On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times

Moore, Michael; Lavieri, Patrícia S.; Dias, Felipe F.; Bhat, Chandra R.

doi:10.1016/j.trc.2019.11.013

Cited by 62 publications

(20 citation statements)

References 41 publications

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“…Consequently, the more-trips question may have drawn greater attention to the issue of limited time budgets than to longer trips. Increases in the number and length of trips correspond with decreased VTT (Moore et al 2020;Wardman et al 2020).…”

Section: Discussionmentioning

confidence: 99%

“…Simulations focusing on the impact of AVs have linked reduced VTT to increased car use, especially when AVs are personal rather than shared (Soteropoulos et al 2019). Lower VTT with automated driving has also been linked to decisions to relocate and accept longer distances between home and work (Moore et al 2020). VTT, together with the direct monetary cost of travel, can be used to determine the generalised costs of travel (Litman 2016).…”

Section: Introductionmentioning

confidence: 99%

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Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

et al. 2021

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Automated vehicles (AVs) are expected to change personal mobility in the near future. Most studies on the mobility impacts of AVs focus on fully automated (SAE L5) vehicles, but the gradual development of the technology will probably bring AVs with more limited capabilities to begin with. This stated-preference study focused on the potential mobility impacts of conditionally automated (L3) and highly automated cars (L4). We investigated personal mobility impacts among 59 participants who experienced automated driving repeatedly in a driving simulator. Half of them drove with an L3 and half with an L4 motorway function. After the first and final drive they answered questions on their travel experience and how automated vehicles could change their mobility. After the drives, participants in both groups were willing to accept 30–50% longer travel times for a 30 min trip if they did not need to drive the whole trip themselves. This translates into savings of around 30% for the perceived value of travel time on routes where automation is available. There were no statistically significant differences between L3 and L4 in the accepted travel times. Most participants did not expect to make more trips with automated cars, but around half of them anticipated making longer trips. The amount of car travel may increase more with L4 than with L3 automation, possibly due somewhat to changes in the experienced travel quality. The results suggest that the mobility impacts of automated driving may increase with a higher level of automation.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

et al. 2021

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“…Considering a private ownership hypothesis, ref. [28] found that 32% of respondents would be ready to move home with a longer commute time. Finally, and contrastingly, the empirical study led by [29] concluded that no long-term changes in the valuation of travel time due to the advent of AVs should be expected to be reflected in residential location choice.…”

Section: Residential Locationmentioning

confidence: 99%

Anticipating Changes in Lifestyles That Shape Travel Behavior in an Autonomous Vehicle Era—A Method-Oriented Systematic Literature Review

Aguiléra

Gallic

2022

Future Transportation

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This article proposes a systematic review of the recent literature on the impacts of the deployment of autonomous vehicles through the lens of lifestyle changes that will modify our mobility practices. It discusses the main findings of the studies, analyzes their links with the foresight methods used by their authors, and identifies research gaps. Four components of lifestyles are considered: residential location, car ownership, activity patterns, and tourism. Particular attention is given to the diversity of the reviewed foresight approaches, to the way they complement one another in the construction of knowledge, and to their influence on the forecasts and the lessons learned. Our work shows a convergence of results across methods, especially for expected impacts on household location and car ownership, and the influence of the way autonomous vehicles are deployed. Our analysis also makes it possible to draw up a more comprehensive and nuanced picture of the anticipated changes in lifestyles, and the main sources of uncertainty. Finally, our work identifies several research gaps and avenues for future studies such as the impacts on job choices and tourism, the need for a better understanding of the links between the different deployment models for autonomous vehicles, and the need to explore more scenarios that are compatible with environmentally- and socially-oriented goals.

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“…Pourrahmani et al (29), using as their case study a full-scale penetration of CAVs in the San Francisco Bay Area, estimated a 10% increase in car travel mileage (i.e., more people using a car more often for longer distances) and an 11% decrease in walk/bicycle trip mode share as well as significant emission reduction by several CAV-enabled mechanisms such as eco-driving [more than 30% carbon dioxide (CO 2 ) emission reduction], engine performance adjustment (over 20% CO 2 emission reduction), and platooning (i.e., road space use optimization). Moore et al (30) predict a substantial extent of urban sprawl due to CAVs, potentially up to a 68% increase in the horizontal spread of cities, unless proactive planning and policies are implemented to avert such consequences. These impacts are mostly internally offset such that the overall impact of AV deployment on GHG emissions is not significant in the near-to mid-term.…”

Section: 11mentioning

confidence: 99%

The Environmental and Resource Dimensions of Automated Transport: A Nexus for Enabling Vehicle Automation to Support Sustainable Urban Mobility

Nikitas

Thomopoulos

Milakis

2021

Annu. Rev. Environ. Resour.

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Automation carries paradigm-shifting potential for urban transport and has critical sustainability dimensions for the future of our cities. This article examines the diverse environmental and energy-related dimensions of automated mobility at the city level by reviewing an emerging and increasingly diversified volume of literature for road, rail, water, and air passenger transport. The multimodal nature of this investigation provides the opportunity for a novel contribution that adds value to the literature in four distinctive ways. It reviews from a sustainability angle the state of the art underpinning the transition to a paradigm of automated mobility, identifies current knowledge gaps highlighting the scarcity of non-technicalresearch outside the autonomous car's realm, articulates future directions for research and policy development, and proposes a conceptual model that contextualizes the automation-connectivity-electrification-sharing-multimodality nexus as the only way forward for vehicle automation to reach its pro-environmental and resource-saving potential.

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On investigating the potential effects of private autonomous vehicle use on home/work relocations and commute times

Cited by 62 publications

References 41 publications

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

Travel experience matters: Expected personal mobility impacts after simulated L3/L4 automated driving

Anticipating Changes in Lifestyles That Shape Travel Behavior in an Autonomous Vehicle Era—A Method-Oriented Systematic Literature Review

The Environmental and Resource Dimensions of Automated Transport: A Nexus for Enabling Vehicle Automation to Support Sustainable Urban Mobility

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