Human Factors, User Requirements,
and User Acceptance of Ride-Sharing
In Automated Vehicles

10

Discussion Paper 2017 • 10

Natasha Merat and Ruth Madigan
Institute for Transport Studies,
University Of Leeds, Leeds, UK

Sina Nordhoff
Transport & Planning, Delft Universityof Technology, the Netherlands

Human Factors, User Requirements, and User
Acceptance of Ride-Sharing in Automated
VehiclesDiscussion Paper No. 2017-10

Prepared for the Roundtable on
Cooperative Mobility Systems and Automated Driving(6-7 December 2016)

Natasha Merat and Ruth Madigan
Institute for Transport Studies, University Of Leeds, Leeds, UK
Sina Nordhoff
Transport & Planning, Delft University of Technology, the Netherlands Innovation Centre for
Mobility and Societal Change, Berlin, Germany

July 2017

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Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Acknowledgements

Some of the work cited in this paper is linked to the studies conducted by Merat and Madigan for
the CityMobil2 project, funded by the European Commission Seventh Framework Programme (GRANT
AGREEMENT N°: 314190). The CityMobil2 project also initiated the collaboration with the third
author, Ms Sina Nordhoff, who is currently a PhD student at Delft University of Technology, conducting
her PhD on user acceptance of highly automated vehicles in public transport. This PhD is funded by the
Innovation Centre for Mobility and Societal Change in Berlin.

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Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Abstract

This paper provides an overview of the social-psychological factors that are likely to influence thetrust and acceptance of shared SAE Level 4 Automated Vehicles (AVs). It begins with a shortsummary of what influences users’ engagement in ride-sharing for conventional vehicles, followedby the factors that affect user acceptance and trust of robotic systems. Using studies of human robot
interaction (HRI), recommendations are made on how to improve users’ trust, acceptance and use ofshared AVs. Results from real-world studies and on-line surveys provide some contradictory views
regarding willingness to accept and use the systems, which may be partly due to the fact that on-line
users have not had actual interactions with AVs. We recommend that the pathway to adoption andacceptance of AVs should be incremental and iterative, providing users with hands-on experience ofthe systems at every stage. This removes unrealistic, idealised, expectations, which can ultimately
hamper acceptance. Manufacturers may also use new technologies, social-networks and crowdsourcing techniques to receive feedback and input from consumers themselves, in order to increase
adoption and acceptance of shared AVs.

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Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Table of Contents

Introduction .................................................................................................................................. 6
Overview of social-psychological factors affecting the use of shared ...................................... 7
conventional vehicles .................................................................................................................... 7
Users’ desire to engage with AVs – web-based studies ............................................................. 9
Factors influencing trust and acceptance of AVs ..................................................................... 10
Distinguishing between acceptance and acceptability ............................................................. 10
Use of social-psychological models to study user acceptance of AVs .................................... 10
From acceptance to trust .......................................................................................................... 12
Real-world studies on user acceptance of ride-sharing AVs .................................................... 15
User willingness to share a small unattended vehicle with strangers .................................... 16
Realistic opportunities to serve mobility-impaired users ....................................................... 17
Interactions of AVs with pedestrians and bicyclists ................................................................. 18
Summary and conclusions ......................................................................................................... 20
Bibliography ............................................................................................................................... 23

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Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Introduction

This paper offers an overview of some of the social-psychological factors likely to affect car- andride-sharing for automated vehicles (AVs), providing an indication of the issues that will affect if, how
and when citizens will need, utilise and be prepared to pay for automated ride-sharing systems. It is
hoped this information can be used by policy makers and city authorities, when attempting to establish
the best model for deploying such systems. The paper summarises key results from a number of recent
surveys conducted on citizens around the globe, and the small number of actual demonstration projects,
where users’ views are sought during AV trials. However, likely caveats exist for both sets of data: theformer simply rely on participants’ image or perception of the systems, as portrayed by the popular/social
media, or may well be developed following the descriptions provided by the survey
questions/investigator. On the other hand, large-scale and commercially viable AVs, which are shown tobe fully reliable and operating at an optimal level, are not yet available for general public use1. In
addition, most such vehicles are currently based on prototypes, which are being tested to identify any
operational and technological shortcomings. Assessments include how such AVs can be deployed in thecurrent road network, and how the fast developing (but not yet optimal) mapping and communications
technologies influence successful operation. Consequently, seeking users’ views of their benefit will
likely reflect such shortcomings, a factor which must be taken into account when assessing user response
to current AVs.
Therefore, as well as considering studies which take users’ impression of AVs for mass-transit use
and ride-sharing purposes into account, this paper will provide a short summary of our current
understanding of the social-psychological factors that have contributed to the success, or otherwise, ofconventional ride-sharing schemes. This will include a consideration of if, and how, these factors arelikely to be affected by what is surely the fundamental difference between the AV and conventional
vehicles: the driver. Here, the analysis will mostly be based on SAE Level 4 vehicles (SAE, 2016),
which, for legal and safety reasons, are likely to have an operator present, at least for the foreseeable
future (DfT, 2015). It is also worth mentioning that this paper only considers the potential ride-sharing
opportunities of publically-owned or -used version of such vehicles, rather than those owned byindividuals, for private use. At present, the former category of vehicle has been predominantly deployed
at low speeds in urban settings, and is electrically powered. The primary purpose of these vehicles is toprovide first mile/last mile transit solutions for the urban environment, and they are also designed tocontribute to the reduction of transport-related noise and emissions. Since successful deployment of thisform of AV is likely to rely heavily on user acceptance, buy-in, and uptake, an understanding of how
these services assimilate with already established public transport systems is crucial. Another importantfactor to consider here is whether this category of transit provides a ‘superior’ service to the consumer,
for instance in terms of total journey time, value for money, network and locations served. Finally, weneed to understand how these forms of AV complement (or affect the use of) other modes of transport,
including the users’ own vehicle.
In preparing the first draft of this paper for the OECD round table discussion, the authors wereasked to focus on a number of main topics, as outlined below:
1. User willingness to share a small unattended vehicle with strangers
2. User willingness to trust AV technology -- how to achieve adequate, but not excessive, trust?
3. Interactions of AVs with pedestrians and bicyclists
1

At the time of writing, data from only one or two publically-funded projects testing fully-automated (SAE level4) ride-sharing AVs wereavailable to the authors. However, we are also aware that many more such studies are currently in progress, with a number also considering thesocial-psychological aspects discussed here.

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4. Managing user expectations or mental models about AV capabilities
5. Realistic opportunities to serve mobility-impaired users (children, seniors, handicapped)
Therefore, broadly speaking, the paper focusses on the above concepts, although additional content
is provided, where appropriate, to strengthen some of the arguments. This includes a short overview ofthe main social-psychological factors affected the use of shared conventional vehicles, which we
assumed would also users’ willingness to shared AVs.

Overview of social-psychological factors affecting the use of sharedconventional vehicles

To date, a large number of studies have considered the success of car and ride-sharing withconventional vehicles. However, in a recent literature review of the area, focussing in particular on thetransfer aspect of a public transport trip, Chowdhury and Ceder (2016) emphasise that much of theresearch has thus far concentrated on improving and understanding the operational aspects of the trips,
rather than understanding the psychological and policy aspects of such behaviour.
A number of such schemes have been in place for over three decades, including, for example, thosearranged by organisations for their employees (see e.g. Dueker et al., 1977). The introduction of new‘apps’ and on-line technologies has also meant that remotely organised car and ride-sharing schemes are
increasingly on the rise (see below). As with AVs, the main motivation for the introduction of such
conventional ride-sharing schemes has been based on the desire to reduce:

traffic volume, fuel use and any associated emissions; (IEA, 2005; Minett andPearce, 2011)

the need for parking spaces, and;the cost of travel for its users (TDM Encyclopaedia, 2012);



At the same time, they are considered attractive because they are believed to:provide a more convenient service than public transport (or an alternative forareas without such provisions);

reduce driver fatigue (MAIF, 2009), which can then enhance productivity, and;

improve social interaction (Agatz et al., 2012).

According to a recent paper by Delhomme and Gheorghiu (2016), conventional vehicle
carpooling/sharing has successfully achieved many of the above aspirations (see also Ballet and Clavell,
2007). In terms of social-psychological determinants, a number of factors are likely to work together todetermine users’ willingness to engage in car and ride-sharing schemes, ideally, in exchange for utilising
their own vehicle. These include the nature of the trip itself, such as its purpose, the length of thejourney, the time of day (Chowdhury and Ceder, 2016; Malodia and Singla, 2016), the make and modelof the vehicle (Kawgan-Kagan, 2015), as well as demographic determinants such as users’ age, gender,socio-economic status (Vanoutrive et al., 2012) and personality (Roy, 2016).
Regarding demographics, in particular, studies show rather mixed findings. For instance,
Kuhnimhof, Chlond, and von der Ruhren, (2006) found that when it comes to trips other than
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commuting, women are more likely to use these services, than men. Likewise, a recent survey of
1 207 drivers in France (52% of whom had carpooled), reports that women with children, who were also
eco-conscious and had a positive view of public transport were the most likely demographic to use thiskind of scheme (Delhomme and Gheorghiu, 2016). On the other hand, looking at e-car-sharing
subscribers, Kawgan-Kagan (2015) reports this group to be over represented by men. Yet, she argues thatwomen are likely to benefit from car and ride-sharing schemes, since this demographic normally has amore complex and varied mobility pattern: taking many shorter trips in the day to address a number ofdifferent personal and family-related errands. Organisations and policy makers are therefore encouraged
to consider the needs of this demographic, when assessing methods for enhancing car-sharing uptake.
Using a Stated Preference web survey, Malodi and Singla (2016) confirm that, for conventional
vehicles, two main factors affect consumers’ desire for carpooling: time and cost. In terms of time, theauthors considered both comfort and convenience in their analysis, arguing that aspects such as (i) thetime it takes to reach the carpooling facility (ii) any waiting time at the pickup point, and (iii) the timeused for picking up all other car poolers will all have an effect on the decision to use such facilities. In
terms of consumers’ impressions of costs associated with carpooling schemes, Malodia and Singla(2016) propose that increased use will be achieved by suitable promotional campaigns, which, forexample, highlight the likely cost savings associated with such schemes. Furthermore, since the attraction
associated with privately owned vehicles includes factors such as the provision of privacy (Gardner andAbraham, 2007), convenience, and comfort, not only allowing more freedom of movement(anytime/anywhere), but also affording more in terms of (driving) pleasure and higher social status(Beirao, and Sarsfield- Cabral, 2007), promotional campaigns should also consider what benefits the user
will gain from ride-sharing, when encouraged to surrender their own vehicle, in favour of such services.
The use of suitable and targeted campaigns is also relevant when attempting to increase consumers’
uptake of shared AVs. This uptake is also likely to be influenced by the ever-increasing availability ofmobile apps, which are starting to provide consumers with Mobility as a Service (MaaS), whereIntelligent Transport Systems (ITS) will create a tailor-made travel service for consumers (seehttp://maas-alliance.eu/). Furthermore, as additional social networking and crowd sourcing capabilities
are added to such apps (e.g. see waze.appurse.com), consumers can potentially assist one another,transport suppliers/planners and app developers alike, to understand how the combination of available
transport services are likely to influence factors such as travel time and cost. In addition, users can obtain
information about the likely fuel cost of a journey, choose their preferred route by opting for the
shortest/fastest option, or by selecting their favored type of road. Crowd sourcing will even allow
scenery-based route selection, the attraction of which will have been be voted by others. Finally, for ridesharing opportunities, users can choose like-minded companions, by joining social networking apps (seewww.blablacar.co.uk, and Figure 1).
Figure 1. Use of social networking facilities to enhance ride-sharing experiences

Source: blablacar.co.uk

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Users’ desire to engage with AVs – web-based studies

In the past four to five years, a large number of studies have used on-line surveys and focus groups
to understand public opinion and perception of AVs (J.D. Power, 2012; Casley et al. KPMG, 2013;
Vallet, 2014; Schoettle and Sivak, 2014; 2016, Underwood, 2014; AAA foundation, 2016; Bansall et al.,2016). However, the focus here has been on understanding how much users are willing to pay for suchtechnology, which in most cases is related to the users’ own vehicle, with little investigation of their
willingness to utilise public versions of such vehicles, in a ride-sharing setting. Overall, results illustrate
a relatively cautious attitude from respondents, with some concern about reduced safety, mixed responses
regarding willingness to pay for extra features, and questions about the legal/liability aspects of AVs.
The surveys also do not show a clear pattern when assessing age and gender differences. For instance,
Schoettle and Sivak (2015) report higher levels of willingness to pay for their older (>60 year- olds) andyounger (21-34 year-olds) participants, whereas a 2014 survey by Seapine Software (2014) found a large
proportion of these two age groups did not trust AVs (84% of 18–34 year-olds and 93% of 65 year olds).
In their most recent survey of individuals’ preferences for vehicle automation, Schoettle and Sivak(2016) report that acceptance of vehicle automation decreases as the level of automation increases.
Summarising the responses of 618 licenced US drivers, the authors found that only 15.5% favoured acompletely self-driving car, with 38.7% accepting a partially automated car and 45.8% preferring to rely
on manual driving. In addition, 94.5% of these drivers preferred to have access to a steering wheel orpedals, presumably allowing them to intervene in case of an emergency. The authors report this general
pattern of responses to be similar to those conducted on a larger group of participants from across the
US, UK and Australia, who took part in a similar survey in 2014. This similarity in results, obtained over
the two year time gap, is perhaps a little surprising, especially considering the higher level ofdevelopment/interest in (and coverage of) this type of vehicle between 2014 and 2016.
A similarly cautious response, regarding the contribution of AVs, is provided by results of a recent
AAA survey (2016), which found that 75% of Americans would be “afraid to allow an autonomousvehicle to drive itself with them in it”, with female respondents, in particular, expressing the mostconcern (81%). However, it is interesting to note that the respondents were generally more likely to trust
systems that have already been in operation for some time, such as Adaptive Cruise Control (47%) orlane departure warning and lane keep assist (52%). This hands-on experience with systems is therefore
an important factor in influencing results.
Unfortunately, however, a major shortcoming of most such large-scale surveys is that, due to thelack of wide-scale deployment and use of AVs by the general public, they cannot provide survey
respondents with an accurate overview of the capabilities and limitations of the new technologies inquestion. It is also difficult for the surveys to capture an accurate understanding of users’ actual use andimpression of these vehicles. Results are therefore likely to be influenced by the interviewer’s
descriptions, or rely on respondents’ impressions, which are, for example, shaped by promotional videos.
As outlined in the next section, user trust and acceptance of new technologies is greatly influenced bytheir actual interaction and hands on experience with such systems, which casts some doubts on the true
value of the results from the large-scale surveys outlined above.
Before summarising results from the small number of studies which have investigated consumers’
views of shared AVs in recently completed projects, the next section will provide a short overview of thefactors that are thought to influence users’ trust and acceptance of AVs, which should ultimately lead totheir use and uptake.

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Factors influencing trust and acceptance of AVs

Distinguishing between acceptance and acceptability
If AVs are to have a real impact on shaping future transport, user trust and acceptance of thesesystems must be high. The terms acceptance and acceptability are often used interchangeably, and there
is currently little consensus amongst researchers with regards to their definition and how the concepts
should be measured (Regan et al., 2006; Vlassenroot, 2006). However, it is recognised that these
constructs play an important role in understanding users’ behaviour and interaction with the system.
Schade and Schlag (2003) differentiate between the concepts of acceptance and acceptability basedon the dimension of time, suggesting that acceptability is a prospective judgement about a measure orsystem without the actual need to have had experience or exposure to it. Acceptability is therefore
synonymous with a user’s attitude towards the system. On the other hand, acceptance is a post- hocconcept and generally measured after exposure, whereby users can express their support, either via theirattitudes or by demonstrating a behavioural response. Separating the constructs in this way is useful, as itallows researchers to take a diagnostic approach – therefore not only evaluating what features could be
improved (via acceptability), but also establishing the real-time/post-hoc impact of a system in the field(acceptance). This distinction also recognises that acceptability may not lead to acceptance, or that
acceptance is not necessarily indicative of acceptability. Clearly, AV developers need to consider boththe acceptability of shared systems, in order to maximise stakeholder buy-in, prior to theirimplementation, along with considering user acceptance of the systems after implementation. Indeed,
Najm et al. (2006) suggest that “driver acceptance is the precondition that will permit new automotive
technologies to achieve their forecasted benefit levels”.

Use of social-psychological models to study user acceptance of AVs
To date, two social-psychological frameworks have been used to investigate user acceptance of
information technology – the Technology Acceptance Model (TAM; Davis, 1989) and the Unified
Theory of Acceptance and Use of Technology (UTAUT; Venkatesh et al., 2003). UTAUT is an
expanded version of the TAM and posits that the degree to which automated systems are accepted andultimately used will be impacted by seven main variables (Venkatesh, Thong, and Xu, 2012), see Table 1
for an example of how these were adapted for a recent study, investigating the value of the model forassessing the acceptance of AVs.

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Table 1. UTAUT Construct Definitions used by Madigan et al.
UTAUT Construct

Definition

Performance Expectancy

The degree to which using a system will provide benefits to
consumers in their travel activities

Effort Expectancy

The degree of ease associated with system use

Hedonic Motivation

The fun or pleasure derived from using the system

Facilitating Conditions

Consumers’ perceptions of the resources and support available to use
the system

Social Influence

The extent to which consumers perceive that important others (e.g.
family and friends) would use the system

Price

Value for Money

value

The extent to which an individual believes a behaviour to be
automatic

Habit
Source: Adapted from Vankatesh et al., (2012)

Numerous studies have investigated the impact of one or more of these variables on users’ acceptanceof vehicle automation (e.g. Park, Junghwan, Changi, and Seongcheol, 2013; Osswald Wurhöfer, Trosterer,
Beck and Tscheligi, 2012; Zmud, Sener and Wagner, 2016). However, to date, only one study has
investigated acceptance of shared AVs with people who have actually experienced these vehicles. Using aquestionnaire study, conducted as part of the CityMobil2 trials in three European cities (Lausanne Switzerland, La Rochelle-France, and Trikala Greece), Madigan et al. (2016, submitted) used an adapted
version of the UTAUT to investigate user acceptance of Automated Road Transport Systems (ARTS),
the term used for the AVs used in this project (substituting “the system” with ARTS, where appropriate see Figure 2).
These authors found that Hedonic Motivation or respondents’ enjoyment of AV use, had a large
impact on their desire to use these vehicles again. However, since 79% of survey respondents had used the
AV less than five times, this response is likely linked to the novelty value associated with the facility andan assessment of the longer term impression of the AV is clearly warranted. AV performance, the resources
provided to support their use, and the social norms surrounding the AVs also influenced users’ decisions
about whether or not they would use the AVs again in the future.

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Figure 2. The EasyMile (left) and ROBOSOFT (right) vehicles used in the CityMobil2 project

Source: La Rochelle photo courtesy of © Frédéric Le Lan, Communauté d’Agglomération de La Rochelle.

These results have a number of design implications for shared AVs, since it is likely that, as people
become more familiar with AVs, the excitement and enjoyment of using them may decrease. Therefore,
developers need to consider the use of different approach to maintain this enjoyment, such as ensuring
user comfort, or promoting the use of AVs for working or social networking activities (Nordhoff, Van
Arem, and Happee, 2016a). Designers of AVs also need to ensure that these systems perform to a highlevel and are reliable, along with optimising their connectivity with other transport services (Sessa et al.,2015). Finally, providing the correct infrastructure and increasing public engagement and awareness ofthe vehicle’s capabilities is also likely to increase the acceptance of these AVs.

From acceptance to trust
It is important to note that the UTAUT model considers users’ adoption decisions as a rational,
goal- directed behaviour that results from controlled and deliberate reasoning (Venkatesh et al., 2003).
However, what is currently lacking in this context is the effect of unconscious psychological processes
that are beyond the direct control of users. For example, one acceptance factor that meets these
conditions is user trust, which has a strong influence on the reliance, acceptance and adoption of
innovative technologies (Nordhoff, Kyriakidis, Van Arem, Ruhrort, Graff, and Happee, 2016c, see also
Choi and Ji, 2015). Trust in automation refers to the user’s belief in the reliability or ability of theautomated system (e.g. Parasuraman, Sheridan, and Wickens, 2008). In studies considering trust during
human robot interaction (HRI), a multitude of factors are considered to be responsible for establishing
the user’s trust in the robot’s capabilities, and some or all of these will ultimately ascertain whether or
not the robot/system is used and accepted. A number of models have been proposed in this context, andalthough some are beginning to be linked directly to AVs, many were originally developed for otherforms of automated systems and robots.
According to the Three Factor Descriptive Model of Human Robot Trust (see Hancock et al., 2011;
Schaefer et al., 2016), environmental, human and robot related factors all influence users’ trust in asystem, with the three elements also influencing each other, as shown in Figure 3.

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Figure 3. Venn diagram showing the interdependent nature of trust across the three factors oftrust

a The items listed as cognitive factors include additional antecedents of trust: ease of learning, priorexperiences, self-efficacy, workload, expertise, familiarity, and proximity. b The antecedents of trust with the teamcollaboration category include role interdependence, team composition, mental models, cultural and societal impact,
and in-group membership.
Source: Schaefer et al., 2016

Hoff and Bashir (2015), build on this model to provide a number of recommendations aboutmaximising appropriate trust in automated systems. They suggest that usability of a system will affecthow much it is trusted. Operators will generally trust an automated system if it behaves in the manner
they expect. However, if they experience unanticipated actions, there is a rapid drop in trust that often
leads to disuse (Schaefer et al., 2014). Therefore, designers of shared AVs need to consider the
expectations of potential users and ensure that their vehicles can meet these expectations.
Numerous studies have shown the influence of cultural, gender and age differences in shaping
consumers’ willingness to trust and use automated vehicles (Kyriakidis et al., 2015; Schaefer et al.,2014), although again, these are not necessarily based on hands-on experience with shared-riding AVs.
Nevertheless, in order to accommodate for diversity in cultures and demographics, designers of AVs
should consider implementing features that can be adapted to the needs of target user groups (Hoff andBashir, 2015). This point is also highlighted by Grush et al. (2016), who suggest that studies considering
the successful deployment of AVs should focus on understanding consumer preferences when designing
the correct programmes, or offering the appropriate subsidies, since a “one size fits all” approach to
public transport is regularly unsuccessful.
Appropriate trust in AVs is also likely to be facilitated by the provision of accurate, ongoing, andup- to-date feedback on both the reliability of automation, and any situational factors such as traffic

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conditions or weather, which might affect its performance (Seppelt and Lee, 2007; Hoff and Bashir,
2015). However, it is important to reach an appropriate balance between providing timely and helpful
updates, versus overloading users with too much information. For example, experimental research by
Lees and Lee (2007) found that while false alarms decreased trust in a vehicle collision warning system,
unnecessary alarms i.e. instances where the automated device indicated a hazardous event which thehuman driver did not deem to be hazardous, actually increased trust. Finally, a recent paper by Ekman etal. (2016) suggests that understanding that “trust formation is a dynamic process that starts long before auser’s first contact with the system, and continues long thereafter” and, of course, user interaction withthe system over time is also likely to affect this trust.
Appearance and communication style are also likely to have an impact on the development of user
trust. If an automated aid or machine is perceived to be aesthetically compatible with its function, it ismore likely to be used (Goetz et al., 2003). Research also suggests that the anthropomorphism, or humanlike characteristics, of an interface can be a significant variable (e.g. de Visser et al., 2012), and that trustin automated systems can be increased by making the system more human-like. However, there havebeen mixed findings regarding the effects of appearance on trust in automated vehicles. Research by
Weinstock, Oron-Gilad, and Parmet (2012) found that system aesthetics did not necessarily affect trust in
an imperfect in-vehicle navigation system, and that the performance of the system was a more importantvariable. A more recent study by Lee et al. (2015) compared participants’ evaluations of perceived
intelligence, perceived safety, and trust in an automated driving system (a remote-control car) based onwhether the vehicle appeared to be controlled by a humanoid robot or by an iPhone. They found that the
combination of a human-like appearance, with high autonomy i.e. the ability to make driving decisions,
led to higher ratings of perceived safety and trust. However, since there was no real risk associated withthe remote- control car, it is unclear whether similar results would emerge in interactions with real-world
vehicles. Once again, there has been little actual research into the effects of vehicle appearance on users’
trust or acceptance of shared AVs. However, a small focus group study, conducted as part of the
CityMobil2 trials in La Rochelle (see Dziennus et al, submitted), found that participants liked the highvisibility offered by the vehicle’s large windows, but were confused about its direction of travel, which
was not easy to comprehend, due to the absence of a driver and lack of any traditional vehicle controls.
Indeed, it is worth noting that many of the current ride-sharing versions of AVs do not include asteering wheel, or gas and brake pedals. This changes the role of the person on-board the vehicle from anactive driver to a passive passenger. For these vehicles, establishing user trust is crucial, to prevent theabuse or misuse of the AV (Schaefer and Straub, 2016). In a recent study examining the effect of drivercontrols on driver behavior and trust, Schaefer and Straub (2016) asked 18 drivers to interact with two
versions of a simulated driverless vehicle: one with and the other without traditional vehicle controls. A
red (disengage automation) and green (reengage automation) button were available for both set ups. The
authors found that drivers were less likely to press the red button when the vehicle lacked traditional
controllers, allowing the system to complete its task.
A number of questions remain here, such as whether this lack of intervention was due to the colourof the disengage button, which is universally associated with danger/emergency situations, and may havetherefore prevented user response. In addition, understanding how users of different age and ability
interact in such circumstances, and whether a need to intervene is influenced by the expected (versus
unexpected) behaviour of the vehicle, are some of the factors that require further investigation.
Nevertheless, results from this small-scale study suggests that drivers’ desire to intervene with theactions of a successfully operating automated system is reduced in such circumstances, which is usefulfor fully operational, faultless and capable systems, since it prevents unnecessary human intervention andreduces the likelihood of new and unforeseen errors.
As we progress towards the deployment of shared AVs, some form of communication andinteraction mechanism may be required to positively relate to their acceptance and use. These control

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mechanisms can take the form of a safety button inside the vehicle that can be pressed by passengers tostop it in case of an emergency or unforeseen circumstances. The connection of the vehicle to a hotlinethat provides (technical) information may also increase use and acceptance, in case of system breakdown(Nordhoff et al., 2016b).
Finally, in addition to improving the technical capabilities of AVs, the introduction of AVs should
be associated with the simultaneous management of individual expectations. This is important because
research has indicated that long-term acceptance may be seriously hampered if a priori capabilities are
based on unrealistic, idealised expectations, before individuals actually experience these vehicles(Beggiato and Krems, 2013). Therefore, we propose that the creation of more realistic expectations canbe achieved by exposing the public to the technology at a very early stage and in an iterative manner,using small scale demonstrations under limited conditions, allowing individuals to gradually gain
familiarity with these vehicles and learn about their technological capabilities and limitations (Seppelt
and Lee, 2007).

Real-world studies on user acceptance of ride-sharing AVs
Using an online survey, supplemented by a series of telephone interviews, Piao et al. (2016) reporton the views of 425 respondents living and working around the route of the CityMobil2 ARTS vehicles
in La Rochelle (with 87% of the sample reported to be aware of the vehicles’ presence in the city). The
sample of 18-65 year olds (53.6% female) was asked to compare automated buses, cars and taxis toconventional versions of such vehicles. Results showed that the majority of respondents were positive
about the automated vehicles, if they were offered at a lower price (likely to arise due to the absence of apaid driver), provided more space (due to the absence of vehicle controls) and were more readily
available upon request, when compared to conventional vehicles. However, safety and security werereported as a problem for automated vehicles, with night- time services being of particular concern.
The City Automated Transport System (CATS) project, which tested a publically shared driverlesselectric vehicle (Navya Shuttle) on the EPFL campus in Switzerland, provides a comprehensive summaryof a questionnaire completed by 181 users (66% male, 70% aged between 20 and 50 years). The study
suggest that respondents were willing to pay about the same price for this service as a conventional
vehicle, and thought the vehicle was user-friendly (92%) aesthetic (81%) and functional (80%).
However, due to gradual damage to the road infrastructure, caused by the weather and also inappropriate
speed management on slopes, only 14% of participants found the quality of the ride to be “excellent”.
Respondents also found the vehicle to be easy or very easy to use (92%) safe or very safe (91%) with
82% reporting the size and speed of the vehicle to be suitable for the EPFL campus (12 km/h), althoughopinions were divided on speed, which is slightly faster than walking pace. Finally, lack of seating andwindow panes were considered a weakness of the vehicles.

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User willingness to share a small unattended vehicle with strangers
The acceptance and uptake of AVs is likely to be influenced by the extent to which users like toshare space with other people in a public transport setting. This may also be further affected by thepresence (or not) of a vehicle operator, with users likely to be more apprehensive of sharing the vehicle
with others, in the absence of an operator, although remote observations/CCTV will likely help to (atleast partly) alleviate this sense of unease. According to Hall’s proxemics theory (1966), strangers areforced into an intimate social distance in a public transport situation, which often causes psychological orsocial discomfort. Clearly, the level of perceived discomfort will increase with passenger density,
because it is likely to create closer interpersonal space between passengers. Lack of familiarity withothers in the vehicle is also likely to influence this level of discomfort, as corroborated by a recent survey
by Bansal et al. (2016), who sought the views of 347 Austinites, on factors which influenced their carand ride-sharing decisions. The authors report that for rides taking place during day time hours and forshort durations, 51% of respondents stated that they were comfortable in sharing a ride with a stranger,
with 53% reporting that they would share with a friend of one of their Facebook friends (including ones
they had never met). However, the highest figure of 90.8% was reported for sharing of rides with regularfriends and family members. This survey also reports that full-time male workers living in urban areas
were the most likely to use shared automated vehicles, provided costs were in line with current pricing
schemes for shared vehicles. This finding is in contrast to the research of Delhomme and Gheorghiu(2016) in France, which found a higher likelihood of females with children using conventional forms ofcar-sharing, showing that the lack of a human operator may lead to differences in who chooses to use
these systems.
When investigating the views and well-being of seated passengers in a public transport setting,regarding a train journey between New Jersey and Manhattan, Evans and Wener (2007) report that stressin a crowded carriage is based on the violation of personal space or perceived privacy, rather than theoverall carriage density. This explains why the central seat of a three-seat row, for example in busses andtrain carriages, is the last to be occupied, leading the authors to recommend the inclusion of ‘territorial
props’ such as armrests and tables to enhance enjoyment of the journey. Hall (1966) also highlights anumber of strategies adopted by passengers in a public transport setting when their personal space isinvaded by strangers. These include avoiding eye contact and touch, as well as tensing muscles to remain
immobile and avoid intimacy in a socially awkward situation. This basic psychological need for personal
space may also explain why the privately owned car is still the most common mode of transport in many
places in the world, even in less car-dependent places which have more sustainable and environmentallyfriendly travel (Olafsson, Nielsen, and Carstensen, 2016). Therefore, since many of the currently
developed initial prototypes of shared AVs in a public transport setting provide seating capacities foraround 8 -10 passengers, with little consideration of enhancing such personal space, they are likely to
exacerbate feelings of tension and stress for users, especially during longer journeys. Research will
therefore benefit from taking the design requirements of AVs into account, to ensure large-scale
acceptance and uptake of these vehicles. In addition, as highlighted in a recent panel discussion at theInternational Research Council on the Biomechanics of Injury (2016), currently deployed examples ofshared AVs do not include any of the standard passive safety features incorporated in conventional
vehicles. This absence of airbags, seatbelts and strong body structures in AVs, is likely to reduce theirsafety in case of a collision and will likely reduce costumer acceptance, unless addressed in future
models.

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Realistic opportunities to serve mobility-impaired users

One group who are consistently identified as benefiting from the introduction of automated vehicles
are mobility-impaired groups such as the elderly or those with disabilities. Investigating the opportunities
of AVs to serve mobility-impaired users involves the differentiation between SAE level 4 and SAE level
5 automation, with each having different effects on the extent to which individuals find AVs acceptableand useful, and the democratisation of mobility in general. The potentials of AVs to provide door-to-doortransport everywhere and anytime can only be fully unleashed with SAE level 5 automation, whilst themobility options at SAE level 4 automation remain limited as impaired individuals need to bridge the socalled first or last mile themselves (Nordhoff, Kyriakidis, Van Arem, Ruhrort, Graff, and Happee,
2016b).
Due to the removal of steering wheels and pedals in AVs, they have generally been considered as an
attractive mobility option for mobility-impaired users, such as children, seniors or people withdisabilities. License holding, and the availability of a car drop significantly at the age of 75, mainly due
to health effects, which increases the need of such users to use driverless vehicles as they constitute a
low-cost, convenient and flexible age-appropriate mobility solution (Krueger et al., 2016). In addition,individuals who have been previously excluded from riding a car or taking public transport may be those
who suffer more from age- related disabilities, such as visual, physical or cognitive disabilities(Anderson, Kalra, Stanley, Sorensen, Samaras, and Oluwota, 2014). Pristavec (2016) shows that thefacilitation of ride-giving, and developing flexible transportation options, may enhance social
participation among older adults who cease, or begin ceasing, to drive. However, the regain in mobility
and social participation will only be achieved if AVs are accepted by these groups, and current researchis still ambiguous on the commonly hypothesized correlations between age or impairment and thewillingness to accept and use AVs. For example, Krueger et al. (2016) found that respondents aged
between 65 and 84 are not more likely than respondents between 24 and 29 years to use sharedautomated vehicles.
When considering privately owned use of AVs, Payre et al. (2014) found that as age increases, theintention to use automated cars decreases. The same difference was observed by Schoettle and Sivak(2014) who found that younger people were more interested in using self-driving technology than older
people. Further support for age differences in attitudes towards automated cars were found by Kyriakidis
et al. (2015) and Bansal et al. (2016), who showed that, compared to older people, younger people werewilling to pay more for these vehicles (Hohenberger et al., 2016). One possible explanation may be thelower level of trust in self-driving vehicle technology among older cohorts, which drops with age (see:
56% of Gen Y (1977-1994) and 55% of Gen Z (1995-2000) stated that they trusted self-driving
technology, compared to 41% of Gen X (1965-1976), 23% of Baby Boomers (1946-1964) and 18% of
Pre-Boomers (born before 1946). Further, the proportion of respondents indicating that they would nottrust self-driving vehicle technology was 18%, 11%, 27%, 39% and 40% for the generational cohorts
respectively (J.D. Power, 2016).
However, a meta-analysis conducted by Schaefer et al. (2014) contradicts these studies, finding thatolder adults showed a higher level of trust in automation than younger adults. Similarly, an online survey
investigation of a group of older Floridian adults aged over 55, found that attitudes towards, andacceptance of, AVs was higher for those reporting ease with higher levels of technology (Souders and
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Charness, 2016). This group also reported higher interest in different models of AV ownership, and weremore likely to report that the introduction of autonomous vehicles would be beneficial in terms of safetyenhancements, less traffic congestion or enhanced mobility for those unable to drive (Souders andCharness, 2016). As highlighted earlier, one reason for this apparent anomaly in findings may well be thelack of data from real-world studies in this context.
The UTAUT model, discussed earlier, suggests that individuals’ usage of a system is determined byfacilitating conditions, indicating that an advanced technical infrastructure could support the acceptanceand use of AVs among older users. AVs should be easy to use, non-intrusive and easy to access bypeople with disabilities, even for those who do not own a personal electronic device. Access to anorganisational and technical infrastructure may include, but is not restricted to, a smartphone and thecorresponding smartphone app, providing information on the real-time position of the AV, its near-future
trajectory and enabling booking and payment for AV use. The provision of this technical infrastructure tofacilitate the acceptance and use of AVs should form part of a systemic and holistic approach toencourage the use of AVs among age- or physically/visually impaired people. This systemic approach ofenabling acceptance and use of AVs among people who have been previously excluded from driving a
car or using public transport must take into account both the features inside and outside of the AV. For
instance, a wheelchair ramp may provide physical access to the vehicle, while a technical hotline ensuresthe digital connectivity between vehicle and control units outside the vehicle that provide assistance inthe case of (technical) questions. This can be particularly beneficial for those users without personal
mobile devices. Other elements outside the vehicle which may increase connectivity may include sign
posts, bulletin boards, and benches that are connected to the digital information space, facilitating
implicit or explicit interaction in public spaces (Kötteritzsch et al., 2016). One example would be
responsive street furniture that enables passengers to submit their inquiries and receive information
regarding reservation and cost options for the next available shared AV.

Interactions of AVs with pedestrians and bicyclists

The challenges in the development of AVs do not stop inside the vehicle. There is also a need toconsider the interaction of automated vehicles with other road users, particularly in situations such as
those explored by the CityMobil2 (www.citymobil2.eu) and the Lutz Pathfinder (seehttps://en.wikipedia.org/wiki/LUTZ_Pathfinder) projects, where it is anticipated that AVs will operate ina shared space with vulnerable road users such as pedestrians and cyclists.
Currently, pedestrians and cyclists use various implicit and explicit cues to understand a vehicles’
intended behaviours on the road. Some of these cues involve explicit two-way communication such aseye- contact, physical gestures and the use of flashing lights (Sucha, 2014). Other vehicle-centric cues
such as velocity, distance, trajectory, and speed limit are also used to aid pedestrians in their decisionmaking when it comes to crossing the road (Sucha, 2014; Schneemann and Gohl, 2016). Research has
shown that vehicle speed, in particular, can have an impact on how pedestrians make their decisions. In
faster speed zones, pedestrians tend to use the behaviour of an oncoming vehicle to judge whether it issafe to cross the road, whereas in slower speed zones they tend to rely more on eye-contact with thedriver (Schneemann and Gohl, 2016).
Although, we have some understanding of how pedestrians interpret the actions of vehicles with18

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drivers, one of the huge challenges for the roll-out of AVs is how to replicate these implicit and explicit
communication strategies in the absence of person-to-person communication. Vehicle manufacturers
have only recently turned their attention to this issue. In 2015, Google secured a patent for technology tocommunicate with pedestrians which proposes the use of electronically-based mechanisms and messages
to mimic human behaviour, including the use of robotic hands and eyes, along with written messages ormoving graphics designed to let pedestrians know the car’s intentions (Urmson et al., 2015). Mercedes
have also developed a concept car which includes a laser to project a crosswalk in front of the vehicle
when it stops, in order to tell a pedestrian it sees them and is expecting them to cross (Kerferbock andRiener, 2015). Similar efforts are in place by other vehicle manufacturers and tier 1 suppliers. However,despite these novel suggestions, to date, there have been very few experimental studies examining user
acceptance and understanding of these communication techniques.
A number of projects at Chalmers University have made use of the Wizard of Oz (WOZ) techniqueto examine pedestrians’ interactions with a “fake” driver. The WOZ technique is based on the idea ofusing a human operator to simulate a fully working technical system, and users are generally told thatthey are interacting with an automated system. Habibovic and colleagues (Habibovic et al., 2016,
Langström and Lundgren, 2015) set up a WOZ experiment by placing a dummy steering wheel on thepassenger side of a vehicle, and hiding the real steering wheel from sight. A “fake” driver in thepassenger seat seemingly drove the vehicle or engaged in distracting activities such as looking at theirphone or a newspaper. Experimental and interview results showed that pedestrians were less willing tocross in front of the vehicle when the driver appeared distracted, and they expressed discomfort when
there was nobody sitting in the driver seat. They did not suspect that the vehicle was automated or driven
by WOZ, suggesting that until people have time to get used to automated vehicles, they should beperhaps be designed to look different from regular cars.
Langström and Lundgren (2015) and Clamann, Aubert, and Cummings (2017) have explored anumber of potential communication techniques between automated vehicles and pedestrians, with mixed
results emerging. Langström and Lundgren (2015) found that after training, pedestrians were able tounderstand the signals conveyed by an LED strip located at the top of a vehicle windscreen, and used
these to illustrate appropriate crossing decisions. However, it is unlikely that the pattern of LED lights
presented in this study will provide an intuitive message to an untrained/unfamiliar pedestrian. Therefore,
some effort is clearly required in this area, to understand the communication needs between pedestrians
and AVs, and as manufacturers begin to propose new communication methods, the introduction of new,globally acceptable, standards will need to be considered.
In a study assessing pedestrians’ interactions with three vehicle-to-pedestrian messages (seeFigure 4), Clamann, et al. (2017) investigated crossing behaviour at different types of road crossing.
Each message was placed on an approaching ‘driverless’ van, and pedestrians were asked to indicate
when it was safe to cross. Results showed that there were no significant effects on crossing time, basedon the type of display. The authors use this data to suggest that pedestrians are more likely to base theircrossing decisions on existing strategies, such as gap acceptance and vehicle trajectory, indicating that
display types may be less important than assumed.
Figure 4. Vehicle intention displays used in Clamann et al. (2017)

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In addition, this study found age and gender related differences, with younger participants
illustrating riskier crossing decisions, and males showing quicker decisions than females. This study,
therefore, suggests that different strategies may be required for communicating with different
demographic groups.
The CityMobil2 trials, outlined above, also sought to evaluate how pedestrians and other road users
might interact with low speed shared automated vehicles. A questionnaire study, by Merat et al.
(submitted), showed that pedestrians assumed they had priority of movement over the automated vehicles
when operating in a shared space, but that the AV was assumed to have priority, if moving through aclearly demarcated lane. Pedestrians also reported higher ratings of perceived safety when road markings
were present. In terms of how AVs’ behaviour should be communicated, results showed that participants
were particularly keen to know they had been detected and wished to receive information about theturning, starting and stopping of the ARTS, but that external information regarding the speed of the
ARTS was not deemed important. This supports the finding of Clamann et al. (2017) that pedestrians areable to make use of vehicle-centric cues to appreciate vehicle speed. On the whole, participants from thisstudy preferred the use of lights to provide information about whether the vehicle was turning or
stopping, and sounds to indicate whether it was going to start moving and whether they had been
detected. However, it should be noted that there were differences in preference across the three locations
examined (Lausanne in Switzerland, La Rochelle in France, and Trikala in Greece) suggesting there may
be some cultural differences in how the communication tools used by these vehicles are perceived.
Results from this study suggest that other road users would like to receive some form of informationfrom the AVs when the space is shared between these actors, and that knowing they have been detectedby the AV is the most important message to pedestrians and cyclists.

Summary and conclusions

As the implementation of more advanced, sensors, radars and localization/navigation technologies
in vehicles increases, there is potential for the mass-deployment of a new form of publicly available,
electrically operated, ‘driverless’ vehicle for the urban environment, which can be adopted for sole orshared use. Such SAE Level 4 vehicles (striving towards Level 5) have the potential to provide firstmile/last mile transport, and, if designed with the needs of the user in mind, can greatly enhance traffic
flow and management in cities, whilst reducing transport-related emissions. By denying access to
privately owned vehicles in busy city centres, yet allowing easily accessible transport within the city,which complements walking and cycling facilities, these vehicles also have the potential to remove theneed for parking spaces and are considered a solution for increasing green spaces in congested urban
environments. Their successful deployment is also likely to contribute to improved health and social
well-being of citizens and enhance enjoyment of the local environment. This type of AV may also
provide on- demand access to more remote areas, not easily served by traditional public transport, andcan potentially be beneficial to mobility-impaired individuals or those who have ceased (or have neverlearned) to drive. It can be argued that, until recently, much of the effort dedicated to the implementationof such vehicles has focussed on improving their operational and technical aspects. However, it is now
time to consider the policy and behavioural factors that will allow successful deployment anduser/societal uptake.
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This paper provides an overview of the social-psychological factors that are likely to influence thetrust and acceptance of AVs, starting with a short summary of the factors that have influenced users’
engagement in ride-sharing for conventional vehicles. It is argued that that, since there is currently asevere lack of data on (different) users’ actual hands-on and day to day experience with fully functioning,
trustworthy and infallible AVs, any recommendations must rely on results from a limited number ofstudies which have conducted small-scale demonstrations of these vehicles (mostly in Europe) . In
addition, many of our assumptions in this context must still be based on what we know about users’interactions with automation in other domains, together with results from large-scale on-line surveys,
which seek user opinion on these systems, which have not actually been fully developed, tested or
implemented. Nevertheless, based on this short overview, it can be assumed that for such low speed
shared urban AVs to successfully replace the developed world’s dependence on (and love of) the
privately owned motor vehicle, they must overcome a number of fundamental barriers, and perhaps forthe first time, should be designed for, and by, the user.
The question at the moment is whether individuals will prefer to continue investing resources intheir privately owned motor vehicle, or be willing to take advantage of the services offered by automatedride- sharing services. As outlined above, the time and cost associated with each service play animportant role in this decision-making process, and the influence of user demographics and city
infrastructure cannot be ignored.
In the coming years, the consumer is faced with two potential (possibly complimentary) options:investing in a (i) privately owned, semi-automated (level 2/3) vehicle which strives to provide level 4capability, and/or investing in/paying for the use of a (ii) highly-automated level 4 vehicle, which may
also be shared with others. We argue that apart from reducing energy use and emissions, the availabilityof options for the former type of vehicle are currently superior to shared AVs. For the latter to be
accepted and used, it will need to be:reliable and safe;

available at any time;able go anywhere and everywhere, and;

capable of operating in all weather conditions. The shared AV must also provide:a clean, comfortable and safe interior environment;enhanced privacy features;allow easy access for dependents (children, impaired and elderly) and theirequipment (luggage, push chairs, wheelchairs).

Therefore, as outlined in Figure 5, the private semi-automated vehicle available today and in thenear future, provides a level of convenience and comfort which is perhaps superior to the shared AV.
Therefore, for shared AVs to be considered a serious alternative to privately owned vehicles, city
authorities will need to work with manufacturers and suppliers to enable the development of some, if notall, of the features offered by privately owned vehicles.

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Figure 5. Overview of factors that influence consumer choice for private semi-automated andshared fully automated vehicles

The pace of technology improvement in this area, and the dedicated interest and investment by
manufacturers and policy makers alike provides hope that such developments for shared AVs are
possible. As social-networking and crowd-sourcing opportunities increase, travellers are likely to have astrong influence on shaping their own means of mobility, which can in turn be influenced by anindividual’s subjective norm. With increasing congestion in cities, rising fuel costs, expensive andlimited car parking facilities, citizens of the developed world may well appreciate the benefit of access tovehicles providing high levels of automation. For a time-poor and connected society, these vehicles arelikely to allow engagement in other activities, increasing comfort, productivity and enjoyment. However,the ultimate success of ride-sharing AVs will only be achieved following an effective collaboration
between manufacturers, local and central government, to provide citizens with the most suitable options
for each specific environment.

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Bibliography

AAA (2016). Three-Quarters of Americans “Afraid” to Ride in a Self-Driving Vehicle. Retrieved
from
http://newsroom.aaa.com/2016/03/three-quarters-of-americans-afraid-to-ride-in-a-self-drivingvehicle/ in December 2016.
Agatz, N. A., Erera, A. L., Savelsbergh, M. W., Wang, X., (2011), “Dynamic ride-sharing: A
simulation study in Metro Atlanta.” Transportation Research Part B 45 (9), 1450–1464
Ahmadpour N., Robert, J.M., Lindgaard, G. (2014), “Impact of the seat on aircraft passenger
comfort experience in the cabin interior.” In: Rebello F, Soares M, editors. Advances in Ergonomics
in Design, Usability & Special Populations (Part II). AHFE International; 2014, p. 603.
Ahmadpour, N., Kühne, M., Robert, J.-M., and Vink, P. (2016). Attitudes towards personal andshared space during the flight. Work, 54,981-987.
Anable, J. (2005). “'Complacent car addicts’ or' aspiring environmentalists’? Identifying travel
behaviour segments using attitude theory”, Transport Policy, 12, pp. 65–78.
Anderson, J.M., Kalra, N., Stanley, K.D., Sorensen, P., Samaras, C., Oluwatola, O.A. (2014).
“Autonomous vehicle technology a guide for policymakers”, RAND Corporation (2014).
Ballet, J.-C., Clavel, R., (2007), Le covoiturage en France – État des lieux et perspectives[Carpooling in France – Situation and perspectives]. CERTU, Lyon, pp. 86.
Bansal, P., Kockelman, K. M., & Singh, A. (2016). “Assessing public opinions of and interest in
new vehicle technologies: An Austin perspective”, Transportation Research Part C, 67, 1-14.
Beggiato M., Krems J. F. (2013). “The evolution of mental model, trust and acceptance of adaptive
cruise control in relation to initial information.” Transportation Research Part F: Traffic Psychology and
Behaviour, 18, 47–57. doi:10.1016/j.trf.2012.12.006
Beirao, G., Sarsfield-Cabral, J.A. (2007). “ Understanding attitudes towards public transportand private car: a qualitative study”, Transport Policy, 14, 478–489.
Casley, S. V., Jardim, A. S., & Quartulli, A. M. (2013). “A study of public acceptance ofautonomous cars” (Bachelor of Science), Worcester Polytechnic Institute, Worcester, MA, USA.
<http://www.wpi.edu/Pubs/E-project/Available/E-project-043013155601/unrestricted/A_Study_of_Public_Acceptance_of_Autonomous_Cars.pdf>.
Chowdhury, S., & Ceder, A. (2016). “Users’ willingness to ride an integrated public-transport
service: A literature review.” Transport Policy 48 (2016) 183–195.
Choi, J.K., and Ji, Y.G. (2015). “Investigating the Importance of Trust on Adopting an
Autonomous Vehicle.” International Journal of Human–Computer Interaction, 31 (10), 692-702.
Clamann, M., Aubert, M., & Cummings, M.L. (submitted). “ Evaluation of vehicle-topedestrian communication displays for autonomous vehicles.” Traffic Research Board.
Davis, F. D. (1989). “Perceived usefulness, perceived ease of use, and user acceptance of
information technology.” MIS Quarterly, 13(3), 319-340.
ITF Discussion Paper 2017-10 — © OECD/ITF 201723

Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

de Visser, E., Krueger, F., McKnight, P., Scheid, S., Smith, M., Chalk, S., & Parasuraman, R.
(2012). “The world is not enough: Trust in cognitive agents.” In Proceedings of the Human Factors and
Ergonomics Society, 56, 263–267.
Delhomme, P. and Gheorghiu, A. (2016), “Comparing French Carpoolers and Noncarpoolers:
Which Factors Contribute the most to Carpooling?” Transportation Research Part D: Transport andEnvironment, 42, pp. 1-15.
DfT (2015). “Automated vehicle technologies testing: code of practice.” UK Department forTransport, Accessed at: https://www.gov.uk/government/publications/automated-vehicle-technologiestesting-code- of-practice 10/11/2016.
Dueker, K. J., Bair, B. O., & Levin, I. P. (1977). “Ride-sharing: psychological factors.”
Transportation engineering journal of the American Society of Civil Engineers, 103(6), 685-692.
Dziennus, M. Schieben, A., Kelsch, J., Madigan, R., Louw, T., & Merat, N. (submitted). “ Users’
views of Automated Road Transport Systems (ARTS): A focus group study in La Rochelle.”
Ekman, F., Johansson, M., & Sochor, J. L. (2016). “ Creating Appropriate Trust forAutonomous Vehicle Systems: A Framework for HMI Design”. Proceedings of the 95th Annual
Meeting of the Transportation Research Board.
Evans, G.W. & Wener, R.E. (2007). “ Crowding and personal space invasion on the train: Please
don’t make me sit in the middle.” Journal of Environmental Psychology, 27, 90-94.
Freier,A.
(2015).
“Uber
usage
statistics
andrevenue.”
Accessed
http://www.businessofapps.com/uber-usage-statistics-and-revenue/ on 21st November 2016.

at:Gardner, B., and C. Abraham. 2007. “What Drives Car Use? A Grounded Theory Analysis ofCommuters’ Reasons for Driving.” Transportation Research Part F: Traffic Psychology and
Behaviour, 10, 187–200.
Goetz, J., Kiesler, S., Powers, A. (2003). “ Matching robot appearance and behaviour to tasks
to improve human-robot cooperation.” Proceedings of the 12th IEEE International Workshop on Robotand Human Interactive Communication, Milbrae, CA, 2003, pp 55-60.
Grush, B., Niles, J. & Baum, E. (2016). “Ontario Must Prepare for Vehicle Automation
Automated vehicles can influence urban form, congestion and infrastructure delivery.” Retrieved from
RCCAO.COM
Habibovic, A., Andersson, J., Nilsson, M., Lundgren, V.M., & Nilsson, J. (2016). “Evaluatinginteractions with non-existing automated vehicles: Three Wizard of Oz approaches.” 2016 IEEE
Intelligent Vehicles Symposium (IV), Gothenburg, Sweden, 19-22 June.
Hall, E. (1966). The Hidden Dimension. New York: Doubleday.
Hancock, P. A.; Billings, D. R.; Schaefer, K. E.; Chen, J. Y. C.; de Visser, E.; Parasuraman, R. A
(2011) “Meta-Analysis of Factors Affecting Trust in Human-Robot Interaction.” Human Factors, 53 (5),.
Hoff, K.A., & Bashir, M. (2015). “Trust in automation: Integrating empirical evidence on factorsthat influence trust”. Human Factors, 27(3), 407-434.
Hohenberger, C., Sporrle, M., Welpe, I.M. (2016). “How and why do men and women differ intheir willingness to use automated cars? The influence of emotions across different age groups.”
Transportation Research Part A, 94, 374-385.
International Energy Agency (2005). Saving Oil in a Hurry: Measures for Rapid Demand Restraint
in Transport. Paris International Energy Agency and Organization for Economic Co-operation andDevelopment.
24

ITF Discussion Paper 2017-10 — © OECD/ITF 2017

Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

J.D Power (2016) Young Consumers Are Much More Trusting in Automotive Technology,Paving
the Way for Automated Vehicles. http://www.jdpower.com/press-releases/2016-us-tech-choice-study.
Accessed November 20, 2016.
Kawgan-Kagan, I. (2015). Early adopters of carsharing with and without BEVs with respect to genderpreferences. European Transport Research Reviews, 7, 33, 1-11.
Keferböck, F., & Riener, a. (2015). Strategies for negotiation between autonomous vehicles andpedestrians. In A. Weisbecker, M. Burmester, & A. Schmidt. Mensch und Computer 2015 Workshopband,
Stuttgart: Oldenbourg Wissenschaftsverlag, 2015, pp. 525-532.
Kötteritzsch, A., Wallrafen, S., and Koch, M. (2016). Expand your Comfort Zone! Smart Urban
ObjectstoPromoteSafetyin
PublicSpacesfor
Older
Adults.
file://innozdc01/FolderRedirection/snordhoff/Desktop/p1399-kotteritzsch.pdf
Kuhnimhof, T., Chlond, B., von der Ruhren, S. (2006). Users of transport modes and multimodal
travel behavior, Transportation Research Records. (2006), pp. 40–48
Kyriakidis, M., Happee, R., & De Winter, J. C. F. (2015). Public opinion on automated driving:
Results of an international questionnaire among 5,000 respondents. Transportation Research Part F:
Traffic Psychology and Behaviour, 32, 127-140. doi:10.1016/j.trf.2015.04.014
Krueger, R., Rashidi, T.H. and Rose, J.M. (2016) Adoption of Shared Autonomous Vehicles – A
Hybrid Choice Modeling Approach based on a Stated Choice Survey, Proceedings of the 95th Annual
Meeting of the Transportation Research Board, Washington D.C., United States, 10th-14th January.
Langström, T., & Lundgren, V.M. (2015). AVIP – Autonomous Vehicles Interaction with
Pedestrians: An Investigation of Pedestrian-Driver Communication and Development of a Vehicle
External Interface. Masters Thesis, Chalmers University of Technology, Gothenborg, Sweden.
Lee, J.D., & See, K.A. (2004). Trust in automation: designing for appropriate reliance. Human
Factors: The Journal of Human Factors & Ergonomics Society, 46, 50-80
Lee, J.G., Kim, K.J., Lee, S., & Shin, D.H. (2015). Can autonomous vehicles be safe and trustworthy?
Effects of appearance and autonomy of unmanned driving systems. International Journal of HumanComputer Interaction, 31, 682-691.
Lees, M.N., & Lee, J.D. (2007). The influence of distraction and driving context on driver response
to imperfect collision warning systems. Ergonomics, 50(8), 1264-1286
Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M. & Merat, N.
(2016). Acceptance of Automated Road Transport Systems (ARTS): an adaptation of the UTAUT model,
Transportation Research Procedia, 14:2217-2226 · December 2016
Madigan, R., Louw, T., Dziennus, M., Scheiben, A. & Merat, N. (under review). What influences
the decision to use automated public transport? Using UTAUT to understand public acceptance ofAutomated Road Transport Systems, Transportation Research Part F
MAIF, 2009. Utilisation and attitudes of covoiturage.fr users [Usages et attitudes des utilisateurs dusite Internet Covoiturage.fr].
Malodia, S. & Singla, H. (2016) A study of carpooling behaviour using a stated preference web survey
in selected cities of India, Transportation Planning and Technology, 39:5, 538-550, DOI:
10.1080/03081060.2016.1174368
Minett, P.; Pearce, J. Estimating the Energy Consumption Impact of Casual Carpooling. Energies
2011, 4, 126-139.

ITF Discussion Paper 2017-10 — © OECD/ITF 201725

Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Najm, W. G., Stearns, M. D., Howarth, H., Koopmann, J., & Hitz, J. (2006). “Evaluation of an
automotive rear-end collision avoidance system” (chapter 5). U.S. Department of transportation,
National highway traffic safety administration, DOT HS 810 569, Research and Innovative
Technology Administration, Volpe National Transportation Systems Centre, Cambridge,
USA.
Nordhoff, S., Van Arem, B., and Happee, R. (2016a). A Conceptual Model to Explain, Predict, andImprove User Acceptance of Driverless 4P Vehicles. http://docs.trb.org/prp/16-5526.pdf. Accessed April
21, 2016. To appear in the Journal of the Transportation Research Records.
Nordhoff, S., Kyriakidis, M., Van Arem, B., Ruhrort, L., Graff, A., & Happee, R. (2016b). Modeling
Acceptance of Driverless Vehicles in Public Transport: Results of an International Survey with 10.001
Respondents Worldwide. (Not yet submitted)
Nordhoff, S., Kyriakidis, M., Van Arem, B., Ruhrort, L., Graff, A., & Happee, R. (2016c). Beyondsocio-demographics and mobility characteristics: why we need psychological factors in explaining
acceptance of automated vehicles. (Not yet submitted).
Nichols, J. (2016). Survey: New Yorkers and Californians Ready for Autonomous Cars: Texas andPennsylvania Residents Skeptical. https://www.media.volvocars.com/us/enus/media/pressreleases/193745/survey-new-yorkers-and-californians-ready-for-autonomous-cars-texasand-pennsylvania-residents-skept. Accessed September 11, 2016.
Nordhoff, S., Kyriakidis, M., Van Arem, B., Ruhrort, L., Graff, A., & Happee, R. (2016). Modelling
Acceptance of Driverless Vehicles in Public Transport: Results of an International Survey with 10.001
Respondents Worldwide. (Not yet submitted)
Olafsson, A. S., Nielsen, T. S., & Carstensen, T. A. (2016). Cycling in multimodal transport
behaviours: Exploring modality styles in the Danish population. Journal of Transport Geography, 52,
123-130. DOI: 10.1016/j.jtrangeo.2016.03.010.
Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2008). Situation awareness, mental workload,
and trust in automation: Viable, empirically supported cognitive engineering constructs. Journal of
Cognitive Engineering and Decision Making, 2, 140–160
Payre, W., Cestac, J., Delhomme, P. (2014). ‘Intention to use a fully automated car: Attitudes and apriori acceptability’, In Transportation Research Part F, 27 (2014), pp. 252–263.
Piao, J., McDonald, M. Hounsell, N. Graindorge, M. Graindorge, T., Malhene, N. (2016). Public
Views towards Implementation of Automated Vehicles in Urban Areas, Transportation Research Procedia,
14, 2168–2177.
Pristavec, T. (2016). Social participation in later years: the role of driving mobility. The Journals ofGerontology Series B: Psychological Sciences and Social Sciences, gbw057.
Regan, M. A., Triggs, T. J., Young, K. L., Tomasevic, N., Mitsopoulos, E., Stephan, K., et al.
(2006). On-Road Evaluation of Intelligent Speed Adaptation, Following Distance Warning and Seatbelt
Reminder Systems: Final Results of the TAC SafeCar Project Victoria: Monash University Accident
Research Centre
Roy, S. (2016). The Impacts of gender, personality, and previous use on attitude towards the sharing
economy and future use of the services. Master of Business Administration Thesis. Accessed athttps://repository.library.fresnostate.edu/bitstream/handle/10211.3/179585/ROY_Sandip.pdf?sequence=1
26/11/16.
SAE (2016). Taxonomy and Definitions for Terms Related to Driving Automation Systems for OnRoad Motor Vehicles, Accessed at: http://standards.sae.org/j3016_201609/

26

ITF Discussion Paper 2017-10 — © OECD/ITF 2017

Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Schade, J. & Schlag, B. (2003). Acceptability of Transport Pricing Strategies: An Introduction.
Acceptability of Transport Pricing Strategies. 2003, 1-9.
Schaefer, K.E., and Straub, E.R. (2016). Will Passengers Trust Driverless Vehicles? Removing thesteering wheel and pedals. 2016 IEEE International Multidisciplinary Conference on Cognitive Methods
in Situation Awareness and Decision Support (CogSIMA). DOI: 10.1109/COGSIMA.
Schaefer, K.E., Billings, D.R., Szalma, J.L., Adams, J.K., Sanders, T.L., Chen, J.Y.C., & Hancock,
P.A. (2014). “A meta-analysis of factors influencing the development of trust in automation:
implications for human-robot interaction”. Army Research Laboratory ARL-TR-6984.
Schneemann, F., & Gohl, I. (2016). Analyzing driver-pedestrian interaction at crosswalks: A
contribution to autonomous driving in urban environments. IEEE Intelligent Vehicles Symposium,
Gothenburg, Sweden, 19th-22nd June 2016.
Schoettle, B., and Sivak, M. (2015). Motorists’ Preferences for Different Levels of Vehicle
Automation.
http://deepblue.lib.umich.edu/bitstream/handle/2027.42/114386/103217.pdf?sequence=1&isAllowed=y.
Accessed July 22, 2015.
Schoettle, B., and Sivak, M. (2016). Motorists’ Preferences for Different Levels of Vehicle
Automation: 2016. http://www.umich.edu/~umtriswt/PDF/SWT-2016-8.pdf. Accessed July 22, 2015.
Seppelt, B. D., and Lee, J.D. (2007). “Making adaptive cruise control (ACC) limits visible.”International Journal of Human Computer Studies, 65 (3), 192–205.
Seppelt, B.D., & Lee, J.D. (2007). Making adaptive cruise control (ACC) limits visible. International
Journal of Human-Computer Studies, 65(3), 192-205.
Sessa et al., 2015. How can automated transport systems contribute to our future mobility?:
http://www.citymobil2.eu/en/upload/Final_conference/3%20%20Contribution%20of%20ARTS%20to%20the%20future%20mobility%20-%20Carlo%20Sessa.pdf
Souders, D.J. and Charness, N. (2016). “Challenges of Older Drivers’ Adoption of Advanced
Driver Assistance Systems and Autonomous Vehicles”, in: Human Aspects of IT for the Aged
Population. Healthy and Active Aging, pp.428-440.
Stanton, N.A., Young, M.S., & Walker, G.H. (2007). “ The psychology of driving automation:
A discussion with Professor Don Norman.” International Journal of Vehicle Design, 45(30), 289-306.
Šucha, M. 2014. “Road users’ strategies and communication: driver-pedestrian interaction.”Transport Research Arena (TRA) 5th Conference, 2014.
Schneemann, F. and Gohl, I. (2016). “Analyzing driver-pedestrian interaction at crosswalks: A
contribution to autonomous driving in urban environments.” Intelligent Vehicles Symposium 2016: 3843.
Urmson, C. et al. (2015). United States Patent for Pedestrian Notifications. Accessed at:
http://pdfpiw.uspto.gov/.piw?PageNum=0&amp;docid=09196164&amp;IDKey=51D7F153B940&amp;
HomeUrl=http%3A%2F%2Fpatft.uspto.gov%2Fnetacgi%2FnphParser%3FSect1%3DPTO2%2526Sect2%3DHITOFF%2526u%3D%25252Fnetahtml%25252FPTO%25
252Fsearchadv.htm%2526r%3D1%2526p%3D1%2526f%3DG%2526l%3D50%2526d%3DPTXT%2526S1%3DUr
mson%2526OS%3DUrmson%2526RS%3DUrmson

ITF Discussion Paper 2017-10 — © OECD/ITF 201727

Natasha Merat, Ruth Madigan and Sina Nordhoff– Human factors, user requirements, and user acceptance of ride-sharing in automated vehicles

Vallet, M. (2014) Autonomous cars: Will you be a co-pilot or a passenger? Retrieved from:http://www.insurance.com/auto-insurance/claims/autonomous-cars-self-driving.html. (December 21,2016).
Vanoutrive, T., E. Van De Vijver, L. Van Malderen, B. Jourquin, I. Thomas, A. Verhetsel, andF.Witlox. (2012). “What Determines Carpooling to Workplaces in Belgium: Location, Organisation, or
Promotion?” Journal of Transport Geography 22: 77–86
Van der Laan, J.D., Heino, A., & de Waard, D. (1997). “A simple procedure for the assessmentof acceptance of advanced transport telematics.” Transportation Research Part C, 5, 1-10.
Venkatesh, V., Morris, M.G., Davis, G.B., and Davis, F.D. (2003). “User acceptance of
information technology: toward a unified view.” MIS Quarterly, 27 (3), 425-478.
V. Venkatesh, J.Y.L. Thong, X. Xu (2012). “Consumer acceptance and use of information
technology: Extending the unified theory of acceptance and use of technology” MIS Quarterly, 36 (1)(2012), pp. 157–178.
Verberne, F.M.F., Ham, J., & Midden, C.J.H. (2012). “Trust in smart systems: Sharing driving
goals and giving information to increase trustworthiness and acceptability of smart systems in cars.”
Human Factors, 54, 799-810.
Vlassenroot S., Broekx S., De Mol J., Brijs T., and Wets G. (2006), “Driving with intelligent speed
adaptation: final results of the Belgian ISA-trial.” Transportation Research, Part A: Policy and Practice
Weinstock A., Oron-Gilad T. and Parmet Y. (2012). “The effect of system aesthetics on trust,cooperation, satisfaction and annoyance in an imperfect automated system,” Work, 41(1), 258-265.
http://iospress.metapress.com/content/2438772400737724/
Zmud, J. Sener, I., Wagner, J. (2016) “Consumer Acceptance and Travel Behavior Impacts ofAutomated Vehicles.” Texas A&M Transportation Institute, PRC 15-49 F. January 2016. Retrieved
6/12/2016 via: http://d2dtl5nnlpfr0r.cloudfront.net/tti.tamu.edu/documents/PRC-15-49-F.pdf

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