Estimation of the value of time for automated driving using revealed and stated preference methods

Kolarova, Viktoriya; Steck, Felix; Cyganski, Rita; Trommer, Stefan

doi:10.1016/j.trpro.2018.09.044

Cited by 47 publications

(11 citation statements)

References 8 publications

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“…In a stated choice experiment conducted among an Australian online panel, sequentially shared and simultaneously shared automated vehicles showed to be perceived as two distinctive modes by the participants, with a strong preference for sequentially shared SAV over simultaneously shared SAV [25]. Conversely, a stated choice experiment conducted among a German online panel found that the simultaneously shared SAV is preferred over the sequentially shared one [24] the authors suggest that this could be attributed to the lower costs associated with the simultaneously shared option. In regard to potential early adopters of SAV, the following demographics have been found to describe people with a higher preference for SAV: people currently using public transport or using multiple modes frequently [25], younger people [19,25], men [19] and people with a higher income or a higher degree of education [4,5,6,19].…”

Section: Introductionmentioning

confidence: 90%

Identifying user classes for shared and automated mobility services

Winter

Cats

Martens

et al. 2020

Eur. Transp. Res. Rev.

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New forms of shared mobility such as free-floating car-sharing services and shared automated vehicles have the potential to change urban travel behaviour. In this paper, we identify potential user classes for these new modes. For this, a stated choice experiment on mode choice among a sample of the Dutch urban population has been conducted, which features free-floating car-sharing and shared automated vehicles next to private vehicles, bus, and taxi. The experimental design allows disentangling the effects of vehicle ownership, vehicle sharing and vehicle automation on the perceived utility of these modes. Further contributions lie in the identification of user classes for shared and automated mobility services and their potential migration from their current modes to the these services. Latent class choice models were estimated to capture the heterogeneity in these preferences among the respondents. The most explanatory mode choice model is obtained by estimating a 3-class nested logit model capturing the impact of vehicle ownership. The results show that higher educated and more time-sensitive respondents are more inclined than others to favour the (automated) car-sharing options. By simulating a scenario that directly compares car with free-floating car-sharing and taxi with shared automated vehicles, a migration analysis has been performed. This analysis shows that the preferences towards shared automated vehicles and free-floating car-sharing is highest for those currently combining car and public transport for their commute. Commuters using the car showed a high preference towards free-floating car-sharing, in particular as for the latter no parking fees are issued. Respondents currently commuting by public transport showed the lowest preference for the new modes.

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Section: Introductionmentioning

confidence: 90%

Identifying user classes for shared and automated mobility services

Winter

Cats

Martens

et al. 2020

Eur. Transp. Res. Rev.

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show abstract

“…It has been speculated that the increased convenience and the possibility of spending time more productively in an AV would make individuals more tolerant of higher in-vehicle travel times ( 17 , 22 , 23 ). To model this fact, it has been suggested in literature to the reduce value of time (VOT) by some factor when computing mode utilities ( 24 , 25 ).…”

Section: Expected Impacts Of Avs On the Transportation Systemmentioning

confidence: 99%

“…In our application to the NCTCOG model, we used F AV_TripDistVOT values of 0.75, based on findings published elsewhere ( 17 , 22 , 23 ).…”

Section: Modeling Frameworkmentioning

confidence: 99%

Incorporating Autonomous Vehicles in the Traditional Four-Step Model

Dias

Nair

Juri

et al. 2020

Transportation Research Record

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Automated vehicles (AVs) are a concrete possibility in the near future. As AVs may shift transportation paradigms, transportation agencies are eager to update their models to consider them in planning. In this context, the use of advanced models may be challenging, given the uncertainty in the use and deployment of AVs. In this paper, we present a general framework to extend the four-step model to include AVs, and test our extension on North Central Texas Council of Governments’ model. Our approach introduces a module for AV ownership and exogenous parameters into an existing four-step model to account for changes in travel decisions for AV owners, and for the impacts of AVs on network performance. The latter is modeled using the concept of passenger-car-equivalent to avoid imposing network-wide assumptions on AVs’ capacity consumption. We analyze five scenarios, representing different assumptions on the impacts of AVs, and include references to inform the selection of modeling parameters. We compute aggregate metrics that suggest that the model is sensitive to the proposed parameters, with the passenger-car-equivalent assumptions having the largest impact on model outcomes. Results suggest that, even when we assume that AVs can better use network capacity and that trip-making rates do not drastically increase, AVs may lead to an increase of about 2.8% in vehicle-hours traveled while also improving speeds by about 1.8%. If AVs introduce additional friction on traffic, the system performance may deteriorate. The analyses presented here suggest that existing modeling tools may be adjusted to support analyses of a future with AVs.

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“…Moving forwards, with the dawn of autonomous vehicles (AVs) there is likely to be an increasing need to expand the evidence base around the impact of travel time use on VTTS. The Department has tentatively taken early studies by Wadud, MacKenzie and Leiby (2016) and Kolarova et al (2017) to indicate an approximately 25% reduction in the VTTS with AVs compared to conventional cars. The evidence to date is thin, however, and there is no authoritative figure to use in policy appraisal.…”

Section: Summary and Synthesismentioning

confidence: 99%

Worthwhile Use of Travel Time and Applications in the United Kingdom

Batley

Dekker

Stead³

2020

International Transport Forum Discussion Papers

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This paper is largely underpinned by the study "Provision of market research for value of time savings and reliability" (RM3388 SB-1569) undertaken by the Arup, ITS Leeds and Accent consortium for the UK Department for Transport (referred to henceforth as the "Department").The authors acknowledge the contributions of various parties which helped facilitate that study, as follows:• Staff from the UK Department for Transport's Transport Analysis and Strategic Modelling (TASM) Division, in particular the project officer Adam Spencer, and colleagues Alice Crossley, Margot Shatz and Marcus Spray, who guided the study on a day-to-day basis.• Members of the Department's wider Project Board, who reviewed the progress and direction of the study at key milestones.• Stakeholders from various public and private sector organisations across the UK transport sector, who attended workshops at the outset of the study and at the end of the survey design and testing phase.• Operators of railway stations, "other PT" services, motorway service stations, petrol stations and other areas where interviewers worked.

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Estimation of the value of time for automated driving using revealed and stated preference methods

Cited by 47 publications

References 8 publications

Identifying user classes for shared and automated mobility services

Identifying user classes for shared and automated mobility services

Incorporating Autonomous Vehicles in the Traditional Four-Step Model

Worthwhile Use of Travel Time and Applications in the United Kingdom

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