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Robotaxis or autonomous shuttles? The role of urban representations and travel habits in tomorrow's mode choice in France

Author

Listed:
  • SALLADARRE, Frédéric
  • LE BOENNEC, Rémy
  • BEL, Marlène

Abstract

Autonomous vehicles (AVs) will profoundly modify our travel habits. The collective impact of AVs will differ according to the autonomous mode choice. In this paper, we apply a simultaneous-equation model to a database from an original 2017 survey of French mobility users to analyze their acceptance of two forms of autonomous transport mode: autonomous shuttles and robotaxis (N=3,297). Our results show that the intention to use autonomous shuttles is on average greater than robotaxis. Gender and age influence autonomous mode choice, as well as the current transport mode. In addition, location and urban representations play a central role.

Suggested Citation

  • SALLADARRE, Frédéric & LE BOENNEC, Rémy & BEL, Marlène, 2021. "Robotaxis or autonomous shuttles? The role of urban representations and travel habits in tomorrow's mode choice in France," MPRA Paper 113635, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:113635
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    References listed on IDEAS

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    1. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    2. Shen, Yu & Zhang, Hongmou & Zhao, Jinhua, 2018. "Integrating shared autonomous vehicle in public transportation system: A supply-side simulation of the first-mile service in Singapore," Transportation Research Part A: Policy and Practice, Elsevier, vol. 113(C), pages 125-136.
    3. Felix Becker & Kay W. Axhausen, 2017. "Literature review on surveys investigating the acceptance of automated vehicles," Transportation, Springer, vol. 44(6), pages 1293-1306, November.
    4. Natasha Merat & Ruth Madigan & Sina Nordhoff, 2017. "Human Factors, User Requirements, and User Acceptance of Ride-Sharing in Automated Vehicles," International Transport Forum Discussion Papers 2017/10, OECD Publishing.
    5. Pettigrew, Simone & Dana, Liyuwork Mitiku & Norman, Richard, 2019. "Clusters of potential autonomous vehicles users according to propensity to use individual versus shared vehicles," Transport Policy, Elsevier, vol. 76(C), pages 13-20.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Robotaxi; Autonomous shuttle; Autonomous vehicle; Urban representation; Travel habit; Intention to use; Acceptance; Transport mode; Autonomous mode choice; Simultaneous-equation model;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise

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