An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Nair, Gopindra Sivakumar; Astroza, Sebastián; Bhat, Chandra R.; Khoeini, Sara; Pendyala, Ram M.

doi:10.1007/s11116-018-9945-9

Cited by 45 publications

(22 citation statements)

References 14 publications

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“…Auto enterprises, policy makers, and researchers are increasingly interested in understanding the public acceptance and potential use of self-driving technology. Numerous studies have designed stated preference experiments to explore the underlying factors affecting the adoption of AVs ( 11, 12, 15–29 ). These studies measure the adoption rates in three ways: (i) general interest scale or intent to use ( 17, 19–26 ); (ii) WTP ( 15 , 18 , 27 , 28 ); (iii) mode choice for a given trip ( 11 ).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The surveys provide a random sample because their respondents may not have a predisposition with regard to self-driving technology. Lavieri et al ( 25 ) and Nair et al ( 29 ) have studied the general interest in AVs and SAVs using the 2015 Puget Sound Regional Travel Study data set; however, they have not analyzed the factors affecting an employee’s interest in commuting by AVs and SAVs. Commuting trips have large impacts on the environment because of their high frequency (usually 5 days per week), time of day, and distance traveled ( 3 – 5 ).…”

Section: Literature Reviewmentioning

confidence: 99%

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Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

Wang¹,

Akar²

2019

Transportation Research Record

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The emergence of autonomous vehicles (AVs) may shape the future landscape of urban mobility. Although there is a growing literature on public opinions with regard to self-driving, little attention has been explicitly given to the commuters and their preferences for their commute trips. Using data from the 2015 and 2017 Puget Sound Regional Household Travel Studies, this study investigates the factors associated with employees’ propensity for using AVs. We develop a bivariate ordered probit model to jointly estimate the determinants of levels of interest in (i) commuting alone using AVs and (ii) commuting with others using shared AVs. Not surprisingly, it was found that current solo drivers are more likely to commute alone using AVs compared with other mode users. Significant differences were not found between current drivers and other commuters when it comes to the potential use of shared AVs. The results also reveal that, controlling for other factors, commuters surveyed in 2017 are less likely to be interested in shared AVs compared with their 2015 counterparts. The conclusion is that more planning efforts are needed to support the market penetration of shared AVs.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

Wang¹,

Akar²

2019

Transportation Research Record

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“…Previous studies (see e.g. Woldeamanuel and Nguyen, 2018;Sanbonmatsu et al, 2018;Daziano et al, 2017;Haboucha et al, 2017;Nair et al, 2017;Bansal et al, 2016;Lavieri et al, 2017;Bansal and Kockelman, 2017;Lavasani et al, 2016;Kyriakidis, et al, 2015;Adnan et al, 2018;Umberger, 2016) have attempted to examine public opinions and to forecast long-term adoption of autonomous mobility technologies, by focusing largely on the financial attributes of driverless technologies, and/or accounting for a limited number of attitudinal factors, such as public safety and privacy concerns and confidence in driverless technologies. Another strand of AV research has sought to estimate optimal vehicle fleet-size on dynamic AV ridesharing systems (Fagmant and Kockelman, 2015), potential effect of AVs on users' daily activities (Pudāne et al, 2018), as well as to assess the land use and environmental impacts of various driverless cars adoption and diffusion scenarios (e.g.…”

Section: Introductionmentioning

confidence: 99%

Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars

Acheampong

Cugurullo

2019

Transportation Research Part F: Traffic Psychology and Behaviou

232

102

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“…As a model, it should best represent survey results when asked to rank hypotetical alternatives. The authors are using this model to attainthe results which contribute to their conclusions Nair et al (2018) are applying their model to a survey about alternative configurations of AVs and found differences in responses across certain socioeconomic and sociodemographic characteristics. Thus, they draw the conclusion that ranked and preference-based choices made are not so determined.…”

Section: Confusion About Behavioural Trends For Future Urban Mobilitymentioning

confidence: 99%

Urban mobility and public transport: future perspectives and review

Ceder

2020

International Journal of Urban Sciences

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The purpose of this work is to review urban transportation likely to be offered in the future. Trip-making behaviour has already changed considerably as lifestyles change and they will continue to change in the future. This work reflects and places emphasis on profound thinking about the possibilities, rather than predicting them. Thoughts about possibilities for the future draw upon imagination, perceived and justified feasibility, and lessons gained from the past. This work attempts to capture the possibilities, logistics and travel modes of future urban transportation. A visionary, feasibility-related approach grounded in a realist perspective is proposed, only conceptually, to explore plausible visions for the future. In addition, this work shows the inefficiency of using private cars (PCs) and argues that in the development of autonomous and electric vehicles, PCs cannot provide a solution competitive with the potential that urban transportation systems have for the future. Hence, the solutions for the future must be based on public transport (PT) modes of travel, regardless of whether they are metro, bus, light rail, tram, ridesharing services, an ordinary taxi, personal rapid transit, or any other PT-based future mode. The key principal of operation for the mobility of a smart city will be the ability to optimize the connectivity of movement in order to approach a seamless move, while endowing the phrase door-to-door travel with new meaning. Finally, it would be remiss not to mention the unforeseeable implications of the Covid-19 pandemic for future mobility, more controllable by automation of non-privately owned vehicle, and with the prospect of people demonstrating a greater inclination towards changing their habits, behaviour, and thinking paradigms.

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An application of a rank ordered probit modeling approach to understanding level of interest in autonomous vehicles

Cited by 45 publications

References 14 publications

Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

Factors Affecting the Adoption of Autonomous Vehicles for Commute Trips: An Analysis with the 2015 and 2017 Puget Sound Travel Surveys

Capturing the behavioural determinants behind the adoption of autonomous vehicles: Conceptual frameworks and measurement models to predict public transport, sharing and ownership trends of self-driving cars

Urban mobility and public transport: future perspectives and review

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