IMPACTS OF AUTONOMOUS VEHICLES (AVS) ON …€¦ · Soumen Chakraborty AITPM 2017 National Conference IMPACTS OF AUTONOMOUS VEHICLES (AVS) ON TRANSPORT SYSTEMS IN THE …

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


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


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


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


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


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


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


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


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


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


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


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.

5. References

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networks”, The Netherland Trail Research School, Delft University Press, The Netherlands,

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IMPACTS OF AUTONOMOUS VEHICLES (AVS) ON …€¦ · Soumen Chakraborty AITPM 2017 National Conference IMPACTS OF AUTONOMOUS VEHICLES (AVS) ON TRANSPORT SYSTEMS IN THE …