Anticipating long-distance travel shifts due to self-driving vehicles

Perrine, Kenneth A.; Kockelman, Kara M.; Huang, Yantao

doi:10.1016/j.jtrangeo.2019.102547

Cited by 48 publications

(16 citation statements)

References 25 publications

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“…Based on the inherent AI, decision-making process in AVs enables all the mechanical operations of driving without the active control or continuous monitoring of a natural person. Although some people have safety, legal liability and data privacy concerns regarding the use of AVs (e.g., Gkartzonikas & Gkritza, 2019;Kröger et al, 2019;Perrine et al, 2018), it is believed that widespread adoption of AVs can bring about 1 According to SAE International (2016), AV technology can be classified according to six categories (i.e., L0~L5) ranging from Level 0, where the human driver does all the driving tasks without automation, to Level 5, where the AV can perform all driving tasks without any human intervention. Throughout this article, we refer to AVs as high-level (i.e., Level 4 and Level 5) vehicles, as suggested by Becker & Axhausen (2017).…”

Section: Introductionmentioning

confidence: 99%

“…Based on the inherent AI, decision‐making process in AVs enables all the mechanical operations of driving without the active control or continuous monitoring of a natural person. Although some people have safety, legal liability and data privacy concerns regarding the use of AVs (e.g., Gkartzonikas & Gkritza, 2019; Kröger et al, 2019; Perrine et al, 2018), it is believed that widespread adoption of AVs can bring about various benefits. For example, with the application of AI, AVs are expected to help improve road safety by leading to fewer traffic accidents, which are mainly caused by human error (Choi & Ji, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Understanding the potential adoption of autonomous vehicles in China: The perspective of behavioral reasoning theory

Huang

Qian

2021

Psychology and Marketing

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Powered by artificial intelligence (AI), autonomous vehicles (AVs) are one of the most disruptive technologies to reduce road incidents, improve road efficiency and enhance mobility for underserved populations. However, existing literature of AV adoption is either based on descriptive analysis or simple applications of conventional behavioral frameworks. Drawing on behavioral reasoning theory (BRT), we examine the effect of reasoning process on consumers' attitudes and intentions toward adopting AVs, and how their psychological traits moderates the relationships. By conducting a nationwide survey in China (n = 849), we identify positive (negative) effects of individuals' reasons for (against) adopting AVs on their adoption intentions for AVs, and positive influence of face consciousness on the reasoning process. More importantly, we find that the psychological trait of need for uniqueness strengthens the association between consumers' reasoning for AVs and their adoption intention, while the risk aversion trait intensifies the negative relationships between consumers' reasoning against AVs and their attitude/adoption intention. We contribute to the literature of AI adoption by establishing the important roles of consumers' reasons for and against such innovations. We also extend the BRT framework by identifying the roles of psychological traits in moderating the relationships among reasoning, attitude, and adoption intention.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Understanding the potential adoption of autonomous vehicles in China: The perspective of behavioral reasoning theory

Huang

Qian

2021

Psychology and Marketing

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“…For Krueger et al [15], they could provide a flexible solution compared to other modes to complete the "last mile", encouraging multimodality. Moreover, because AVs will free users from driving, a significant reduction (of around 25-75%, depending on the various authors) in the value of trip time perceived by users is generally expected [16][17][18], although this reduction cannot be generalized to all cases [19].…”

Section: Introductionmentioning

confidence: 99%

Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model

et al. 2021

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Autonomous vehicles (AVs) can generate major changes in urban systems due to their ability to use road infrastructures more efficiently and shorten trip times. However, there is great uncertainty about these effects and about whether the use of these vehicles will continue to be private, in continuity with the current paradigm, or whether they will become shared (carsharing/ridesharing). In order to try to shed light on these matters, the use of a scenario-based methodology and the evaluation of the scenarios using a land use–transport interaction model (LUTI model TRANSPACE) is proposed. This model allows simulating the impacts that changes in the transport system can generate on the location of households and companies oriented to local demand and accessibility conditions. The obtained results allow us to state that, if AVs would generate a significant increase in the capacity of urban and interurban road infrastructures, the impacts on mobility and on the location of activities could be positive, with a decrease in the distances traveled, trip times, and no evidence of significant urban sprawl processes. However, if these increases in capacity are accompanied by a large augment in the demand for shared journeys by new users (young, elderly) or empty journeys, the positive effects could disappear. Thus, this scenario would imply an increase in trip times, reduced accessibilities, and longer average distances traveled, all of which could cause the unwanted effect of expelling activities from the consolidated urban center.

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“…Pudā ne et al, de Almeida Correia, Looff, van Cranenburgh, Snelder, & van Arem, 2019), a focus on the medium-and longer-term behavioral implications of AVs has been fairly limited. Perrine, Kockelman, and Huang (2020), by using the inter-regional rJourney travel demand model, analyzed changes in mode and destination choices for long distance trips with an AV option available. Olsen and Sweet (2019) explored commuters' willingness to commute farther, while Kim, Mokhtarian, and Circella (2019c) analyzed potential changes in residential location and vehicle ownership in the AV era.…”

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confidence: 99%

How, and for whom, will activity patterns be modified by self-driving cars? Expectations from the state of Georgia

Kim

Mokhtarian

Circella

2020

Transportation Research Part F: Traffic Psychology and Behaviou

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Many studies have explored travelers' perceptions of self-driving cars (or autonomous vehicles, AVs) and their potential impacts. However, medium-term modifications in activity patterns (such as increasing trip frequencies and changing destinations) have been less explored. Using 2017-2018 survey data collected in the US state of Georgia, this paper (1) measures (at a general level) how people expect their activity patterns to change in a hypothetical all-AV era; (2) identifies population segments having similar profiles of expected changes; and (3) further profiles each segment on the basis of attitudinal, sociodemographic, and geographic characteristics. In the survey, respondents were asked to express their expectations regarding 16 potential activity modifications induced by AVs. We first conducted an exploratory factor analysis (EFA) to reduce the dimensionality of the activity-change vector characterizing each individual, and estimated non-mean-centered (NMC) factor scores (which have been rarely used in applied psychology). The EFA solution identified four dimensions of activity change: distance, time flexibility, frequency, and long distance/leisure. Next, we clustered Georgians with respect to these four-dimensional expectation vectors. The cluster solution uncovered six segments: no change, change unlikely, more leisure/long distance, longer trips, more travel, and time flexibility & more leisure/long distance. Using NMC factor scores identified considerably more inertia with respect to expectations for change than would have been apparent from the usual mean-centered scores. Finally, the various segments exhibit distinctive demographics and general attitudes. For example, those in the more leisure/long distance cluster tend to be higher income and are more likely to be Atlanta-region residents compared to other clusters, while those in the no change and change unlikely clusters tend to be older and are more likely to be rural residents.

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Anticipating long-distance travel shifts due to self-driving vehicles

Cited by 48 publications

References 25 publications

Understanding the potential adoption of autonomous vehicles in China: The perspective of behavioral reasoning theory

Understanding the potential adoption of autonomous vehicles in China: The perspective of behavioral reasoning theory

Modeling the Impacts of Autonomous Vehicles on Land Use Using a LUTI Model

How, and for whom, will activity patterns be modified by self-driving cars? Expectations from the state of Georgia

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