the dispatcher
would issue the order to the corresponding operator.
It is important to consider encouraging a healthy competitive marketplace for such services, which is why
such a solution could, while delivering the benefits of competition between operators, still retain theefficiency gains of having a single dispatch function managing real-time information on available rides.
Whatever the market structure put into place, it will be important to manage empty kilometres made by
Shared Taxis as they roam to best position themselves in anticipation of future requests.
In the design concept presented here, the dispatcher centralises real-time information about the locationand occupancy of Shared Taxis. Predictive decision-making algorithms using historical use data informs thedispatching function as it seeks to adapt to demand patterns throughout the day. This task plays a key role
in reducing vehicle.kilometres made by empty Shared Taxis. Centralising empty-kilometre reduction
through a single dispatcher reduces inefficiencies arising from guess-inspired roaming.
From the perspective of overall system efficiency, strict adherence to dispatch routing is necessary.
Dispatch compliance may be difficult to enforce where there are multiple operators, but options such as
differentiated tariffs set by relevant authorities could limit such a practice.

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Alternatively, we also assessed the impact of remaining with a single operator but considering a new classof service – exclusive occupancy of a Shared Taxi, by specific request of the client. In the model weassumed these are the same clients that would use Dispatcher 3 in the previous example (3.4% of thetrips, 4.1% of the PKM). Table 19 presents the corresponding results.
Table 19. Performance indicators for a scenario with a single dispatcher and the possibility of exclusive
occupancy in Shared Taxi services
Reference scenario
2011

Full adoption ofshared mobility

Shared services (Shared
Taxi with single use)

67.0

0

4.1

VKM (weighted) variation relative toref. case

-

-50.8

-43.9

CO2 emissions variation relative toref. case

-

-63.5

-56.6

% of PKM in individual modes(Shared Taxi alone)

Allowing the provision of exclusive occupancy of taxi services responds to a market segment that inevitably
will exist and can be very important (at least in the introductory stage of shared mobility) to encourage
general acceptance. Moreover, it would be very difficult to enforce such a prohibition, since in significant
parts of their duty Shared Taxis only carry one passenger. But the impacts in terms of traffic volumes andCO2 emissions (in relation to no possibility of exclusive services) are not negligible: allowing exclusive
occupancy service in 3.4% of the trips, (representing 4.1% of the PKM) cuts reductions of traffic volumes
and CO2 emissions by 6.9% (a factor of 1.7 to that 4.1%).
The price for this kind of service must naturally be higher per passenger.kilometre to compensate theoperator for the potential loss of revenue from other passengers that might share part of the ride. Thisamplified impact on efficiency, however, leads to the recommendation that authorities calibrate the pricedifferential between this and the regular Shared Taxi service so that its demand stays at a modest level.
These two tests give two clear indications. First, the loss of efficiency associated with a split of the dispatch
function across multiple providers (three in this test) is considerable. While there could be some advantages
in terms of competitive innovation in services provided, these results suggest that system efficiency is bestdelivered through a single dispatch function, possibly a monopoly regulated by a public authority under atendered concession contract for some years. Second, allowing exclusive occupancy of Shared Taxis
responds to a natural demand but it must be managed through the price differential to ensure that overall
impacts on the system-wide efficiency are not too high.

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4. Can shared mobility serve people with special mobility
needs?
One important aspect in any implementation of shared mobility systems will be the extent to which they
can provide equal or better mobility outcomes for all people, not just for one portion of the population. In
particular, if these systems cannot provide good access for those with physical, sensory or cognitive
impairments – impairments that many in the general population will experience at specific periods or more
systematically as they age – then it is difficult to see how these systems will gain broad support, especiallyfrom the governments that will likely retain regulatory oversight and licensing authority over these services.
ITF (2016b) proposed a framework to value the social and economic benefits of improved access, includingtime savings, greater convenience, more employment opportunities and savings across the social care andhealth sectors. International evidence presented in that report also points to the potential of accessible
transport systems to generate large economic benefits. It is also important from a human perspective; canthese services provides better outcomes for those who face difficulty walking or using cars, buses, trains
and metros today? If so, then their deployment should become a priority.
This section presents a first simulation of how demand-responsive, shared mobility solutions could be used
to address the access and mobility needs of people with some form of significantly reduced mobility. For the
purposes of this simulation, we have focused on transport of individuals using wheelchairs or those with
reduced mobility due to chronic or temporary disabilities or special mobility requirements. Given the
exploratory nature of this exercise and the lack of specific data, the simulation treats all these cases in asimilar manner:All are served by door-to-door Shared Taxis (no Taxi-Bus services).

All are served with a specially adapted vehicle with a ramp (or a lift), a wider door (on the side oron the back) and a safe place to anchor a wheelchair (or a baby pram or larger shopping cart),
along the lines required by the US ADA (Americans with Disabilities Act) or other analogous
national standards. The same kind of vehicle as for the general population is used as a basis for theaccessible service. These vehicles are 8-seater minivans with accessible doors and capacity for fivepassengers. The vehicles adapted for service to this segment also perform service to passengers
without special needs in the same conditions as the non-adapted vehicles.

Boarding and alighting time for each of these passengers is extended by 30 seconds on average in
relation to that adopted for the general public.

These are rather generic attributes and the concept is rather simple: include people with special mobility
needs in the shared rides scheme that are made available to the whole population. Given the nature of theexercise and the absence of specific information on the distribution of these special needs across the
population represented in the mobility databases (and consequently in our synthesis population), we madea set of simplified assumptions:

We assumed that the percentage of people with special mobility needs was 10%. Whether thisvalue is above or below the real situation in any particular city will depend not only on thecharacteristics of its population but also on what may be understood as "with special needs". We
expect that it is sufficiently realistic to be accepted as approximately right for the targeted
population we are proposing in the simulation, i.e. wheelchair users and individuals withsignificantly reduced mobility.

The shared mobility simulation approach employed in this case includes Shared Taxis, in addition toother choices including Taxi-Buses, walking and metro or regional rail-based public transport.

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Whereas all these choices are available to the rest of the population, our scenario calls for the useof Shared Taxis only by those with accessibility needs. They therefore represent a disproportionate
share of Shared Taxi users and account for about 35% of all Shared Taxi trips.


The allocation of the 10% of travellers with accessibility needs in our synthesis population wasconditioned by several other variables for which we had information:oPeople with more than four trips per day were excluded from this segment.

o

The probability of having special mobility needs was indexed to age: people under the ageof 55 were allocated a weight of 1, those between 56 and 75 a weight of 2, and thoseabove 76 a weight of 4.

oPeople driving their own car are ranked after all the others.

o

We went through the whole synthesis population and sorted according to the resulting
combined weight of these different aspects. Once the percentage of the population to beconsidered with special needs is defined by the user at the beginning of the simulation,
those in the high end of the list down to the corresponding number of people are labelled
as “with special needs” and those below as “no special needs”.

Since the beginning of the ITF work on shared mobility solutions critical importance was given to the issueof quick availability of Shared Taxis as specified through the maximum allowed waiting time and total losttime (waiting + detour times). This was motivated by the need to get strong acceptance of the new
solutions by current car drivers and thus achieve a decisive modal shift. This aspect of quality of service
naturally has an impact on the cost of provision of that service, and after some research it was found thatthose maximum times should vary with the trip length.
Given the much higher costs of accessible vehicles, a critical question in designing these services is related
to the fleet: what fraction of the whole fleet should be dedicated to accessible vehicles? This hasrepercussions not only on costs but also on maximum waiting times for the people with special mobility
needs.
Many cities around the world strive to make all taxis and public transport services accessible to people withspecial mobility needs. In other instances, cities provide some form of paratransit service especiallytargeting citizens with accessibility needs that cannot be readily met by taxis or by public transport. Suchservices are often provided in a demand-responsive manner, but with long response time, typically at least
one hour but sometimes even one day, much higher than the headways of scheduled public transport or
response times of regular ride services. The reasons for this lower quality of service are associated with theseparate, dedicated provision of that service to a relatively small part of the overall demand for public
transport (and to a much lesser extent, to the additional cost of the adapted vehicles).
This work explores how much shorter that response time could be, leveraging potential scale effects of a
Shared Taxi fleet in a system configuration where vehicles are shared by people with special accessibility
requirements and those who are fully mobile.
In our earlier studies, maximum acceptable waiting times (as well as the total lost time = waiting time +detour time) were found to be preferably dependent on trip length in our other work on shared mobility
systems. Because of this, the exploration of "How much shorter could the response time be?" wascharacterised in terms of the ratio between the maximum acceptable waiting times in the segments ofpeople with reduced mobility and with full mobility, for various ranges of trip length.
It is worthwhile thinking a bit more about those additional costs: if the maximum acceptable waiting times
for people with and without special mobility are the same, many adapted vehicles will be necessary. But as
the ratio between these maxima grows (say up to a ratio of 2.0), the number of adapted vehicles necessary

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will diminish (reducing capital costs). But serving those passengers with special needs may bring longer
empty rides and/or may force them to longer waiting times. The value of the additional waiting time
imposed on those passengers must also be taken into consideration.
So, we ran the simulation model for a number of different situations regarding fleet composition:No adapted fleet (as if there were no passengers with special mobility needs) - this is the referencecase for computation of the cost differentials.

All adapted fleet - this is the maximum flexibility case, offering equal quality of service to allclients. It also has the maximum capital cost, but possibly not the highest operating cost.

Partly adapted fleet – this includes three different ratios between the maximum acceptable waitingtimes (RMAWT) for the two population segments (respectively 1.0; 1.5; 2.0). The ratio for total lost
(waiting and detour) times would be the same.

For each of these ratios we computed three cost components:


Capital costs (amortisation + maintenance + insurance) of the Shared Taxi fleet.



Operating costs of the Shared Taxi fleet.

Value of time lost waiting by the people with special mobility needs (considering only the time
above the maximum allowed for the people without special mobility needs): The value of time
adopted was EUR 0.05 per minute, similar to the typical values calibrated in other studies in the
LMA (Eiró and Martinez, 2014).

For the cost calculations, the assumptions about the accessible minivans used as Shared Taxis were thefollowing:

additional purchase price 40% above the normal purchase price of the same base vehicle (as used
for the non-accessible Shared Taxis)

fuel costs 10% above those for the non-accessible minivans

maintenance and insurance costs in the same percentage of the purchase cost

amortisation period same as for non-accessible minivans.

As in earlier studies, the simulation is run with a Shared Taxi “generated” in the system when a request
cannot be satisfied with the already available supply and respecting the maximum waiting time, with the
only difference that, depending on the case being run, the type of vehicle may be generic or accessible:

Scenario A: When there is no accessible fleet, no clients are considered as having special mobility
needs and all generated vehicles are generic (non-accessible).



Scenario B: When all the fleet is accessible, the generated vehicles are accessible irrespective of
the mobility needs of the client triggering that generation.



Scenario C-E: In the cases of partially accessible fleet, when the client making that request has
special mobility needs, an accessible vehicle is generated; otherwise a generic vehicle is generated.
Thus, in this group of three cases, the fraction of accessible vehicles in relation to the whole fleet of
Shared Taxis, and the overall size of the Shared Taxi fleet, are a function of the specifications
regarding the maximum acceptable waiting and total lost time of the clients with the special needs
in relation to the general population.

As mentioned above, the first runs of the simulation had the purpose of making the basic decision regarding
fleet composition, based on the costs and service quality (maximum waiting times) associated with each

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option. To facilitate perception of these results in a meaningful scale, the unit of all costs is EUR per
passenger.kilometre of the shortest route possible for every trip.
For reasons of computation time, this initial analysis regarding the composition of the fleet covered only the
territory of Lisbon city centre, with a much stronger concentration of elderly people. The results for each of
the cost components indicated are shown in Table 20.
Table 20. Characterisation of tested scenarios for accessible Shared Taxi services
Fleet scenario

A

B

C

D

E

No accessible fleet

All accessible fleet

Partly accessible
fleet: RMAWT 1.0

Partly accessible
fleet: RMAWT 1.5

Partly accessible
fleet: RMAWT 2.0

3997 / 0/ 3997

0/4190/4190

1541/2811/4352

1653/2499/4152

1677/2358/4035

Capital cost

0.122

0.133

0.130

0.128

0.127

Operating cost

0.487

0.533

0.519

0.512

0.509

Value of extra
waiting time

0

0

0

0.006

0.009

Sum of the twomoney costs

0.609

0.666

0.649

0.640

0.636

Sum of the threemoney costs

0.609

0.666

0.649

0.646

0.644

Fleet size
(Reg /Adapt / Tot)

Note: Table calculates EUR per vehicle.kilometre, shortest route.
RMAWT=ratios between the maximum acceptable waiting times.

From these results, it is possible to see that the lowest cost option for delivering accessible services is
scenario E which costs EUR 0.027 more than the no accessible vehicle scenario – or only 4.4% more. This
scenario, however, implies that people requiring accessible Shared Taxis wait up to twice the maximum
acceptable waiting time as those with no special mobility needs and requires that 58.4% of the total Shared
Taxi fleet be accessible (2 358 out of 4 035 vehicles).
Having a wholly adapted fleet (scenario B) is not only more flexible to operate, particularly if considering an
ageing population, but more equitable. And the additional costs implied in relation to the reference case(scenario A) is EUR 0.057, i.e. a 9.3% cost increase. The cost differential from comparing scenario B to thelowest cost scenario (E) is only EUR 0.030 (4.7%). But taking into account the value of extra time lost by
those clients, the difference is only EUR 0.22, i.e. 3.4% of that lowest cost.
In synthesis, the lowest additional money cost of providing accessible service to this segment is EUR 0.027
with solution E. Providing a fully accessible service costs another EUR 0.030. From the total
EUR 0.057 = 9.3% cost increase associated with full service (scenarios A-B), roughly half is associated with
providing accessible service in the first place service, and another half for extending that service to thewhole of the fleet.
Based on these findings, the rest of our analysis concerns scenario B – the scenario where every Shared
Taxi deployed is fully accessible. Even for this relatively more expensive service, the final delivered service
is still much less expensive than the current price of a single-user taxi, and in fact lower than the current
public transport price (see below).
Scenario B is also much more compatible with transition arrangements in the case of a gradual deployment
since the fleet is homogeneous. In the case of a partly accessible fleet, and for a situation in which only ashare of overall trips is served by these modes (e.g. as described in the Chapter 3), the resulting
percentages of accessible vehicles would have to be higher than here to get to similar service quality. Thisis because the quality of service to people with special mobility needs depends essentially on the absolute
number of Shared Taxis deployed.

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50 – CAN SHARED MOBILITY SERVE PEOPLE WITH SPECIAL MOBILITY NEEDS?

Costs and their impact on breakeven prices
Any political choices about provision of mobility solutions for people with reduced mobility will necessarily
consider the quality of service provided, the costs for providing that service and the fundamental right forequal access. A second order decision will then have to be made about what part of those costs is
supported by the direct beneficiary (the travelling citizen) and what part is borne by society.
Despite the exploratory nature of this work, it is useful to include a breakeven price analysis similar to what
has been done in our other reports on shared mobility solutions. As in earlier ITF reports, these breakeven
prices are calculated as 25% above the full capital and operating costs, so as to cover back-office costs andprofits.
As outlined earlier, wheelchair accessible vehicles cost more in capital and operations, and may experienceslightly longer boarding and alighting times. For these two reasons, delivering a more accessible service
costs a bit more than to a fully able population. But having the same fleet serving both segments also
benefits the clients without special mobility needs as the additional sharing allows a reduction of the waitingtimes and of the prices per passenger.kilometre (PKM).
Following the results of the analysis about the composition of the Shared Taxi fleet (covering only the city)
the exercise for the breakeven prices was done both for the city and for the LMA. We calculated thebreakeven prices for scenarios A and B – that is the breakeven price per PKM for Shared Taxis with noaccessibility provisions to a fully accessible Shared Taxi fleet. As usual, these breakeven prices arepresented as a percentage of the current equivalent taxi price per PKM. For the case of the city, the value
for scenario A - all citizens considered as fully able, all regular minivans – is 33.0%. For scenario B - 10%of the citizens considered as having special mobility needs (equivalent to 35% of taxi trips), all accessible
minivans – the value is 36.1%.
The price in scenario B is only 9.5% above that of scenario A, which seems a relatively small differential.
The main reasons for the quite small increment in the equivalent price per PKM in spite of the 40% higher
capital costs and 10% fuel costs of the adapted vehicles, are the large distances run each day by theseshared vehicles and the relatively large part of the costs corresponding to labour, which we assumed would
not change. And the breakeven price differential per PKM (9.5%) is a bit different from the cost differential
per vehicle.kilometre (9.3%) because the average occupation of the Shared Taxis are also a bit different in
the two scenarios.
For the case of the LMA, the breakeven prices per PKM are a bit lower than for the city, at 30.5% of thecurrent equivalent taxi price per PKM for scenario A – all citizens considered as fully able, and 35.5% forscenario B - 10% of the citizens considered as having special mobility needs, all Shared Taxis being
wheelchair accessible. This price for scenario B is still below the current equivalent price of public transport,
at 85% of that price.
In a case of gradual deployment, as long as the size of the Shared Taxi fleet in each phase is decided with aprocess similar to the one used in this study, those vehicles will still have very intensive use, leading tosimilar breakeven price levels.
Concluding remarks on using Shared Taxis for inclusive mobility
This is a generic, exploratory exercise aiming to explore the concept of using Shared Taxis as the basis for
ensuring accessible services for people with reduced mobility. The results obtained in the simulation in asetting in which all road-based mobility is provided with shared solutions (Shared Taxis and Taxi-Buses)
and all Shared Taxis are adapted (according to ADA or other national standard) minivans show this isindeed possible at a very low cost differential relative to standard shared mobility services (and still lower
than the current [subsidised] price for public transport). These results are equally valid for the city and for
the LMA.

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CONCLUSIONS – 51

5. Conclusions
This report, dedicated to shared mobility solutions in large urban areas, builds upon previous work in earlier
CPB reports on simulations for the city of Lisbon. It addressed several issues, which are outlined below,
followed by a brief note about the associated results for each of them.
How do the estimated impacts for the larger Lisbon Metropolitan Area compare with those estimated for
Lisbon city?
Application to this wider territory followed essentially the same approach but took advantage of theexistence of a network of high capacity public transport services (suburban rail and ferry, and the metro in
the city) by the introduction of a new type of service – Taxi-Buses used as feeders to those public transport
services – and led to results that were even slightly better than those for the city, in terms of reduction of
traffic volumes and emissions as well as of equivalent prices per passenger.kilometre. Three very important
co-benefits would be felt across the whole LMA: access to jobs and other public services would becomemuch better and more equitable; a massive release of parking spaces for other uses of public interest
would be possible; and, quite interestingly, introducing the feeder buses leads to a considerable increase of
the use of high capacity public transport services.
How could a phased deployment of services, supported in each step by specific policy decisions, beorganised and what results could be expected at each phase?
A phased pathway was defined, focusing on more acceptable policy measures that could mobilise early
adopters, identifying a first phase that could be introduced to deliver very noticeable positive results, andsubsequently building on this for the next phases to achieve the full benefits of the shared mobility concept.
An additional scenario was explored, with differentiated implementation of measures in support of sharedmobility, in the city of Lisbon but not in the surrounding municipalities. We found that application only inthe city of the first effective phase of the pathway (as simulated for the whole LMA), coupled with a schemeof reduced access to the city for private cars of non-residents could deliver significant positive results forthe city.
How would different market structure configurations for the dispatcher role impact efficiency?
Two scenarios were run as an alternative to the single dispatcher configuration and all trips open to sharing
adopted as the basis. The first scenario was based on three different dispatchers addressing different
market segments, for which we found that the losses of efficiency are very high, leading to a
recommendation that this function should be a monopoly. Other ways of organising supply with more than
one dispatcher might be less damaging than the one tested, but the results suggest that any movement inthat direction should be carefully assessed in advance. It could be feasible to design shared mobility
services with a single dispatcher and multiple Shared Taxi operators, thus benefiting from competition
amongst operators while preserving system-wide dispatching efficiency. In such a case, special care will
have to be given to reduce the incidence of empty vehicle.kilometres.
The second scenario invovled with a single dispatcher allowing requests by some clients for exclusive
occupancy (i.e. non-shared) service. Since we believe the demand for these services is inevitable but they
induce some loss of efficiency, the recommendation is to make it possible but control the price differential
to keep the demand volume for this kind of service at a modest level.

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52 – CONCLUSIONSHow could an efficient scheme providing equitable access for all be designed, thus delivering high-quality
accessibility to those with physical and cognitive disabilities?
The analysis initially focused on what should be the percentage of the Shared Taxi fleet composed of such
adapted vehicles, looking at the implications of that choice on costs and on waiting times for thosepassengers. The result was that if the whole fleet of Shared Taxis was composed of adapted minivans,
jointly serving all clients with and without special mobility needs, with the same standards for maximum
allowed waiting time, the cost increase per passenger.kilometre would be only a little higher than if noservice was provided for those clients, but still lower than the current price of public transport. So therecommendation would be that all Shared Taxis are adapted to serve wheelchairs when the program islaunched – this also allows more flexibility in the case of gradual deployment.

Globally, the results are very encouraging in the sense that shared mobility solutions can indeed be a
powerful catalyst of change for the quality of life in urban areas, including the wider metropolitan
agglomerations. It is also relevant that provision of inclusive mobility to all citizens is possible with a very
low cost increase (but still at much lower cost levels than those of public transport in the present), thus
bringing together significant gains of personal accessibility and of territorial accessibility.
Since all those results were obtained for a configuration with full deployment of the shared mobility
solutions (and the corresponding elimination of some of the current supply options), transition paths were
explored and we tried to couple political acceptance and visible positive impacts on each step of theprocess, and found this to be possible. Additionally, different market structures were tested and importantlessons learned about the trade-offs between the efficiency possible with a (regulated) monopoly dispatchof these shared mobility services and the diversity of choices and resilience expected from situations withmultiple suppliers.
Evidence in favour of starting the process towards adoption of shared mobility solutions in urban
agglomerations is now even stronger. Naturally, each case is different and careful planning is necessary,
but the main challenges that had been identified for this exercise have all been successfully met andtechnology is at a stage (and cost) where this can be applied in support for an evolution towards urban
agglomerations with much better accessibility and quality of life for all, virtually no congestion, much lower
emissions and much lower capital and operational costs.
Finally, it is important to remember that this study should not be seen as an accurate prediction in exact
terms of the future of mobility in cities. It is an indicative predictor of the future based on a synthetic
population. In reality, all implementation of such possible new mobility scenarios would need to be
implemented gradually, following extensive planning and consultation in the cities concerned. On
implementation, careful monitoring and attention to user feedback could ensure that any issues be tackled
rapidly so as to not affect quality and reputation of the new services.

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BIBLIOGRAPHY – 53

Bibliography
Câmara Municipal de Lisboa (2005), Lisboa: O desafio da mobilidade.
Eiró, T. (2015), Assessing the Role of Shared Transport Alternatives in Urban Mobility. An Agent-Based
Simulation Approach. University of Lisbon.
Eiró, T. and L. Martinez (2014), “Development of a discrete choice model to evaluate the impact of newtransport alternatives”. In Transportation Research Board 93rd Annual Meeting. Washington D.C.
ITF (2017), “Linking People and Places: New ways of understanding spatial access in cities.” OECD
Publishing, Paris.
ITF (2016a), “Shared Mobility: Innovation for Liveable Cities”, International Transport Forum Policy Papers,
No. 21, OECD Publishing, Paris.
DOI: http://dx.doi.org/10.1787/5jlwvz8bd4mx-en
ITF (2016b), “The Economic Benefits of Improved Accessibility to Transport Systems.” International
Transport Forum Policy Papers, OECD Publishing, Paris. http://www.itfoecd.org/sites/default/files/docs/economic-benefits-improved-accessibility_0.pdf
ITF (2015), “Urban Mobility System Upgrade: How shared self-driving cars could change city traffic”.
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DOI: http://dx.doi.org/10.1787/5jlwvzdk29g5-en

Notes

1

Earlier ITF studies were based on 2010 data for the city of Lisbon, but this report is able to use a newactivity-based demand model calibrated with data from surveys done in 2011.
2

This adaptation is described in detail in the doctoral dissertation of Tomás Eiró (2015), which has developed thecurrent procedure based on activity data collected under the MIT Portugal Program for the LMA.

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