Access to this full-text is provided by Wiley.
Content available from Journal of Advanced Transportation
This content is subject to copyright. Terms and conditions apply.
Research Article
Exploring the Performance of Different On-Demand Transit
Services Provided by a Fleet of Shared Automated Vehicles: An
Agent-Based Model
Senlei Wang ,1 Goncalo Homem de Almeida Correia ,2 and Hai Xiang Lin1
1Del Institute of Applied Mathematics, Del University of Technology, Del, Netherlands
2Department of Transport and Planning, Del University of Technology, Del, Netherlands
Correspondence should be addressed to Senlei Wang; s.wang-3@tudel.nl
Received 8 July 2019; Revised 21 October 2019; Accepted 5 November 2019; Published 16 December 2019
Academic Editor: Giulio E. Cantarella
Copyright © 2019 Senlei Wang et al. is is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Automated vehicles used as public transport show a great promise of revolutionizing current transportation systems. Still, there are
many questions as to how these systems should be organized and operated in cities to bring the best out of future services. In this
study, an agent-based model (ABM) is developed to simulate the on-demand operations of shared automated vehicles (SAVs) in
a parallel transit service (PTS) and a tailored time-varying transit service (TVTS). e proposed TVTS system can switch service
schemes between a door-to-door service (DDS) and a station-to-station service (SSS) according to what is best for the service
providers and the travelers. In addition, the proposed PTS system that allows DDS and SSS to operate simultaneously is simulated.
To test the conceptual design of the proposed SAV system, simulation experiments are performed in a hypothetical urban area to
show the potential of dierent SAV schemes. Simulation results suggest that SAV systems together with dynamic ridesharing can
signicantly reduce average waiting time, the vehicle kilometres travelled and empty SAV trips. Moreover, the proposed optimal
vehicle assignment algorithm can signicantly reduce the empty vehicle kilometres travelled (VKT) for the pickups for all tested SAV
systems up to about 40% and improve the system capacity for transporting the passengers. Comparing the TVTS system, which has
inconvenient access in peak hours, with the PTS systems, which always makes available door-to-door transport, we conclude that the
latter could achieve a similar system performance as the former in terms of average waiting time, service time and system capacity.
1. Introduction
It is being said that we are at the dawn of the next mobility
revolution with the introduction of automated driving.
However, there are aspects of the automated vehicles (AVs)
that still need to be understood, for example, there are many
legal, regulatory and technical problems that are delaying the
deployment of AVs. A eet of shared automated vehicles
(SAV), which functions as a centralized taxi service system,
will probably bring the most disruptive changes in urban
mobility. e real potential of SAVs is that they make the
implementation of an entirely new public transportation sys-
tem possible. at is, SAVs might have the power to funda-
mentally transform transportation mobility and revolutionize
the transport system given the added degrees of freedom of
operating shared taxi systems [1–10].
A eet of SAVs operated in a centralized way in SAV sys-
tems could function as an ecient taxi system to provide
demand-responsive service for travel demand during a day,
especially in urbanized areas. e SAV system could be used
to provide station-to-station (stop-to-stop) service (SSS) to
transport as many people as possible in busy routes in a
demand-responsive fashion. However, the SAV system could
also be operated as a door-to-door service (DDS) giving great
convenience to travellers as of today’s Transport Network
Companies such as Uber, Ly, and Didi Chuxing. In this
paper, we aim to take into consideration these two ways of
operating urban automated transport systems, both in parallel
and in sequence, and propose a simulation tool to assess their
impact on an urban network.
In addition, SAV systems could facilitate the implemen-
tation of dynamic ride-sharing which aims to pool multiple
travelers with similar origins, destinations, and departure
times in the same vehicle. Dynamic ridesharing has the poten-
tial to improve the performance of proposed SAV systems in
terms of energy saving, waiting time reduction, VKT
Hindawi
Journal of Advanced Transportation
Volume 2019, Article ID 7878042, 16 pages
https://doi.org/10.1155/2019/7878042
Journal of Advanced Transportation2
reduction, etc. [11–17]. More importantly, the dynamic
ridesharing could enable the SAV system in accommodating
more travel demand with the same number of vehicles. e
proposed SAV systems oering various service schemes with
dynamic ridesharing could eliminate the problems in past
attempts to provide demand-responsive transit services [18].
Building upon the on-demand DDS and on-demand SSS,
two extra on-demand transit service systems are proposed and
simulated. Time-varying transit service (TVTS) that can
switch service schemes between DDS and SSS depending on
the time of day (peak hours and o-peak hours for example),
and the simultaneous operation of DDS and SSS, allowing
both of them to operate in parallel (designated as parallel tran-
sit service: PTS).
Few studies have explored the operation of variations in
the service schemes of SAV systems. As a rst attempt to inves-
tigate this problem, the paper constructs an agent-based model
(ABM) to study dierent scenarios of operations of dierent
service schemes. With the help of ABM, conceptual design
and a preliminary study are presented for dierent SAV sys-
tems as dened above: SSS system, DDS system, TVTS system,
and four PTS systems. e ABM describes the SAV system
with its details and complexity by modelling the travel requests
and vehicle movements, and especially interactions between
vehicles and travelers. e model allows us to understand how
the system components of the SAV system behave over time
and nd the potential of SAV systems by studying the most
ecient ways of operating them under dierent service
schemes. erefore, the preliminary look at system perfor-
mance of SAV systems could provide useful information for
transport operators when deciding to adopt a SAV system in
the future. But it also provides support for future more detailed
simulation studies whereby these schemes might be important
to test.
e remainder of the paper is organized as follows. Section
2 reviews the existing literature related to SAV systems and
dynamic ridesharing. Section 3 presents the model specica-
tions. Section 4 gives a detailed description of the experiments
that have been run. Section 5 provides an analysis of the sim-
ulation results. Conclusions are drawn in Section 6. e nal
section lists some model limitations and envisions future work.
2. Background Literature
Given that the possible future existence of SAV will bring the
most potentially disruptive changes in the urban transport
system, the exploration of the implementation of these systems
has been a focal point of transportation research in recent
years [19–23].
Burns et al. examined the performance of an SAV system
and cost to explore the feasibility of a eet of SAVs to serve
the existing travel demand, they found that the SAV systems
are compelling due to the shorter waiting time and low oper-
ational cost [24]. Spieser et al. examined the problem of eet
sizing of Automated Mobility-on-Demand Systems using the
actual transportation data of Singapore and found out that the
eet sizes of SAVs that serve the entire mobility needs between
stations in Singapore can be 1/3 of previous passengers’ vehi-
cles while keeping an acceptable waiting time [25]. Fagnant et
al. investigated the benets and environmental implications
of SAV systems in an Austin-sized city, Texas. eir study
results indicate that each SAV can substitute 11 and 9 conven-
tional vehicles in order to serve 3.5% and 1.3% of regional trips
respectively. Although approximately 11% extra empty VKT
was generated, energy savings and emission reductions may
overcome those eects [26, 27].
Several studies focus on investigating the potential benets
of SAV systems when considering shared rides. Fagnant et al.
investigated the impact of the dynamic ridesharing in SAV
systems on vehicle mile travelled (VMT), waiting times and
travel costs for SAV users. ey concluded that the dynamic
ridesharing could result in a reduction of generated VMT up
to 4.2% and the reduction of the waiting time and average total
service time up to 4.5 minutes and 0.3 minutes respectively
[11]. Zhang et al. focused on the potential impact of SAV eet
size, dynamic ridesharing and clients’ preference, and vehicle
cruising on urban parking demand by considering 2% of the
population as the users of the SAV system in a hypothetical
city. eir study indicated that the SAV system could facilitate
the reduction of parking demand of about 90%, and the reduc-
tion could be further expanded by 1% by considering dynamic
ridesharing in the SAV system [28].
Other works concern multi-mode transportation in the
analysis of the impact of an SAV system. Martinez in the
International Transport Forum investigated the performance
of shared automated taxis as a supplement to serve requests
for shared buses oering inter-stop service with prebooking.
e benets of shared taxis and bus systems are the reduction
of emissions and VKT, peaking at 40% and 30% respectively
[29, 30]. Zachariah et al. investigated the operation of a eet
of autonomous taxis supplementing the transit train service
among xed taxi stands in New Jersey. Simulation results
revealed that shared rides could signicantly reduce the VMT.
In addition, they found that temporal and spatial demand
variations inuence the ride-sharing success rate. erefore,
the favorable distribution of the SAV eet based on the
demand variations can signicantly improve the sharing rate
and reduce congestion [31]. SAVs as a feeder service to train
stations have been explored from an operational point of view
looking at how many vehicles are needed, dening an area of
operation, and how to charge the vehicles in case they are
electric [32, 33].
Although studies on SAV systems are now booming, there
are a limited number of research papers that explore the imple-
mentation of ecient on-demand SAV systems in terms of the
dierent service schemes in which they could be operated and
the potential synergies among those. at is, it is unclear what
kind of service schemes the SAV systems should provide in a
demand-responsive fashion. is paper attempts to ll that
gap through a simulation study in a hypothetical city as a rst
approach to the problem. An ABM is used to explore the trade-
os in dierent SAV systems between the service levels, cap-
tured by the waiting time and service time (in-vehicle travel
time) and the system eciency in terms of VKT, system capac-
ity, and served trips.
3Journal of Advanced Transportation
3. Model Specifications and Operations
e ABM is intended to simulate the operations of SAVs and
their interactions with travelers’ real-time requests within a
hypothetical city area. We simulate tailored on-demand SAV
systems with various service schemes as already described. In
this study, the eet operator has no information about the
travel requests in advance. In other words, the eet operator
has no information about travelers before they request service.
Aer a traveler requests a vehicle, the eet operator knows the
information of the traveler. e eet operator only assigns the
idle vehicles to serve the travelers in a real-time fashion, and
therefore scheduled assignment in a prebooking fashion is not
possible. As shown in Figure 1, the eet operator is responsible
for real-time vehicle assignment, dynamic ridesharing, and
managing and monitoring information of travel requests and
vehicles. In addition, the central operator is designed for route
assignments for SAVs. Vehicle assignment means that the eet
operator nds idle vehicles to serve real-time travel requests.
e route assignment is to nd a route either for en-route
pickup vehicles or en-route drop-o vehicles. We distinguish
the functions between the eet operator and the central oper-
ator, enabling the designed system to keep an expanded capa-
bility for multiple operators.
e interaction of system components in Figure 1 between
SAVs and time-dependent travel requests are illustrated. e
eet operator controls the assignment of SAVs to serve real-
time travel requests. Aer the assignment of SAVs, commu-
nications will take place between travel requests and SAVs
until travellers arrive at their destination. at is, aer SAVs
received the essential information (origin, destination, iden-
tication) of travel requests, each SAV will communicate with
targeted travel requests for pickups and drop-os. e dynamic
ridesharing module in the eet operator aims to group trav-
ellers, according to the matching rules. e routing module
in the central operator is responsible for the route calculation
for real-time vehicle routing. e central operator will transit
routing information to the in-service vehicles. e model con-
tents include dynamic generation of time-dependent requests,
real-time vehicle assignment, and dynamic ridesharing. To
deal with the lack of some essential information, we list the
detailed description of model assumptions:
(i) No induced travel demand is taken into account;
(ii) All the travelers are willing to share rides with
strangers;
(iii) e battery capacity can support full-day operations
for each SAV;
- Module
- Collection
- Agent
Travelers
Routing
module
Send demand requests
Notify vehicle assignment results
Vehicle
assignment
Vehicles for route
assignment
Update
Obtain road structure information
Exchange
information
Road network
Demand generation
Fleet operator
Central trac operator
Vehicle
collection
Time-dependent
demand requests
Transit routing information
Vehicles
Idle vehicles
In-service vehicles
Real-time demand
collection
Dynamic
ridesharing
Grouped
travelers
Individual
travelers
Match requests
- Infrastructure
- Category
- Information
F 1:Interaction between system components.
Journal of Advanced Transportation4
Find_the_destination_zone
Get_the_time_to_request_a_serviceWalk_in_the_same_zone
Leave_the_system
Select_service_modes
Request_door-to-door_servcies
Request_station-based_services
Wait_for_vehicle_assignment
Wait_for_enroute_SAVs
Travel_to_the_destinaton
Reach_the_destination_station
Station-based servcie
Walk_to_the_destinat ion
Arrive_at_the_destination
Walk_to_xed_stations
F 2:e state chart that represents the behavior of a travel request.
5Journal of Advanced Transportation
when the eet operator failed in nding adequate idle vehicles
as input for the Hungarian algorithm or when there is only
one request for vehicle assignment in a certain dispatching
time interval.
Aer the SAV assignment, the vehicle will have the essen-
tial information about requests (location, requested service,
ridesharing status), and communicate with travellers by send-
ing an assignment message. Aer that, the traveler waits for
the SAV’s arrival. erefore, the waiting time can be composed
of waiting time for vehicle assignment (due to the unavaila-
bility of a SAV) and waiting for the SAV’ arrival while it is
en-route for picking up the traveller.
3.2. Dynamic Generation of Time-Dependent Travel
Requests. Based on the aggregate travel demand, individual
travel requests are generated with spatial-temporal
characteristics. In this study, the demand generation process
can be divided into the following two steps.
(1) Generating a xed number of time-dependent travel
requests for each zone over each time interval.
Total production of travel requests for each zone is cal-
culated based on an origin-destination (OD) matrix, and
then demand production per one-hour interval for each
zone is estimated by using the departure time distribu-
tion and total demand production per zone for 24 hours.
At the beginning of each time interval, a xed number
of travel requests are generated, and then the generated
travel requests are distributed within this time interval
by following a discrete uniform distribution. As a result,
all the generated requests for each time interval will be
associated with a specied time.
(2) Finding a destination zone for each travel request.
It is assumed that observations of travel requests in each
zone over other trac analysis zones in the whole study
area are known in the OD matrix table. at is to say, the
number of requests ending in every other zone is known.
Based on these observations of travel requests over trac
analysis zones in the OD matrix table, the destination zone
of each travel request will be drawn by using the Monte
Carlo simulation process. In the end, each request will
have a destination zone. We give a detailed overview of
departure time distribution and total travel requests for
each zone in the section of detailed travel demand.
As shown in Figure 2, statechart diagram as one of the ve
Unied Modeling Language diagrams is used to model the
dynamic nature of the travellers. e statechart diagram can
dene dierent states of a traveler during its lifetime and these
states are changed by events. By using statecharts, traveler
behavior can be visually shown. e statechart has states and
transitions. Transitions may be triggered by user-dened con-
ditions (timeouts or rates, agent’s arrival, messages received
by the statechart, and Boolean conditions). For example, aer
the SAV assignment, the vehicle will have the essential infor-
mation about requests (location, requested service, rideshar-
ing status), and communicate with the clients by sending an
assignment message (state transition by receiving a message).
Aer that, the traveler waits for the SAV’s arrival (state
(iv) e parking spaces are enough for all the SAVs in
each station.
For easier model implementation, we simplify the following
model specications:
(i) SAV speed is predened on road segments and
updated for peak hours and o-peak hours
respectively;
(ii) Cancellation of assigned SAV is not allowed;
(iii) Travelers will give up a request when the waiting time
for being assigned a vehicle exceeds a specic time
threshold;
(iv) Travelers’ choices between door-to-door service and
station-based service are based on a xed willingness
to use a certain service, which is an experimental
parameter (20%, 40%, 60%, and 80%).
3.1. Real-Time SAV Assignment. In this model, two assignment
methods are designed. e rst vehicle assignment method is
to assign the nearest idle vehicles to serve the real-time travel
requests according to the rst-come, rst-served (FCFS)
principle. We dene the rst vehicle assignment method as
the FCFS vehicle assignment method. e second is an optimal
assignment method that assigns a group of idle vehicles to
bundled travel requests with the objective of minimizing the
total empty travel distance for the pickups.
3.1.1. FCFS Vehicle Assignment Method. We design a eet
operator to assign the idle and nearest SAVs to serve real-time
travel requests. e rules of the design are as follows:
(i) e eet operator will nd an idle and nearest SAV in
the same sub-region as the request departure location
based on the FCFS principle;
(ii) If there is no available SAV close to the request, the
eet operator will nd an idle SAV from the whole
study area to serve it;
(iii) e eet operator only gives top priority to shared
riders. at is, the travelers who will share their rides
are sorted from the waiting list, and assigned an idle
and nearest SAV as soon as possible.
3.1.2. Optimal Vehicle Assignment Method. e optimal
vehicle algorithm method can assign a group of idle vehicles
={v1, ⋅⋅⋅ ,v}
to bundled travel requests
={
1, ⋅⋅⋅,
}
.
at means that the eet operators can bundle a certain number
of travel requests, each of which is specied with a timestamp,
assign a group of available vehicles to them with the objective
of minimizing the total empty travel distance of the assignment.
e size of bundled travel requests varies along the day according
to the demand that coincides in the same time interval. e
collection of idle vehicles participating in the optimal assignment
is found by searching for the nearest vehicles for each travel
request in the set
𝑅
. e assignment problem can be formulated
as a bipartite matching problem between bundled travel requests
and selected idle vehicles in every dispatching time interval. e
Hungarian algorithm [34] is used to solve the problem.
Nevertheless, a travel request can be assigned a vehicle by
FCFS principle without calling the Hungarian algorithm only
Journal of Advanced Transportation6
ridesharing agent is created, it is responsible for the interac-
tion with an assigned vehicle. Each ridesharing agent records
the information of grouped travellers, the OD of the travel-
lers, and the assigned vehicles for grouped travellers.
According to the designed rules for dynamic ridesharing,
the eet operator dynamically adds and removes ridesharing
agents in the simulation process.
3.5. Service Scheme. We have dened four types of on-demand
SAV systems in terms of variations of service schemes as
described above: DDS system, SSS system, TVTS system,
and PTS system. In all SAV systems, we did not simulate
user choices for dierent services based on attributes such as
price or travel distance; however, we assume that individual
requests have various levels of willingness to use the station-to-
station service in the proposed PTS systems. According to the
willingness to choose the station-to-station service, the PTS
system can be divided into PTS-20%, PTS-40%, PTS-60%, and
PTS-80%. is would result from the prices of both services;
otherwise, travelers would naturally prefer to use the door-to-
door system only because it is more convenient.
4. Model Application and Implementation
e simulation model was developed from scratch in Anylogic
proprietary ABM platform with Java programming language,
which is available for research purposes. In this study, SAV
systems with dierent service schemes are tested in a hypo-
thetical urban road network.
4.1. Urban Road Network. e road network of a city in
the scale of 5 km × 5 km (roughly the size of Del in the
Netherlands) is used for testing the operations of dierent
SAV systems. e network is taken from the UDES (Urban
Dynamics Educational Simulator) model (https://www.
researchgate.net/project/e-Urban-Dynamics-Educational-
Simulator-UDES). e road network topology includes 78
links and 77 nodes (see Figure 3). Stations for the drop-o
transition by vehicle arrival). e travel request will give up
waiting for vehicle assignment when waiting assignment time
exceeds a time threshold (state transition by timeout event).
3.3. Fleet Size. e eet size is an experimental parameter in
the ABM. We simulate the operations of SAV systems with
dierent eet sizes. In addition, in order to illustrate the
relations between multiple system characteristics, we estimate
a small eet size for keeping an acceptable service quality for
SAV systems.
3.4. Dynamic Ride-Sharing. e SAV can facilitate the
implementation of dynamic ride-sharing. Dynamic ridesharing
aims to pool multiple travelers with similar temporal and
spatial characteristics.
In this model, we design a set of rules for the implementa-
tion of the dynamic ridesharing. Travelers who have common
OD zones are allowed to share a SAV. Note that the grouped
travelers with common OD zone may have dierent departing
and arriving specic locations within each zone. e travel
requests can be served at a service station or at their doorstep.
From the service scheme point of view, we design a set of
rules for dynamic ridesharing.
(i) If both of the shared rides need to be served at a sta-
tion, the assigned SAV will pick them up at the origin
station, and then drop them o at the destination
station.
(ii) If both of the shared rides need to be served in a door-
to-door fashion, the assigned SAV will rst pick up the
passenger who is closer to it and then pick up another
one. Based on the trip distance of the passengers, the
SAV will rst drop o the passenger who has a shorter
trip distance, and then it will drop o the second pas-
senger at its specic destination of the same zone. If the
assigned SAV has the same estimated travel distance
from the two passengers in two dierent locations,
the SAV will rst pick up the passenger who sent the
request earlier, and then pick up the second passenger at
his or her doorstep. Aer reaching the rst passenger’s
destination, the second passenger will be dropped o.
(iii) If one of the shared rides needs to be served at a sta-
tion and the other one is to be served at the doorstep,
the SAV will rst pick up the passenger who is closer
to it and then pick up the other passenger. Based on
the trip distance of the passengers, the SAV will rst
drop o the passenger who has a shorter trip distance,
and then it will drop o the second passenger at its
destination (designated station or specic location)
of the same zone. If the assigned SAV has the same
estimated travel distance from the two passengers in
two dierent locations, the SAV will rst pick up the
passenger who sent the request earlier, and then pick
up the second passenger at his or her doorstep. Aer
reaching the rst passenger’s destination, the second
passenger will be dropped o.
In this ABM, a ridesharing agent type is introduced to del-
egate the grouped travel requests. at is, once the
F 3:e road network.
7Journal of Advanced Transportation
the maximum number of travelers in a shared car is two. e
time interval being used for the assignment is 5 seconds.
5. Results and Discussion
5.1. Analysis of the Impact of Vehicle Assignment Methods. To
look at how the optimal vehicle assignment method impacts
the performance of dierent SAVs systems, 70 scenarios for
dierent SAV systems with variations of eet size are simulated
(see in Table 2).
e simulation results in Figure 5 indicate that the optimal
vehicle assignment algorithm can reduce empty VKT. e eet
operator can optimally assign idle vehicles to serve the trave-
lers while minimizing the total empty travel distance for the
pickups. e degree of reduction of empty VKT greatly
depends on the eet size. In Figure 5(a), the optimal assign-
ment can reduce the empty VKT for all the SAV systems in
about 40%, while there is almost all of the same empty VKT
for both assignment methods with a 4000-SAV eet size in
Figure 5(e).
and pickup service in SAV systems are uniformly distributed
among the trac analysis zones (TAZs) in the whole study
area. e scale is graphically dened in the agent simulation
environment as: one pixel corresponds to ten meters. e SAVs
shortest paths are computed using the Dijkstra algorithm.
4.2. Detailed Travel Demand. e SAV systems will serve a
total demand of 110 000 trips in a full day. Figure 4 depicts
the departure time distribution of the demand and the total
production of travel requests for each zone that are used as
input in the simulation model as explained in Section 3.
To mimic the commuting patterns, OD matrices with dif-
ferent assumed observations are used: one in the rst half of
the day and the other for the rest of the day. e destination
zones are found by using the Monte Carlo simulation process.
erefore, heterogeneous observations in the trip table enable
the simulation to generate dierent results.
Travel demand is not only generated and attracted in the
centroid of each TAZ but specic points inside the zones are
used, in order to simulate the operation of dierent service
schemes. at means that travellers would walk from/to the
station when using the station-based service or waiting for
their pickup at their places of residence if there is a door-to-
door service.
4.3. Simulation Parameters. Table 1 shows basic input
parameters for the SAV simulation. e vehicle speed is
predetermined in all SAV systems in peak hours and o-
peak hours respectively. Based on the research conducted by
Wang et al. [35] in terms of speeds during the dierent times
of the day, the reduction of the speed in peak hours range
between 10% and 30%. erefore, we assume that the speed
of the SAV is 20% lower than that in o-peak hours. In this
ABM, we assume the SAV speed in o-peak hours is 36 km/h.
e energy eciency of dierent electrical vehicles roughly
ranges from 1 kWh per 7.16 km to 1 kWh per 4.82 km (https://
pushevs.com/electric-car-range-eciency-nedc/). erefore,
for energy consumption, we adopt a rate of electricity
consumption of 1 kWh per 7 kilometers that is reasonable for
a two-seat, light-weight vehicle. We assume that travelers will
give up requesting a SAV when the waiting time for a vehicle
assignment exceeds 5 minutes. In this paper, we assume that
0
5
10
15
20
25
012345
Demand reqeusts (thousands)
Zone ID
(a)
0
0.2
0.4
0.6
0.8
1
1.2
0123456789
10
11
12
13
14
15
16
17
18
19
20
21
22
23
No. of time intervals
(b)
F 4:e detailed overview of departure time distribution and total demand for each zone. (a) Departure time distribution. (b) Total
demand for each zone.
T 1:Input parameters.
Categor y Va l u e
City scale 5 km
×
5 km
Road links 78
Road nodes 77
Travel requests 110 000
Vehicle o-peak speed 36 km/h
Vehicle peak-hour speed 28.8 km/h
Vehicle capacity 2 persons
Time threshold for client dropout 5 minutes
Time interval for optimal assignment 5 seconds
Operation hours Around the clock
AM peak 7 AM–9 AM
PM peak 4 PM–6 PM
Fleet size [2000, 4500]
Fleet size step 500
Journal of Advanced Transportation8
T 2:Combinatorial scenarios for the simulation of optimal vehicle assignment.
Assignment method Optimal assignment method FCFS assignment method
SAV systems DDS SSS T VS PTS-20 PTS-40 PTS-60 PTS-80
Fleet size 2000 2500 3000 3500 4000
0
20
40
60
80
100
120
140
160
DDS
SSS
TVS
PT-20%
PT-40%
PT-60%
PT-80%
ousands
Empty_VKT_optimal_assignment
Empty_VKT_FCFS
(a)
0
20
40
60
80
100
120
140
DDS
SSS
TVS
PT-20%
PT-40%
PT-60%
PT-80%
ousands
Empty_VKT_optimal_assignment
Empty_VKT_FCFS
(b)
0
20
40
60
80
100
120
DDS
SSS
TVS
PT-20%
PT-40%
PT-60%
PT-80%
ousands
Empty_VKT_optimal_assignment
Empty_VKT_FCFS
(c)
0
20
40
60
80
DDS
SSS
TVS
PT-20%
PT-40%
PT-60%
PT-80%
ousands
Empty_VKT_optimal_assignment
Empty_VKT_FCFS
(d)
0
10
20
30
40
50
60
DDS
SSS
TVS
PT-20%
PT-40%
PT-60%
PT-80%
ousands
Empty_VKT_optimal_assignment
Empty_VKT_FCFS
(e)
F 5:Comparisons of generated empty VKT for dierent assignment methods with variations of eet size. (a) Comparisons of VKT with
the 2000-SAV eet size. (b) Comparisons of VKT with the 2500-SAV eet size. (c) Comparisons of VKT with the 3000-SAV eet size. (d)
Comparisons of VKT with the 3500-SAV eet size. (e) Comparisons of VKT with the 4000-SAV eet size.
9Journal of Advanced Transportation
in Tables 3 and 4 indicate that the average peak-hour waiting
time in all systems with dynamic ridesharing ranges from
8.68 minutes to 13.17 minutes when we adopt the 2000-SAV
eet size. For smaller eet sizes, the service quality would be
lower. Furthermore, there is little dierence in the average
waiting time in the four-PTS system when the eet size is
reduced from 3500 to 2000 as shown in Figure 8. erefore,
we could analyze the SAV systems’ performance starting from
the estimated 2000-SAV eet size.
5.3. Analysis of the Impact of Dynamic Ridesharing. SAV
systems allow travellers to share their rides according to the
designed rules. In this analysis, we analyse the impact of
dynamic ridesharing in the SAV system. Compared with a
nonridesharing system in Tables 3 and 4, SAV systems with
ridesharing signicantly reduce at least 50% of the average
waiting time, 6.0% of VKT and 4.7% of total SAV trips. e
dynamic ridesharing could improve the performance of all
proposed systems.
e DDS system reaches a peak of approximately 34.4%
of shared rides, while the SSS system has the lowest percentage
of shared rides (around 15.6%). Four-PTS systems have
slightly high percentages of shared rides from 18.7% to 25.9%.
e shape of the polyline depicting the results of the opti-
mal assignment displayed in Figure 5 is similar to that repre-
senting the results of the FCFS assignment. It is evident that
the trend of the generated empty VKT over dierent SAV
systems for both vehicle assignment methods is similar to each
other. at means that although the optimal assignment
method can reduce the generation of empty VKT, the dier-
ence of generated empty VKT across SAV systems remains the
same to some extent.
Considering the number of drop-outs (unsatised trips),
it is possible to see the simulation results in Figures 6 and 7
for the total number of dropouts with both vehicle assignment
methods and for all tested systems. Results indicate that the
optimal vehicle assignment can enable the SAV systems to
transport considerably more travelers. is can be explained
because of a reduction in the waiting time due to the higher
eciency of the optimal vehicle assignment method.
5.2. Analysis of Fleet Size Variations. We provide a performance
analysis of the SAV system for dierent eet sizes. In addition,
a small eet size for the base scenario to keep an acceptable
level of service quality is determined to analyze the other
characteristics of dierent SAV systems. Simulation results
T 3:Performance indicators for DDS, SSS, and TVTS systems with a 2000-SAV eet size.
SAV system DDS SSS TVTS
Ridesharing No Ye s No Ye s No Ye s
Avg. waiting time (min) 14.79 7.21 9.84 4.41 12.87 6.43
Avg. peak-hour waiting time (min) 20.53 11.22 16.82 8.68 19.68 9.08
waiting time > 10 minutes (trips) 47 849 29 766 42 962 16 624 48 188 23 773
Avg. service time (min) 26.76 19.01 19.15 11.95 23.67 15.311
Avg. peak-hour service time (min) 33.65 24.34 26.94 16.99 28.56 16.22
Total VKT (km) 769 099 681 432 673 892 600 751 661 443 617 767
Energy consumption (KWh) 109 871 97 347 96 270 85 821 94 491 88 252
Total SAV trips 131 355 117 999 141 076 125 544 138 487 131 901
Requests dropouts 24 328 24 554 12 358 12 027 19 066 16 322
Percentage of dropouts (%) 22.1% 22.3% 11.2% 10.9% 17.3% 14.8%
Percentage of shared rides (%) 0% 34.4% 0% 15.6% 0% 20.1%
T 4:Performance indicators for four-PTS systems with a 2000-SAV eet size.
SAV system PTS-20% PTS-40% PTS-60% PTS-80%
Ridesharing No Ye s No Ye s No Ye s No Ye s
Avg. waiting time (min) 15.07 6.61 14.33 5.78 13.53 5.06 12.06 4.71
Avg. peak-hour waiting time (min) 22.48 13.20 21.79 11.92 21.08 11.11 19.54 9.77
waiting time > 10 minutes (trips) 5 108 2 408 5 078 2 331 5 129 2 123 4 820 1 921
Avg. service time (min) 26.49 16.32 25.22 15.11 23.88 14.01 21.85 13.12
Avg. peak-hour service time (min) 34.88 23.11 33.58 22.14 32.35 20.67 30.18 18.77
Total VKT (km) 651 423 568 632 659 864 564 712 674 841 565 322 675 645 589 666
Energy consumption (KWh) 93 060 81 233 94 266 80 673 96 405 82 022 96 520 84 238
Total SAV trips 136 548 118 441 138 086 118 301 141 295 119 849 141 729 121 721
Requests dropouts 22 130 21 095 20 083 19 290 17 842 17 085 15 103 14 233
Percentage of dropouts (%) 20.1% 19.2% 18.3% 17.5% 16.2% 15.5% 13.7% 12.9%
Percentage of shared rides (%) 0% 18.7% 0% 19.6% 0% 20.1% 0% 25.9%
Journal of Advanced Transportation10
(in-vehicle travel time) in case of the 2000-SAV eet size.
TVTS system has a similar performance in terms of average
waiting time and service time with the PTS-20% and PTS-40%
system. We can infer that the SAV systems, e.g., PTS-20% and
PTS-40% system, that allows two service schemes to operate
in parallel with a degree of restricted access to the door-to-
door service could provide a similar system performance than
the TVTS system which only oers station-based service in
peak hours.
When a total eet size of 3500 SAVs is adopted (Figure
8(d)), the average waiting time in the PTS systems with 80%
willingness to request station-based services could achieve a
similar value with that of TVTS system with approximately
22.8% of average service time. is means that the system
performance in terms of average waiting time and average
service time achieved by the sequential operational rules in
the TVTS system can be obtained by the proposed parallel
modes of service schemes in the PTS-80% system.
5.5. Analysis of VKT and Energy Consumption. e DDS
system has more VKT and energy consumption than other
SAV systems as can be seen in Figures 9(c) and 9(d). Except for
the DDS system, other proposed systems converge to the same
amount both in VKT and energy consumption respectively,
when eet size approaches 4000 vehicles. Both VKT and
energy consumption experience is a growing trend in four
PTS systems with an increase in eet size from 2000 to 2500,
while the TVTS system has a high level of energy consumption
and VKT. Nevertheless, with the continued growth of eet size
to 4000, the TVTS system decreases the energy consumption
and VKT to a relatively low level compared to the energy
consumption on the PTS systems. TVTS system could operate
a relatively large eet size to provide quality service while
consuming less energy.
Figure 9(a) showing the number of total SAV trips indi-
cates that total SAV trips rise rst, then fall for each system
with an increment of eet sizes for each SAV system. One of
the possible explanations is that with the increase of the SAV
eet size, fewer travel requests drop out of the SAV system.
erefore, the SAV system satises many more trips that result
in the increase in the total number of SAV trips. On the other
hand, the gradually increased eet size will potentially reduce
the empty SAV trips for pickup. e decline of empty (unoc-
cupied) SAV trips for en-route pickups appears to reduce the
total SAV trips. As a result, the total SAV trips rise rst and
decline for each SAV system. e peak number of total SAV
trips is about 131342 trips in the TVTS system, while the DDS,
PTS-20% and PTS-40% systems only reach about 118000 trips
with the 2000-SAV eet size.
Results in Figure 9(b) indicate that the empty trips with a
2000-SAV eet size for each SAV system occupy 30–40% of
the total trips served. e percentage of empty trips in the SSS
system has a minimum of 28.1% of the total served 97973 trips
with 2000-SAV eet size, while the DDS reaches a peak of
42.0% with a total of 83480 trips. e percentage of extra
empty trips in the TVTS system is the second-largest percent-
age (40.6%). With a total eet size of 2000 SAVs, SAV systems
seem to generate a higher percentage of empty trips. e high
Especially, the TVTS system is about the same as the PTS-60%
for the percentage of shared rides with a 2000-SAV eet size,
reaching 20.1% of total serviced trips. e PTS systems and
TVTS system, providing two service schemes, can achieve a
relatively high sharing rate of trips. Although the simulation
results for dynamic ridesharing may not give conclusive evi-
dence under designed matching rules to group travelers, the
preliminary investigations of the impact of dynamic rideshar-
ing on dierent SAV systems provide useful insights into the
deployment of dierent SAV systems.
5.4. Analysis of Waiting Time and Service Time. Simulation
results in Figure 8(a) indicate that the average waiting time
in the four PTS systems with dynamic ridesharing has little
dierence, approximately 40–42% of average service time
0
5000
10000
15000
20000
25000
2000 2500 3000 3500 4000 4500
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
SAV eet size
Number of drop-out requests
F 6: Unsatised requests for dierent SAV systems with
variations in eet sizes by optimal vehicle assignment.
0
5000
10000
15000
20000
25000
30000
2000 2500 3000 3500 4000 4500
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
SAV eet
Number of drop-out requests
F 7: Unsatised requests for dierent SAV systems with
variations of eet sizes by FCFS vehicle assignment.
11Journal of Advanced Transportation
smaller eet size can roughly keep 75% of the travelers waiting
10 minutes or less. With the shis of SAV eet size to 3500,
DDS system still has a peak of 14.1% requests whose waiting
time is larger than 10 minutes. Results indicate that PTS-20%
still maintains a high percentage of travelers whose waiting
time exceeds 10 minutes with a 3000-SAV eet size (21%).
erefore, we can infer that the PTS system with a low will-
ingness to choose the station service will lead to a long wait.
Simulation results in Figure 7 indicate that the number of
travelers who give up waiting for a SAV assignment has a sig-
nicant descending trend with the increase of SAV ee sizes
in seven SAV systems. Except for the system with DDS, a eet
of 3500 SAVs can accommodate almost all of the requests. e
eet size that accommodates the total 110000 requests in the
DDS is approximately 4500 SAVs. erefore, e SAV system
only with door-to-door service needs many more SAVs to
handle the high demand. A large number of vehicles in the
eet, has the potential to reduce vehicle utilization. In fact, the
simulation results in Figure 11 reveals that the DDS system
has the lowest number of the served trip per SAV in all
scenarios.
In addition, the numbers of served trips per SAV in
Figure 11 are from 41.7 trips to approximately 49.0 trips in
the SAV system with a 2000-SAV eet size. We nd out that
percentage of empty vehicle trips in DDS and TVTS has the
potential to cause heavy trac congestion.
5.6. Analysis of System Capacity and Drop-Out Requests. With
the 2000-SAV eet (Tables 3 and 4), the peak number of
drop-outs is 24554 trips corresponding to 22.3% of the total
number of requests (110000) in the DDS system, while in the
SSS system this number goes down to 12027 drop-outs, only
accounting for 10.9% of the 110000 requests. e dropout
rate in TVTS system approximates that of PTS-60% system
with a 2000-SAV eet size, reaching 15% of the total number
of requests (110000). e PTS-80% system has the lowest
number of dropouts. It is evident that the PTS system with
a relatively high percentage of willingness to choose station-
based service would be able to accomplish the performance
of the TVTS system.
Results in Figure 10 indicate that the number of trips
whose waiting time exceeds 10 minutes is between 29493 trips
and 16624 trips, going down from 35.3% to 16.9% of system
capacity (total number of served trips) with a 2000-SAV eet
size. e percentage of trips whose waiting time exceeds 10
minutes is about 25% in both PTS-40% and TVTS system,
which are slightly larger than that of PTS-60% and PTS-80%.
Both the TVTS system and PTS system with a relatively
0
2
4
6
8
10
12
14
16
18
20
DDS SSS TVTS PTS-20% PTS-40% PTS-60% PTS-80%
Avg. service time
Avg. waiting time
(a)
0
2
4
6
8
10
12
14
16
18
20
DDSSSS TVTS PTS-20%PTS-40% PTS-60% PTS-80%
Avg. service time
Avg. waiting time
(b)
0
2
4
6
8
10
12
14
16
18
20
DDS SSS TVTS PTS-20% PTS-40% PTS-60% PTS-80%
Avg. service time
Avg. waiting time
(c)
Avg. service time
Avg. waiting time
0
2
4
6
8
10
12
14
16
18
20
DDSSSS TVTS PTS-20%PTS-40% PTS-60%PTS-80%
(d)
F 8:Avg. waiting time and Avg. service time with variations of eet sizes for seven SAV systems. (a) 2000-SAV eet size. (b) 2500-SAV
eet size. (c) 3000-SAV eet size. (d) 3500-SAV eet size.
Journal of Advanced Transportation12
1715-SAV eet size within a network in the scale of 12
miles × 24 miles. at is, each SAV can approximately serve
32.8 trips. e served trips per SAV are relatively lower than
ours. One reason is that the road network in Fagnant and
Kockelman’s study is relatively larger than that of this study.
Another reason is that a relatively large number of vehicles
the PTS-60% and PTS-80% systems present about the same
number of served trips as the TVTS system at about 47 trips
per SAV. We compared the number of served trips per SAV
with Fagnant and Kockelman (2016)’s study. Fagnant and
Kockelman (2016)’s study indicates that the SAV system con-
sidering ridesharing can serve 56324 person-trips with
10 11 12 13
DDS
SSS
TVS
PT
-20%
PT
-40%
PT
-60%
PT
-80%
Number of trips (×10000)
4500-SAV
4000-SAV
3500-SAV
3000-SAV
2500-SAV
2000-SAV
(a)
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
2000-SAV
2500-SAV
3000-SAV
3500-SAV
4000-SAV
4500-SAV
SAV eet size
(b)
40
45
50
55
60
65
70
75
80
2000 2500 3000 3500 4000 4500
TVS
PTS-40%
PTS-60%
DDS
SSS
PTS-20%
PTS-80%
Total VKT (×10000)
SAV eet size
(c)
4500-SAV
4000-SAV
3500-SAV
3000-SAV
2500-SAV
2000-SAV
0246
8
DDS
SSS
TVS
PT
-20%
PT
-40%
PT
-60%
PT
-80%
Energy Consumption (KWh, ×100000)
(d)
F 9:VKT, SAV trips, empty trips of SAVs and energy consumption with variations of eet sizes for seven SAV systems. (a) Total SAV
trips. (b) Percentage of extra empty trips. (c) Total VKT. (d) Energy consumption.
13Journal of Advanced Transportation
are deployed in Fagnant and Kockelman study that leads to
a relatively small average waiting time. e average waiting
times in this paper ranges from 4.41 to 7.70 minutes with a
2000-SAV eet size that are relatively larger than the
1.18-minute average waiting time in Fagnant and Kockelman
st udy.
5.7. Analysis of Empty Trips. Unlike human-driven vehicles
parking at the destinations, SAVs could have an unoccupied
journey to pick up the next request (no pro-active rebalancing
in anticipation of future demand are considered in this
study). erefore, additional empty trips will be generated
to satisfy the next trip. In this study, the additional empty
trips by the vehicle movement between dierent zone
stations are calculated. ese empty trips have the potential
to inuence trac congestion to a great extent. erefore, it
is of importance to know the number of empty trips by SAVs.
As shown in Figure 12, dynamic ridesharing can signicantly
reduce the generation of empty trips. PTS systems have
0
20
40
60
80
100
120
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
ousands
System capacity
Waiting time > 10 minutes
(a)
0
20
40
60
80
100
120
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
ousands
System capacity
Waiting time > 10 minutes
(b)
0
20
40
60
80
100
120
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
ousands
System capacity
Waiting time > 10 minutes
(c)
0
20
40
60
80
100
120
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
ousands
System capacity
Waiting time > 10 minutes
(d)
F 10:System capacity and waiting time > 10 minutes trip number. (a) 2000-SAV eet size. (b) 2500-SAV eet size. (c) 3000-SAV eet
size. (d) 3500-SAV eet size.
20
25
30
35
40
45
50
55
2000 2500 3000 3500 4000 4500
DDS
SSS
TVTS
SAV eet size
Number of served trips per SAV
F 11:e number of served trips per SAV for dierent SAV
systems.
Journal of Advanced Transportation14
6. Conclusions
is paper developed an agent-based simulation model to
assess the potential of on-demand SAV systems with various
service schemes. With the help of the developed ABM, we
understand what the performance of SAV systems with dier-
ent service schemes is, and how the associated factors (varia-
tion of eet size, dynamic ridesharing, dierent vehicle
assignment methods) inuence the service quality of SAV
systems. Our study shows that the promotion of ridesharing
can signicantly improve the performance of the proposed
SAV systems in terms of reducing the average waiting time,
VKT and empty trips. Moreover, compared to the FCFS vehi-
cle assignment method, the optimal assignment can reduce
the generation of empty VKT for all tested systems and enable
the SAV systems to transport considerably more travellers.
Although the DDS system brings great convenience of
doorstep service for real-time requests; it is evident that DDS
generates almost 13% of extra VKT than that of the PTS sys-
tem with a eet size of 2000 SAVs. In addition, the DDS system
generates approximately 42% additional empty trips. e per
-
centage of dropout requests takes up 22.0% of the total 110000
person-trips. at is, the DDS system cannot transport as
many more travelers as the other SAV systems do. Compared
to the DDS system, the TVTS system and PTS systems can
reduce at least 14.6% and 14.8% of the average waiting time
respectively. e empty trips in the TVTS and PTS systems
with dynamic ridesharing account for 41.0% and 33.3% of
total served trips respectively. e TVTS and PTS system pro-
vides a signicant gain in terms of transport capacity, waiting
time and additional trips by empty SAVs. In other words, the
SAV systems that include two dierent on-demand services
have the most signicant improvements in system
performance.
DDS system ranks the highest in total energy consumption
and VKT. Compared to the VKT in the DDS system, the TVTS
system and PTS system can reduce at least 7.6% and 14.0% of
the VKT with 2000-SAV eet size. On the other hand, the
DDD system transports a relatively small amount of travel
requests and reduces vehicle utilization that is the average
number of served trips per day per vehicle. Based on the anal-
ysis of the proposed SAV systems, TVTS and PTS systems are
a promising alternative to be implemented to satisfy the intra-
city transportation needs. In both systems, a SAV can serve
many more trips per day with relatively less waiting time. e
PTS systems with a relatively high percentage of choosing sta-
tion-to-station service show a high level of service that could
transport many more requests with less waiting time and
empty trips. Although the TVTS system could generate many
more VKT and consume much more energy, this system still
has a relatively small waiting time and fewer dropouts with
providing doorstep convenience. In the future deployment of
SAV systems, the station-based service combined with the
door-to-door service parallely in time and space, is of impor-
tance, since blended service could make the system operate at
a relatively high degree of service quality without the incon-
venient access.
the greatest reduction, reaching a peak of 23% in PTS-60%
system; however, there is a large number of additional empty
(unoccupied) trip in all SAV systems. DDS and TVTS system
with dynamic ridesharing generate many more empty trips
at around 40.5% of total served trips. e PTS systems with
dynamic ridesharing generate relatively fewer empty trips than
that of the TVTS system.
Simulation results in Figure 13 indicate the generation of
empty trips with dynamic ridesharing is sensitive to the eet
size. As the eet size increases, the percentage of empty trips
experiences a downward trend. e percentage of empty in all
SAV systems drops below 5% when the eet size is 4500. In
addition, it depicts that the SSS system, TVTS system, and
PTS-80% system have low numbers of empty trips by SAV,
compared with other systems.
0%
10%
20%
30%
40%
50%
60%
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
SAV systems
DRS
Non-DRS
F 12:e percentage of empty trips with dynamic ridesharing
for a 2000-SAV eet size.
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
DDS
SSS
TVTS
PTS-20%
PTS-40%
PTS-60%
PTS-80%
2000
2500
3000
3500
4000
4500
SAV systems
F 13:e percentage of empty trips with the variations of eet
size.
15Journal of Advanced Transportation
[5 ] B. Jager, F. M. M. Agua, and M. Lienkamp, “Agent-based simulation
of a shared, autonomous and electric on-demand mobility
solution,” in IEEE 20th Conference on Intelligent Transportation
Systems, Proceedings, ITSC, IEEEYokohama, Japan, 2018.
[6] H. Dia and F. Javanshour, “Autonomous shared mobility-on-
demand: melbourne pilot simulation study,” Transportation
Research Procedia, vol. 22, pp. 285–296, 2017.
[7] J. B. Greenblatt and S. Saxena, “Autonomous taxis could greatly
reduce greenhouse-gas emissions of US light-duty vehicles,”
Nature Climate Change, vol. 5, no. 9, pp. 860–863, 2015.
[8] N. Masoud and R. Jayakrishnan, “Autonomous or driver-less
vehicles: implementation strategies and operational concerns,”
Transportation Research Part E: Logistics and Transportation
Review, vol. 108, pp. 179–194, 2017.
[9] R. Iacobucci, B. McLellan, and T. Tezuka, “Optimization of
shared autonomous electric vehicles operations with charge
scheduling and vehicle-to-grid,” Transportation Research Part
C: Emerging Technologies, vol. 100, pp. 34–52, 2019.
[10] P. S. Lavieri, V. M. Garikapati, C. R. Bhat, R. M. Pendyala, S.
Astroza, and F. F. Dias, “Modeling individual preferences for
ownership and sharing of autonomous vehicle technologies,”
Transportation Research Record: Journal of the Transportation
Research Board, vol. 2665, no. 1, pp. 1–10, 2017.
[11] D. J. Fagnant and K. M. Kockelman, “Dynamic ride-sharing
and eet sizing for a system of shared autonomous vehicles in
Austin, Texas,” Transportation, vol. 45, p. 143, 2018.
[12] M. W. Levin, K. M. Kockelman, S. D. Boyles, and T. Li, “A
general framework for modeling shared autonomous vehicles
with dynamic network-loading and dynamic ride-sharing
application,” Computers, Environment and Urban Systems, vol.
64, pp. 373–383, 2017.
[13] J. Alonso-Mora, S. Samaranayake, A. Wallar, E. Frazzoli, and
D. Rus, “On-demand high-capacity ride-sharing via dynamic
trip-vehicle assignment,” Proceedings of the National Academy
of Sciences, vol. 114, no. 3, pp. 462–467, 2017.
[14] B. Loeb and K. M. Kockelman, “Fleet performance and cost
evaluation of a shared autonomous electric vehicle (SAEV) eet:
a case study for Austin, Texas,” Transportation Research Part A:
Policy and Practice, vol. 121, pp. 374–385, 2019.
[15] M. Stiglic, N. Agatz, M. Savelsbergh, and M. Gradisar, “Making
dynamic ride-sharing work: the impact of driver and rider
exibility,” Transportation Research Part E: Logistics and
Transportation Review, vol. 91, pp. 190–207, 2016.
[16] N. Agatz, A. Erera, M. Savelsbergh, and X. Wang, “Optimization
for dynamic ride-sharing: a review,” European Journal of
Operational Research, vol. 223, no. 2, pp. 295–303, 2012.
[17] M. Furuhata, M. Dessouky, F. Ordóñez, M. E. Brunet, X. Wang,
and S. Koenig, “Ridesharing: the state-of-the-art and future
directions,” Transportation Research Part B: Methodological,
vol. 57, pp. 28–46, 2013.
[18] C. F. Daganzo, “Checkpoint dial-a-ride systems,” Transportation
Research Part B: Methodological, vol. 18, no. 4-5, pp. 315–327,
1984.
[19] Y. Liu, P. Bansal, R. Daziano, and S. Samaranayake, “A
framework to integrate mode choice in the design of mobility-
on-demand systems,” Transportation Research Part C: Emerging
Technologies, vol. 105, pp. 648–665, 2018.
[20] T. D. Chen, K. M. Kockelman, and J. P. Hanna, “Operations
of a shared, autonomous, electric vehicle eet: implications of
vehicle & charging infrastructure decisions,” Transportation
Research Part A: Policy and Practice, vol. 94, pp. 243–254, 2016.
7. Model Limitations and Future Work
In this model, we did not take into account realistic trac
dynamics. A trac ow model can be implemented in the
model framework to capture the trac dynamics. Moreover,
we did not design the optimal ridesharing rules for travelers
and limited the maximum number of grouped travelers to
two. It is acceptable to use ultra-compact vehicles with
designed two seats. ese low capacity vehicles could keep
travelers in privacy and comfort. We will study the forma-
tion of vehicle platooning between vehicles with low seat
capacity in future research. e small, ultra-compact vehi-
cles could operate together in a platooning fashion to
improve trac capacity and eventually save energy
consumption.
e fact that we use a synthetic network can introduce
some limitations in the study however we also believe that by
having created realistic trip requests and realistic vehicle
movements the small network allows to compare well the dif-
ferent scenarios for the assumptions that were taken. is is
a rst study of exploring many dierent possibilities of oper-
ating the system and in the continuation of this research, we
will expand the size of the network to be analysed.
Data Availability
e data of travel demand and road network used to support
the ndings of this study are available from the corresponding
author upon request.
Conflicts of Interest
e authors declare that there is no conicts of interest regard-
ing the publication of this paper.
Acknowledgments
e rst author would like to thank the China Scholarship
Council (CSC) and the Del University of Technology for the
nancial support towards this publication.
References
[1] F. Kuhnert, C. Stürmer, and A. Koster, Five trends transforming
the Automotive Industry, PricewaterhouseCoopers, London,
UK, 2018.
[2] P. M. Boesch, F. Ciari, and K. W. Axhausen, “Autonomous
vehicle eet sizes required to serve dierent levels of demand,”
Transportation Research Record: Journal of the Transportation
Research Board, vol. 2542, no. 1, pp. 111–119, 2016.
[3] P. M. Bösch, F. Becker, H. Becker, and Kay W. Axhausen, “Cost-
based analysis of autonomous mobility services,” Transport
Policy, vol. 64, pp. 76–91, 2018.
[4] P. Bansal, K. M. Kockelman, and A. Singh, “Assessing public
opinions of and interest in new vehicle technologies: an
Austin perspective,” Transportation Research Part C: Emerging
Technologies, vol. 67, pp. 1–14, 2016.
Journal of Advanced Transportation16
[21] D. Milakis, B. van Arem, and B. van Wee, “Policy and society
related implications of automated driving: a review of literature
and directions for future research,” Journal of Intelligent
Transportation Systems, vol. 21, no. 4, pp. 324–348, 2017.
[22] P. A. Hancock, I. Nourbakhsh, and J. Stewart, “On the future
of transportation in an era of automated and autonomous
vehicles,” Proceedings of the National Academy of Sciences of the
United States of America, vol. 116, no. 16, pp. 7684–7691, 2019.
[23] P. M. Boesch and F. Ciari, “Agent-based simulation of
autonomous cars,” in Proceedings of the American Control
Conference, IEEE Chicago, IL, USA, 2015.
[24] L. D. Burns, J. William, and B. Scarborough, Transforming
Personal Mobility, e Earth Institute, New York, NY, USA,
2013.
[25] K. Treleaven, D. Morton, R. Zhang, M. Pavone, E. Frazzoli, and
K. Spieser, Toward a Systematic Approach to the Design and
Evaluation of Automated Mobility-On-Demand Systems: A Case
Study in Singapore, Springer, Cham, 2014.
[26] D. J. Fagnant, K. M. Kockelman, and P. Bansal, “Operations
of shared autonomous vehicle eet for Austin, Texas, Market,”
Transportation Research Record: Journal of the Transportation
Research Board, vol. 2563, no. 1, pp. 98–106, 2016.
[27] D. J. Fagnant and K. M. Kockelman, “e travel and
environmental implications of shared autonomous vehicles,
using agent-based model scenarios,” Transportation Research
Part C: Emerging Technologies, vol. 40, pp. 1–13, 2014.
[28] W. Zhang, S. Guhathakurta, J. Fang, and G. Zhang, “Exploring
the impact of shared autonomous vehicles on urban parking
demand: an agent-based simulation approach,” Sustainable
Cities and Society, vol. 19, pp. 34–45, 2015.
[29] L. M. Martinez and J. M. Viegas, “Assessing the impacts of
deploying a shared self-driving urban mobility system: an
agent-based model applied to the city of Lisbon, Portugal,”
International Journal of Transportation Science and Technology,
vol. 6, no. 1, pp. 13–27, 2017.
[30] J. Viegas, L. Martinez, and P. Crist, Shared Mobility: Innovation
for Liveable Cities, International Transport Forum, Paris, 2016.
[31] J. Zachariah, J. Gao, A. Kornhauser, and T. Mui, “Uncongested
mobility for all: a proposal for an area wide autonomous taxi
system in New Jersey,” in Transportation Research Board
93rd Annual Meeting, e National Academies of Sciences,
Engineering, and Medicine, Washington, DC, USA, 2014.
[32] X. Liang, G. H. A. Correia, and B. van Arem, “Optimizing the
service area and trip selection of an electric automated taxi
system used for the last mile of train trips,” Transportation
Research Part E: Logistics and Transportation Review, vol. 93,
pp. 115–129, 2016.
[33] A. Scheltes and G. H. A. Correia, “Exploring the use of automated
vehicles as last mile connection of train trips through an agent-
based simulation model: an application to Del, Netherlands,”
International Journal of Transportation Science and Technology,
vol. 6, no. 1, pp. 28–41 , 2017.
[34] H. W. Kuhn, “e Hungarian Method for the Assignment
Problem,” In 50 Years of Integer Programming 1958-2008,
M. Jünger, T. M. Liebling, and D. Naddef, Eds., Springer, Berlin,
2010.
[35] X. Wang, T. Fan, W. Li et al., “Speed variation during peak and
o-peak hours on urban arterials in Shanghai,” Transportation
Research Part C: Emerging Technologies, vol. 67, pp. 84–94, 2016.
Content uploaded by Senlei Wang
Author content
All content in this area was uploaded by Senlei Wang on Dec 17, 2019
Content may be subject to copyright.
