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Soumen Chakraborty
AITPM 2017 National Conference
IMPACTS OF AUTONOMOUS VEHICLES (AVS) ON TRANSPORT
SYSTEMS IN THE (NOT SO DISTANT) FUTURE: A REVIEW OF
LITERATURE AND SUMMARY OF FINDINGS
SOUMEN CHAKRABORTY
WSP
Institute of Transport and Logistics Studies, The University of Sydney
Abstract: Autonomous Vehicles (AVs) are widely assumed to play a game-changing role in
the transport industry in the near future. AVs are expected to contribute to road safety
improvements, congestion mitigation and may offer greater mobility through road space and
public transport system efficiency gains. Governments, road agencies, suppliers and private
operators around the world are seeking to better understand potential impacts on their future
transport systems so that they can be seen as being proactive through the preparation of short
and long-term action plans for implementation. Although many uncertainties about this
new transport mode exist, such as market penetration rates, legal issues, safety and licensing,
affordability, infrastructure requirements, technological hurdles etc., it is evident from a
literature review that AVs may significantly affect our daily travel behaviour and road
capacity expectations. Research indicates that road capacity improvements can vary
significantly based on AV penetration rates and the nature of the road environment (urban,
rural or motorway). Travel patterns may also be impacted and more people may travel longer
distances as the value of travel time (VTT) may decrease in response to increasing travel
comfort inside AVs. These potential transport system impacts would have implications for car
parking policies, rates and infrastructure requirements. Two of the most common questions on
AVs are: (i) what impacts would AVs have on our mobility? (ii) And how would our
cities respond to these changes in the future? Based on a comprehensive literature review, this
paper explores at a strategic conceptual level, the potential impact of AVs on future transport
network modelling.
1. Background
Autonomous Vehicles (AVs) are widely anticipated to play a game-changing role in
the transport industry in the near future. AVs are expected to contribute to road safety
improvements, congestion mitigation and may offer greater mobility through road
space and public transport (PT) system efficiency gains (Milakis et al.2016, Winston
and Mannering 2014).
Although many uncertainties about this new transport mode exist, such as market
penetration rates, ownership/usage models, legal issues, safety and licensing,
affordability, infrastructure requirements, technological hurdles and more, the wider
literature clearly paints a future wherein AVs could significantly affect our daily travel
Soumen Chakraborty
AITPM 2017 National Conference
behaviour and road capacities. AVs are anticipated to change travel patterns by
lowering the values of travel time. This is because AVs (in theory at least) will provide
more comfort, travel time reliability and multitasking opportunities while travelling
(Milakis et al. 2015). Road capacity improvements can vary significantly based on AV
penetration rates, the nature of the (urban, rural or motorway) road environment,
behavioural adaptation and deployment paths (Milakis et al. 2015).
AVs are classified according to different levels of automation, ranging from level 0
(no automation) to level 5 (full automation) (SAE International 2014, National
Highway Traffic Safety Administration NHTSA 2013, Australian Driverless Vehicle
Initiative 2015), See Figure 1.1. This report is focused on full automation level(s) 4
and 5 (SAE, ADVI, VDA) and level 5 (NHTSA).
Figure 1.1: Levels of Automations (Source: Federal Highway Research Institute;
German Association of the Automotive Industry (VDA)
Impacts of AVs on travel behaviour and road capacity will largely depend on the
introduction of AVs in the market and the percentage of AVs on the road (penetration
rates) with conventional cars and other transport modes. Several studies have explored
the deployment and penetration rates of AVs based on surveys primarily in a US
context. Those studies (Litman 2014, Underwood 2014, Kyriakidis et al. 2015, Zmud
et al. 2015, Townsend 2014) summarised that the possible introduction of AVs in the
market would vary between 2018 and 2025 and the penetration rates of AVs would
significantly increase from the year 2035. It is expected that Level 5 AVs would reach
50% penetration rate by 2050 (cited in Milakis et al. 2016). Willumsen (2016) found
similar outcomes after conducting a Delphi exercise with transport professionals
Soumen Chakraborty
AITPM 2017 National Conference
around the world which indicated that AVs will be available to purchase from the year
2023 and the penetration rate will be 20% by 2037.
In the Dutch context, Milakis et al. (2016) carried out a scenario analysis to identify
the possible deployment paths of AVs and their impacts on transportation in
Netherlands for the years 2030 and 2050. According to this study, AVs are expected to
be commercially available in the Dutch car market between 2025 and 2045 and the
penetration rate will rapidly increase after their introduction from about 10% in 2030
to 65% in 2050. However, uncertainties in policy making and technological
advancement in road infrastructure improvements would have impacts on the
deployments and the market share of AVs. Also, the willingness to purchase
automotive emerging technologies will also influence AV market share. Several
surveys (Power 2012, Sommer 2013, Missel 2014) showed that the public opinions are
in favour (generally more than 50% of the sample size) of the use of AVs, but
responses were varied based on the prices of AVs, income, age, gender and different
countries (cited in Kyriakidis et al. 2015). It was also clear that public opinions on
AVs are diverse because of lack of knowledge on AVs and a number of concerns such
as safety, insurance, legal issues, software hacking, sharing data to the third parties
(Kyriakidis et al. 2015, Willumsen 2016).
AVs would have wider implications on transport infrastructures, transport modes,
environment, travel safety, economy, social equity and public health. This new
transport mode will change car parking policies, rates and infrastructure requirements.
AVs are likely to transform the public transport system which would provide more
flexibility in the mobility of passengers.
Two of the most common questions on AVs are: (i) what impacts would AVs have on
our mobility? (ii) how would our cities respond to these changes in the future? Based
on a comprehensive literature review in the following sections, this report explores the
current research outcomes that will assist in understanding the potential impacts of
AVs on the future transport system. This paper also provides an overview on the
development of a current research proposal on AVs to build a framework of a (proto-
type) transport model to test different future scenarios and transport policies.
Soumen Chakraborty
AITPM 2017 National Conference
2. Literature review
This section investigates the existing literature on AVs mainly focusing on transport
system modelling. More literature papers will be reviewed as part of the on-going
research proposal development. To give a more structured format of literature review,
firstly transport demand side and then transport supply side are discussed in the
following sections.
2.1 Demand Side
The transport demand side is represented by the number of trips made for passenger
and freight transport. Travel choice behaviour influences the travel demand on the
road. There are many behavioural dimensions, including household activities, car
ownership, car sharing, trip chaining, mode choice, and route choice. It is expected
that AVs will impact almost all dimensions of travel behaviour in the future.
Current travel behaviour models that are used in transport planning are not able to
capture shifts in behaviour due to the introduction of AVs. AVs do not merely
represent a new transport mode alternative, but rather are expected to have a system
wide impact on the travel demand across many behavioural dimensions as mentioned
earlier. This makes including AVs in existing travel behaviour models a challenging
task. For example, AVs will likely affect public transport use for which AVs can be
used as and access and egress mode, and AVs will also likely change how drivers
value travel time.
In the 2016 Automated Vehicles Symposium, research on the impacts of travel choice
behaviour by AVs was discussed briefly (Autonomous Vehicles Symposium 2016).
All research aspects were grouped in five approaches: i) Perform simulation based /
scenario analysis studies ii) Stated preference surveys iii) Virtual reality / Games /
Simulators iv) Revealed Preference / naturalistic experiments and v) Qualitative.
Detailed questionnaires were designed to investigate behavioural responses of
travellers with respect to AVs. The awareness, knowledge and experiences of the
survey participants on AVs were key challenges to understand the adoption of this new
technology. Avoiding behavioural bias was also important to measure adoption
intensions. It was revealed that many research topics were work-in-progress and some
outcomes will be released in the future research papers.
As the technology evolves, the implications of AVs would be wider. The potential
benefits of AVs in reducing traffic congestion and parking demands, and increasing
safety and accessibility, are anticipated to the deployment of AVs and the penetration
Soumen Chakraborty
AITPM 2017 National Conference
rates in the traffic flow. Further, trips are expected to become longer on average
(Milakis et al. 2016). Table 2.1 summarises key points from existing research on the
influence of AVs on travel behaviour.
Table 2.1: Existing research on the influence of AVs on travel behaviour
Topic Researcher Methods Outcomes Influence of
demographic
changes and
travel
behaviour by
AVs
Li et al.
(2016), US
Develop and test
scenarios using the
SE Florida activity-
based model(ABM)
to explore model
sensitivities and
identify areas of
data need
- Average trip length would be
increased, but longest trips would be
reduced
- No of stops would be increased per
tour
- More activities would be introduced
It was revealed by the ABM that
complexity for model development and
computation time would be increased.
Forecasting
future travel
behaviour
resulting from
AV usage
Kuppam et
al. (2016),
US
Test scenarios
and/or ranges of
variables in a series
of ABM
components
Vehicle ownership model will be
changed. Two car family model will be
reduced to one car and shared AVs.
Preferences &
plans for AV
technology
Kockelman
et al. (2016),
US
By three online
surveys - Americans‟ average WTP (among
those with WTP >$0) for Level 4
($14,589) is much higher than that to
add Level 3 automation ($5,551)
- Austinites WTP for adding Level 4
automation ($7,253) is much higher
than adding Level 3 ($3,300) to
current vehicles
- Texans ‟average WTP varies from
$2,910, $4,607, $7,589, & $127 for
Level 2, 3, & 4 automation.
User
preference
regarding
AVs
Shiftan et al.
(2016),
Israel
stated choice
experiments,
attitude &
perception
measurements,
discrete choice
- Large overall hesitation toward AV
adoption (44% still choose regular
vehicles)
- Early adaptors of automation are:
younger, students, better educated,
spend more time in vehicles
- SAV users are: younger, commute
less than 5 days a week,
environmental concern, and PT
prons.
- Latent variables of “enjoy driving”
(regular car), “environmental
concern” (SAV), and “technology
interest” (AV) are the strongest
variables
(Source: Autonomous Vehicle Symposium 2016)
Soumen Chakraborty
AITPM 2017 National Conference
2.2 Supply Side
The supply side of the transport system – which includes infrastructure, transfer and
parking facilities, transport services and vehicles, and traffic controls – will be
impacted by the introduction of AVs in several ways. Road capacity will be impacted
based on the percentage of AVs penetration and road type. AVs may also change
traffic control at intersections. Further, AVs are expected to extend linkages between
multiple modes of transport, thereby influencing the multimodal transport network and
resulting route alternatives.
Most of the research covers the impacts of AVs in traffic flows and their penetration
rates in the mixed traffic conditions. The majority of studies are based on micro-
simulations combined with field tests. The outcomes of these studies indicated an
expected increase in road capacity of more than 10% when the penetration rate of AVs
is higher than 40% and 100% penetration rates could theoretically double the capacity
of the road compared with a scenario of 100% manually driven vehicles (Arnaout and
Bowling 2011, and Shladover et al. 2012). Michael et al. (1998) indicated that the
capacity of a single lane automated highway system can be increased by increasing the
level of vehicle co-operation and platoon length (cited in Milakis et al. 2016).
Theoretical capacity of the road segment can be increased by autonomous control
system and the capacity can be doubled by allowing a co-operative system (vehicle to
vehicle and vehicle to infrastructure) comprising of 10-vehicle platoons with 6.5 meter
distance between vehicles (Rajamani and Shladover 2001, cited in Milakis et al. 2016).
Another group of studies were carried out on automated intersection control systems
(Clement et al. 2004, Kamal et al. 2015). Their studies indicated that the intersection
throughputs can be significantly increased (100% to 169%) by lowering the vehicle
spacing of AVs and coordinating connected AVs at intersection with no traffic signal
environment (cited in Milakis et al. 2016).
Fagnant and Kockelman (2014) used an agent based simulation technique to estimate
the required fleet size of shared autonomous vehicles (SAVs) by servicing all trips
reasonably in a grid based urban type area. To minimise the travellers waiting time
when the SAVs are called, the model considered several relocation strategies in the
network. The outcomes of the study indicated that eleven conventional vehicle trips
can be replaced by one SAV.
SAV can be used as a transit system which will be cheaper than a taxi service
(Martinez et al. 2014, cited in Correia and van Arem 2016). A study in Lisbon,
Portugal suggested that 100% fleets of automated taxis with only the metro transit
system could remove 9 out of 10 cars in the city and without the metro, 5 cars would
Soumen Chakraborty
AITPM 2017 National Conference
be removed by AVs (The International Transport Forum (ITF), 2015). Another study
in Manhattan in New York City identified that 60% of current taxi demand can be
replaced by a fleet of 8,000 AVs (Zhang and Pavone 2014, cited in Correia and van
Arem 2016). It was also identified that the replacement of all vehicles with AVs can be
done by one third of the total number of passenger vehicles in Singapore city (Spieser
et al. 2014).
AVs can be privately owned in which the concept of a two car household may change
to a single AV household (an AV can serve multiple household members). Correia
and van Arem (2016) established a methodology for the User Optimum Privately
Owned Automated Vehicles Assignment Problem (UO-POAVAP). That methodology
identified that privately owned AVs can satisfy more trips than a single car and reduce
generalised costs as the AV can park itself at lower parking spots or can drive back
home to satisfy other household trips. In general, it may increase the number of cars on
the street as the empty car needs to be relocated, but on the other hand it may also
reduce the congestion by using some other routes which were not cost efficient before
for conventional cars. The methodology was developed by combining traffic
assignment (TA) methods and vehicle routing problems (VRPs) for privately owned
AVs only and did not include the concept of shared AVs or PT networks to better
characterise the competition of mode alternatives.
However, no study exists that considers an integrated approach in which all modes of
transport, including AVs, are considered simultaneously. Therefore, there is a gap in
the understanding that what changes AVs would bring in multimodal transport
network context and how this can be modelled so that different future scenarios can be
tested to predict the change of future mobility. Since multimodal network modelling
will play a key role in this research, we include here an overview of state-of-the-art
approaches that have been proposed in the literature.
Van Nes (2002) presented a multimodal transport network concept where uni-modal
transport networks are interconnected via waiting and walking links. A route in a
multimodal network describes not only the travel paths but also the sequence of
transport modes and connections. This approach is an extension of a generic
conceptual framework of travel choice behaviour (Bovy et al. 1990, cited in Van Nes
2002). Lanser (2005) developed a theoretical framework of the multimodal route
choice process where the choice sets were generated by data collection on multimodal
travelling and different discrete choice models were applied to establish traveller‟s
preferences for different aspects of multimodal trips. During choice set generation, it
was identified that the variation of train services and accessibility to the train stations
and their locations play a dominant role in choosing a multimodal trip with train as a
Soumen Chakraborty
AITPM 2017 National Conference
main mode. Brands (2015) worked on the optimisation of multimodal passenger
transportation networks. A mathematical approach was adopted for optimisation by
formulating a multiple objective network design problem (MO-NDP). A Pareto set of
multimodal network options was generated by considering possible network design
options and the associated sustainability scores. This research estimated multimodal
trips by considering existing and new park and ride facilities, train stations and public
transport service timetables. Catalano-Fiorenzo (2007) analysed multimodal travelling
based on a supernetwork methodology in which network of all individual modes are
combined into a single supernetwork with transfer legs between modes. Multiple
choice dimensions in multimodal trips were generated in the supernetwork. This
method explicitly generated individual route choice alternatives prior to the choice
modelling. The main contribution of this research was a new route choice set
generation algorithm (doubly stochastic approach) where travellers‟ preferences are to
be created with respect to route attributes in multimodal network environment.
3. Proposed research
Research on AVs initially concentrated on the supply side by estimating the impacts
on the road capacity using simulation studies. In recent years there has been an
increasing focus on the demand side by investigating the influence of AVs on different
travel behaviours through surveys. Currently, we are currently working on a research
work by considering an integrated approach in which the supply side is represented by
a multimodal network and the demand side considers all (or the most relevant)
components of travel behaviour.
A multimodal network model will be developed to uncover trip choice, mode choice,
route choice and perhaps departure time choice in the presence of AVs. A (proto-type)
multimodal network model so that uncertainties about the impact of AVs can be
predicted with number of different assumptions and scenarios, such as influence of
AVS on the private vehicle ownership model, change of car parking policies and
infrastructure or road capacity improvements by AVs. The proposed research
questions will go further to uncover the behaviour of users of AVs which will be the
inputs into the prototype model.
Soumen Chakraborty
AITPM 2017 National Conference
Figure 3.2: Proposed research methodology (Supply side)
The proposed research includes three overarching objectives:
1. Clearly understand the influence of AVs on the transport network in the
multimodal context so that network efficiency and gaps can be identified to
improve the transport mobility
2. Investigate how the future transport technologies like AVs would allow us to
get the full value of transport networks
3. Set up directions for transport agencies by testing different future AV scenarios.
To achieve the above three main objectives, a detailed methodology has been
developed to deliver a proto-type multimodal network model in the context of AVs.
Within the proto-type model, the methodology will develop a modelling framework
and formulation that allows sufficient flexibility to include AVs in the multimodal
transport network modelling by bringing both demand and supply side into one frame.
The following section describes the research methodology.
3.1 Scenario Tests
Following five possible scenarios will be considered for the assessment of a
multimodal transport network in the context of AVs.
Scenario 1: Influence of AVs in the vehicle ownership model. It is expected that the
vehicle ownership model will be greatly influenced in the future. In this scenario, it
will be considered that people still would like to have their own private cars, but all the
private cars will be fully autonomous. As the AVs will provide greater flexibility in
the mobility system and working options during in-vehicle time, then two or three
Soumen Chakraborty
AITPM 2017 National Conference
individual trips from single household can be combined as a single trip. It means trip
frequency will be reduced with the influence of the privately owned AVs.
Scenario 2: Influence of shared vehicle ownership or share the ride with others. The
vehicle ownership model will have major shift towards the shared mobility-on-demand
services. Autonomous vehicles will improve this car share or ride share concept by
adding values in travel cost, comfort and flexibility of travel. It is expected that shared
AVs fleet service will substantially reduce the feeder bus service from the
transportation hub and gradually reduce the fixed route bus service by providing more
personalised transport service.
Scenario 3: Influence of car parking policies and parking infrastructures to determine
the travel choice behaviour with and without AVs environment.
Scenario 4: Influence of automated public transport services in the multimodal
transport network. In this scenario, travellers can make real-time request to transport
service providers such as automated shuttle bus service which would reduce waiting
time and transport costs.
Scenario 5: Road capacity enhancement by AVs. It is expected that based on the
mixture of AVs with conventional cars on the road, the road capacity will be hugely
varied in the future. The variation of road capacity by AVS will be also dependable on
the road characteristics such as motorways, highways, arterial collector or local roads.
This scenario will be developed based on the variation of lane capacity which is
directly linked with traffic assignment algorithm and thus traffic flows on the road
link.
Each scenario can be considered as a combination of different policies, such as three
types of car ownership/sharing, two types of capacity (low/high) and two types of
parking cost strategies etc. Though the above scenarios will be considered to test in the
proto-type model, other scenarios may also have potential impacts on future mobility.
At lease, the end-state uncertainty of AVs will be determined by these five scenarios.
Soumen Chakraborty
AITPM 2017 National Conference
Figure 3.3: Proposed scenarios of AVs
4. Conclusion
In transport network modelling, travel behaviour and transport network are
interconnected. One has great influence on other. If the travel behaviour will be
changed by the influence of AVs, then the requirement of future transport
infrastructure will be changed. Oppositely, if the transport infrastructure is ready for
AVs, then the acceptance of this future transport technology will be faster.
Introduction of AVs (level 3 to 5) on the road is widely varied by different sources.
But, it is evident from literature review that this new transport technology will
influence our daily travel behaviours and the requirements of future transport
infrastructure. Governments, road agencies, suppliers and private operators around the
world are seeking to better understand potential impacts on their future transport
systems so that they can be seen as being proactive through the preparation of short
and long-term action plans for implementation.
We are currently developing a framework of transport network modelling for AVs to
determine the impacts of AVs on transport network system so that the influence of
AVs on travel choice behaviours and traffic assignment can be predicted. The research
outcomes will provide a direction to the key government agencies about AVs; such as
the impacts of privately owned or shared AVs, the change of parking policy or
infrastructure or the enhancement of network capacity and many other scenarios. In
Soumen Chakraborty
AITPM 2017 National Conference
the next AITPM 2018 seminar, it is expected that some pilot research outcomes will be
presented to the industry.
Acknowledgments
This paper draws from a draft PhD research proposal at the Institute of Transport and
Logistics Studies at the University of Sydney. The author appreciates the comments of
the two supervisors, Prof Michiel Bliemer and Dr Matthew Beck, on the draft research
proposal.
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AITPM 2017 National Conference
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