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Investigating autonomous vehicle impacts on individual activity-travel behavior

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  • Dannemiller, Katherine A.
  • Mondal, Aupal
  • Asmussen, Katherine E.
  • Bhat, Chandra R.

Abstract

This paper develops an analytic system to investigate the effects of AV availability on multiple dimensions of activity-travel behavior at once, based on a direct survey-based modeling approach. In particular, the model uses individual socio-demographics, built environment variables, as well as psycho-social variables (in the form of latent psychological constructs) as determinant variables to explain likely AV impacts on five dimensions of short-term activity-travel choices: (1) Additional local area trips (that is, those that would not characterized as long distance trips; a long distance trip was defined in the survey as a trip more than 75 miles one-way), (2) Trip distance to shop or eat-out activities in the local area, (3) Trip distance to leisure activities in the local area, (4) Additional long distance road trips beyond the local area, and (5) Commute travel time. The model system includes a confirmatory factor analysis step, a multivariate linear regression model for the latent constructs, and a multivariate ordered-response model for the five main outcomes just listed. Data from a 2019 Austin area survey of new mobility service adoption and use forms the basis for our empirical analysis. Our results, when aggregated across all respondents, does suggest that AVs may not after all have a substantial impact on overall trip-making levels, although local area trips are likely to become longer (for all purposes, including the commute). The highest impact of AVs will, it appears, be on the number of long distance trips (with such trips increasing). Our in-depth examination of the variations in AV activity-travel responses across population segments and geographies underscores the importance of modeling multiple activity-travel dimensions all at once. In addition, our results highlight the value of using psycho-social latent constructs in studies related to the adoption/use of current and emerging mobility services, both in terms of improved prediction fit as well as proactive strategies to design equitable, safe, and community-driven AV systems. There is likely to be considerable heterogeneity in how different population groups view and respond to AVs, and it is imperative that AV campaigns and AV design consider such heterogeneity so as to not “leave anyone behind”.

Suggested Citation

  • Dannemiller, Katherine A. & Mondal, Aupal & Asmussen, Katherine E. & Bhat, Chandra R., 2021. "Investigating autonomous vehicle impacts on individual activity-travel behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 402-422.
    • Handle: RePEc:eee:transa:v:148:y:2021:i:c:p:402-422
    DOI: 10.1016/j.tra.2021.04.006
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    3. Bhat, Chandra R. & Mondal, Aupal, 2022. "A New Flexible Generalized Heterogeneous Data Model (GHDM) with an Application to Examine the Effect of High Density Neighborhood Living on Bicycling Frequency," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 244-266.
    4. Tamakloe, Reuben & Park, Dongjoo, 2023. "Discovering latent topics and trends in autonomous vehicle-related research: A structural topic modelling approach," Transport Policy, Elsevier, vol. 139(C), pages 1-20.
    5. Mondal, Aupal & Bhat, Chandra R., 2022. "A spatial rank-ordered probit model with an application to travel mode choice," Transportation Research Part B: Methodological, Elsevier, vol. 155(C), pages 374-393.
    6. Asmussen, Katherine E. & Mondal, Aupal & Bhat, Chandra R., 2022. "Adoption of partially automated vehicle technology features and impacts on vehicle miles of travel (VMT)," Transportation Research Part A: Policy and Practice, Elsevier, vol. 158(C), pages 156-179.
    7. Fatemeh Nazari & Mohamadhossein Noruzoliaee & Abolfazl Mohammadian, 2023. "Behavioral acceptance of automated vehicles: The roles of perceived safety concern and current travel behavior," Papers 2302.12225, arXiv.org, revised Jan 2024.
    8. Mohammadhossein Abbasi & Amir Reza Mamdoohi & Grzegorz Sierpiński & Francesco Ciari, 2023. "Usage Intention of Shared Autonomous Vehicles with Dynamic Ride Sharing on Long-Distance Trips," Sustainability, MDPI, vol. 15(2), pages 1-17, January.
    9. Andrew L. Kun & Raffaella Sadun & Orit Shaer & Thomaz Teodorovicz, 2022. "Multitasking while driving: a time use study of commuting knowledge workers to access current and future uses," POID Working Papers 028, Centre for Economic Performance, LSE.
    10. Katalin Ásványi & Márk Miskolczi & Melinda Jászberényi & Zsófia Kenesei & László Kökény, 2022. "The Emergence of Unconventional Tourism Services Based on Autonomous Vehicles (AVs)—Attitude Analysis of Tourism Experts Using the Q Methodology," Sustainability, MDPI, vol. 14(6), pages 1-14, March.
    11. Macea, Luis F. & Serrano, Iván & Carcache-Guas, Camila, 2023. "A reservation-based parking behavioral model for parking demand management in urban areas," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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