The document discusses research challenges around the impacts of automated vehicles on urban mobility. It explores how automated vehicles could impact mobility through changes in car ownership, travel behavior, and mode choice. Several studies are summarized that model how shared fleets of automated vehicles could substitute for private vehicles or integrate with public transit. Challenges are noted around simulating these complex systems and optimizing routing of shared automated vehicles. More research is still needed to fully understand how automated vehicles could transform urban transportation systems.

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Dr. ir. Gonçalo Homem de Almeida Rodriguez Correia
(Department of Transport & Planning, TU Delft)
g.correia@tudelft.nl
Facebook group: Transportation Planning and Analysis (>1250 members)
Challenges and opportunities for research on the impacts of
vehicle automation on urban mobility

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Driving to Driverless
Objective: Understand the research challenges on
automated driving regarding its impacts on urban mobility

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What is automated driving?
SAE International (Society of Automotive Engineers)

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It will take some time …
Source: Diffusion of Automated Vehicles: A quantitative method to model the diffusion of automated
vehicles with system dynamics. TIL Master thesis of Jurgen Nieuwenhuijsen. 2015.

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Source: Milakis, D., van Arem, B., van Wee, B. 2015 (work in progress). Implications of
automated driving. Delft Infrastructures and Mobility Initiative.
Impacts of automated driving on urban
mobility

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Mobility Impacts Questions
Fully
automated
vehicles
More
willingness to
travel by car?
Lower car
ownership?
Less trips
by car?
More public transport
demand?
More or less traffic
congestion?
More or less parking
demand?
Substitution of private
conventional vehicles
for automated ones?
Used as public
transport?
How will these be
operated?
Lower
Value of
Time?
More trips satisfied by
each car?
Use shared
fleets of
vehicles?

7. Used as public transport?
How will these systems be operated?
Wepods.nlFully
automated
vehicles
More public transport
demand?
More or less traffic
congestion?
More or less parking
demand?
Used as public
transport?
How will these be
operated?

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D2D100%EV
• The D2D100%EV project
has as its main objective to
study how to operate a fleet
of autonomous electric
vehicles as a feeder to train
stations.
• The case study of Delft-
Zuid to TUDelft is our
reference
• Twizy vehicles are used as
an example for that fleet.
• Planning and operational
studies are being done.
• A Twizy will be automated.

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D2D100%EV: Planning (I)
Source: Liang X., Correia G. and van Arem B. 2015. Optimizing the service area and trip selection
of electric automated taxis used for the last mile of train trips. Submitted to the Transportation
Research Board meeting.
Mathematical model for the operational area definition and trip selection in an
automated taxi system (bookings known in advance)
For a fleet of 5 taxis 15 zones are selected:

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Total
requests
Fleet
size
Obj.
(€/day)
Requests
satisfied
(Trips)
Requests
satisfied
(%)
Total
served
zones
Electric Taxis
466
5
391.9 269 58% 15
Conventional Taxis 452.5 319 68% 24
Electric Taxis
10
518.8 422 91% 31
Conventional Taxis 518.8 422 91% 31
D2D100%EV: Planning (II)
Source: Liang X., Correia G. and van Arem B. 2015. Optimizing the service area and trip selection
of electric automated taxis used for the last mile of train trips. Submitted to the Transportation
Research Board meeting.

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Source: Arthur Scheltes ongoing master thesis
D2D100%EV: Operation

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• It is argued that ridding in an
AV will be more pleasurable
than a normal car and a
normal bus. You will be able to
work or just enjoy your time.
D2D100%EV: Value of Travel Time
Trip
segment
Mode Willingness-to-pay per
10 minutes
Main Private car €1.80 - €1.90
Egress Bus/tram/metro €0.55 - €0.65
Egress Bicycle €1.45 - €1.55
Egress Automatic vehicle:
manually driven
€0.85 - €0.95
Egress Automatic vehicle:
automatically driven
€2.25 - €2.35
Willingness-to-pay for different modes per 10 minutes
What?!
Source: Yap M., Correia G. and van Arem B. 2015. Preferences of travellers for using automated vehicles as last mile Public
Transport of Multimodal train trips. Under review.

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WEpods project
9 km

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WEpods project: Challenge
Easymile

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WEpods project: Scale up
• Results of an optimization for simulation study (MatLab):
Source: Winter K., Cats O., Correia G. and van Arem B. 2015. Designing an automated demand-
responsive transport system: fleet size and performance analysis for the case of a campus-train
station service. Submitted to the Transportation Research Board meeting.

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WEpods project
Riender Happee, 3ME, TU Delft Jan Willem van der Wiel, Springer

17. Use shared fleets of vehicles for all trips?
Car2Go
Fully
automated
vehicles
More willing to
travel by car?
Lower car
ownership?
Less trips
by car?
More public transport
demand?
More or less traffic
congestion?
More or less parking
demand?
Lower
Value of
Time?
Use shared
fleets of
vehicles?

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Source: Luis Martinez, analyst at the ITF
International Transport Forum Model
• Scale up the concept of public transport with automatic
vehicles:
Taxibots (Shared taxis): till 6 pax and 5 min waiting; or
Autovots (Individual carsharing): 5 min waiting as well.

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International Transport Forum Model
International Transport Forum. 2015. Urban Mobility System Upgrade How shared self-
driving cars could change a city. Available online.

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• Simulation takes some hours for representing what
happens in a medium scale city like Lisbon, which is
not much, however:
 These simulation methods do not change travel times as
flows change in the network (static travel times).
 Moreover TaxiBots routing is not optimized: demand is
served using some heuristic that searches for the closest
cars.
International Transport Forum Model
More research in needed!

21. Substitute private conventional vehicles by
automated ones?
Fully
automated
vehicles
More willing to
travel by car?
More or less traffic
congestion?
More or less parking
demand?
Substitution of private
conventional vehicles
for automated ones?
Lower
Value of
Time?
More trips satisfied by
each car?

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Traffic Assignment + Routing Problem
• A model that assigns family
owned automated vehicles
to the trips of the
household.
• As vehicles are routed in
the network traffic
congestion is formed, travel
times increase.
• Trips not satisfied by the
cars are done by Public
Transport.
Source: Correia G. and van Arem B. 2015. The Privately Owned Autonomous Vehicles Assignment
Problem: a model to assess the impacts of private vehicular automation in urban mobility. Submitted for
publication.

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• Example of how automated vehicles could make a difference
in our household:
Traffic Assignment + Routing Problem
Home
Husband
work
Wife
work
Husband
lunch
6km
4km
3km
4km
• Conventional:
Driving distance=6+4+3+3+4+6=26 kms
Total distance inside a car=2*6+4+3+3+4+6*2=38 kms
• With the Automated Driving another routing option may happen:
Driving distance=4+4+4+3+3+4+6=28 kms
Total distance inside a car=2*4+4+3+3+4+6*2=34kms

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Traffic Assignment + Routing Problem

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Traffic Assignment + Routing Problem
Scenario
Generalized
Cost (euros)
(O.F.)
Trips
per
vehicle
Absolute
delay
(hours)
Delay (%
of total
driving
time)
Car
modal
share
(%)
Average time
inside a car per
trip (min)
Conventional 1,539,100 2.97 110 1.65% 43.6% 19.51
With
Automation
1,520,000 3.41 143 1.79% 47.0% 19.46
With
Automation
and lower
value of travel
time
1,267,430 3.70 110 1.08% 53.4% 22.15
++-
-
+-
+
Source: Correia G. and van Arem B. 2015. The Privately Owned Autonomous Vehicles Assignment
Problem: a model to assess the impacts of private vehicular automation in urban mobility. Submitted for
publication.
-
+

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Traffic Assignment + Routing Problem
• Positive:
• This approach considers the variations of travel times as
vehicles are routed in the network.
• Choice is done in a utility maximizing perspective.
• Negative:
• The traffic assignment + Routing problem takes one day
for the city of Delft in order to converge to equilibrium! this
is quite slow!
More research in needed!

27. Driving to Driverless
Dr. ir. Gonçalo Homem de Almeida Rodriguez Correia
(Department of Transport & Planning, TU Delft)
g.correia@tudelft.nl
Facebook group: Transportation Planning and Analysis (>1250 members)
Thank you!