Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

Talebian, Ahmadreza; Mishra, Sabyasachee

doi:10.1016/j.trc.2018.06.005

Cited by 181 publications

(90 citation statements)

References 74 publications

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“…Herein, user expectation, perceptions, and acceptance towards such operational characteristics should not be disregarded when investigating factors that influence user WTP for AV services. Moreover, the results from Shin et al [27] who evaluated WTP of South Koreans for private vehicles with different smart options (automated driving, connectivity, voice command, wireless internet and availability of software application) at different option prices showed that consumers are homogenously sensitive to price but vary in their preferences of the smart options. This shows the importance to understand the effects of the associated perceptions of the AV services at individual service quality attributes instead of the services as a whole bundle.…”

Section: Past Studies Investigating Wtp For Av/av Technologymentioning

confidence: 99%

Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

Chee

Susilo

Wong

et al. 2020

Eur. Transp. Res. Rev.

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Introduction: Travel demand and travel satisfaction of a transport service are affected by user perceptions of the service quality attributes, and such perceptions should be included in studying user willingness-to-pay (WTP) for automated vehicle (AV) services. This study applied structural equation modelling with service quality attribute perceptions as latent variables affecting WTP. Objectives: We investigated how WTP AV services are affected by socio-demographic characteristics, knowledge and experiences with AV, existing travel modes and particularly, perceptions of the associated service quality attributes. The AV services are: 1) on-demand personalised AV (PAV) service, 2) demand responsive shared AV (SAV) service, and 3) first−/last-mile automated bus (AB) service. Methods: The data were collected from 584 potential users of a first−/last-mile AB service trial operated in Kista, Stockholm. Results: Results show people hold different expectations towards each type of AV service. These expectations act as the minimum requirements for people to pay for the AV services. Respondents are found to be willing to pay more for PAV service if it is safe, provides good ride comfort, and is competitively priced relative to the price travelling by metro and train over a same distance. Other than service quality attribute perceptions, income level, existing travel modes for daily trips, familiarity with automated driving technology and AB ride experience are important factors affecting WTP for the AV services. Conclusion: The developed model can be applied to understand expectations of potential users towards a new AV service, and to identify user groups who are willing to pay the service. New AV services can thus be designed sensibly according to users' actual needs.

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Section: Past Studies Investigating Wtp For Av/av Technologymentioning

confidence: 99%

Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

Chee

Susilo

Wong

et al. 2020

Eur. Transp. Res. Rev.

View full text Add to dashboard Cite

show abstract

“…These two studies revealed a WTP much less than the current purchase price provided by the manufacturers, which means that the mass adoption still needs some time. Talebian and Mishra (2018) predicted the AV adoption through an agent-based approach where WTP could be changed by peer-to-peer communication. Several researchers used stated preference (SP) survey to examine the vehicle choice of users between CVs and AVs.…”

Section: Literature Reviewmentioning

confidence: 99%

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Haspiel

Zhang

et al. 2019

SSRN Journal

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This study proposes an autonomous vehicle incentive program design (AV-IPD) problem for a local government, which aims to promote the adoption of autonomous vehicles (AVs) by deploying AV lanes and subsidizing the purchase of AVs with the objective of system optimum subject to a fixed budget. As it is difficult to anticipate the changing AV market conditions in the future, we take into account the uncertainty in AV purchase price. For the AV-IPD problem, we firstly decide which regular lanes should be converted into exclusive AV lanes. Secondly, we determine the AV purchase subsidy for each realization of AV purchase price with a given AV lane deployment scheme. A binary logit model is applied to characterize the vehicle choice behavior of users. The AV-IPD problem is formulated as a two-stage stochastic programming model with equilibrium constraints. To solve the AV-IPD problem, we develop a solution method based on linear approximation techniques and duality theories. Numerical experiments are conducted to demonstrate the effectiveness of the proposed model and solution method. 1. Introduction The last few years have witnessed an explosive development on autonomous vehicle (AV) technology. Compared to the conventional vehicles (CVs), AVs can achieve a wide range of benefits in terms of traffic capacity, safety and vehicular emission. A successful implementation of AVs depends on the deployment of transport infrastructure catered for AVs and sufficient AV adoption. To promote the market penetration of AVs, a local government could design an AV incentive program to deploy exclusive AV lanes and subsidize users to purchase AVs subject to a fixed budget. The purpose of setting AV lanes is to separate AVs and CVs and exploit AV's advantage in improving road capacity. Current studies demonstrated that the road capacity could become up to triple in full AV environment by a shorter headway (Tientrakool et al., 2011). However, AVs become less beneficial under the mixed traffic with AVs and CVs. With a low AV ratio, the road capacity is possible to decrease due to speed variation and shock waves (Van Arem et al., 2006). To eliminate the issues arising from the mixed traffic, a local government can divide roads into regular lanes and AV lanes (Talebpour et al., 2017) so that AVs can travel with very short headway on AV lanes. The AV users have right of way on both regular and exclusive lanes while CV users can only travel on regular lanes. Different from conventional road construction projects, deploying AV lanes does not increase the number of lanes but converts some regular lanes into AV exclusive lanes. Some sensors are installed to facilitate the vehicle-to-vehicle (V2V) and vehicleto-infrastructure (V2I) communications (Jia et al., 2016). Some roads may require a novel traffic signal system (Li and Zhou, 2017). Besides, the users' vehicle choice between AVs and CVs is another feature needed to consider for the deployment of AV lanes. This is

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“…According to Nikitas (2020) [40] there are 10 areas of priority for CAVs policy and planning that need to be addressed for a smooth transition: technology, legislation, crisis and employment ethics, road infrastructure and land use, integration, traffic safety, cybersecurity and privacy, business models, traffic congestion and travel behaviour and finally acceptability, trust and customer readiness. Even if adequate answers are provided to all the above critical themes the future automobile fleet will be near homogenous no sooner than 2050 and only if CAV prices are reasonably low [41].…”

Section: Connected and Autonomous Vehiclesmentioning

confidence: 99%

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

et al. 2020

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Artificial intelligence (AI) is a powerful concept still in its infancy that has the potential, if utilised responsibly, to provide a vehicle for positive change that could promote sustainable transitions to a more resource-efficient livability paradigm. AI with its deep learning functions and capabilities can be employed as a tool which empowers machines to solve problems that could reform urban landscapes as we have known them for decades now and help with establishing a new era; the era of the “smart city”. One of the key areas that AI can redefine is transport. Mobility provision and its impact on urban development can be significantly improved by the employment of intelligent transport systems in general and automated transport in particular. This new breed of AI-based mobility, despite its machine-orientation, has to be a user-centred technology that “understands” and “satisfies” the human user, the markets and the society as a whole. Trust should be built, and risks should be eliminated, for this transition to take off. This paper provides a novel conceptual contribution that thoroughly discusses the scarcely studied nexus of AI, transportation and the smart city and how this will affect urban futures. It specifically covers key smart mobility initiatives referring to Connected and Autonomous Vehicles (CAVs), autonomous Personal and Unmanned Aerial Vehicles (PAVs and UAVs) and Mobility-as-a-Service (MaaS), but also interventions that may work as enabling technologies for transport, such as the Internet of Things (IoT) and Physical Internet (PI) or reflect broader transformations like Industry 4.0. This work is ultimately a reference tool for researchers and city planners that provides clear and systematic definitions of the ambiguous smart mobility terms of tomorrow and describes their individual and collective roles underpinning the nexus in scope.

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Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations

Cited by 181 publications

References 74 publications

Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

Which factors affect willingness-to-pay for automated vehicle services? Evidence from public road deployment in Stockholm, Sweden

Look Who’s Talking Now: Implications of AV’s Explanations on Driver’s Trust, AV Preference, Anxiety and Mental Workload

Artificial Intelligence, Transport and the Smart City: Definitions and Dimensions of a New Mobility Era

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