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

Abstract

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.

Users’ Acceptance of Connected and Automated Shuttles for Tourism Purposes: A Survey Study

“…In general, it can be said that the results do not show a willingness to pay a price for an AS service that is significantly higher than the average amount paid for services carried out with traditional transport systems. This is partially in line with the results obtained by Chee et al [65], which found a WTP for automated bus service in urban contexts not higher than the average cost for traditional public transport services for 44% of the respondents in their sample. These results show a considerable confidence of potential users for the technology used in AS, but with regard to the willingness to pay they seem to identify the proposed vehicle more with the service provided than with its inherent technological characteristics.…”

Human factors of digitalized mobility forms and services

“…While intermodal commuters, combining PT and the car, are most open to shared (automated) services, people commuting solely with PT are least open. In line with the results by Chee et al [14], the study thus shows that current commuter mode choice is a relevant predictor of the acceptance of new car-based mobility services.…”

“…The third paper by Chee et al [14] examined the potential use of different automated vehicle (AV) services, or more specifically, which factors affect the willingness to pay for their use. Apart from a first/last mile automated bus service that was already in operation as part of an AV trial in Sweden, on-demand personalized AV services and demand responsive shared AV services were considered.…”

Lessons from the deployment of the worlds first automated bus service on a mixed public road in Stockholm

“…For further extensive reviews of the plausible impacts of automated vehicles, McGehee et al (2016), Milakis et al (2017), Innamaa et al (2017) and the MANTRA (2019) project report provide a comprehensive description of the plausible societal impacts and policy implementation challenges. Hoogendoorn et al (2014) discussed the roles of human factors and expected traffic impacts; Chee et al (2020a) and Nordhoff et al (2019) reported on automated vehicle (AV) technology acceptance for daily travel; and Do et al (2019) reported on design and control of the automated vehicle system. More recently, Soteropoulos et al (2019) provided a systematic overview of different modelling approaches that have been used to explain the impacts of automated vehicles on travel behaviour and land use characteristics.…”

“…Light detection and ranging (LIDAR) data 1 for a limited time was collected in order to get a glimpse of the interaction between the automated bus and pedestrians, private cars and other road users, in the given road space. The complete analyses of each of these cases can be seen at Chee et al (2020aChee et al ( , 2020bChee et al ( , 2021.…”