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Anticipating Mode Shifts Owing to Automated Vehicles Based on a Tourist Behavior Model: Case Study on Travel to Kagoshima

Author

Listed:
  • Ruixiang Zhou

    (Department of Automotive Science, Graduate School of Integrated Frontier Sciences, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi 819-0395, Fukuoka, Japan)

  • Yoshinao Oeda

    (Department of Urban and Environmental Engineering, Faculty of Engineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka-shi 819-0395, Fukuoka, Japan)

Abstract

A decrease in group travel and increase in individual and family travel has led to the diversification of travel demand needs in Japan. In Japan, railways and airlines are the main competitors of personal vehicles for mid- and long-distance travel. The use of a personal vehicle can better meet diverse travel needs by offering greater flexibility; moreover, the development of motorization and the improvement of road networks have placed vehicles in a leading position among mode choices for tourism purposes. At present, Level 3 autonomous driving on expressways has become technically feasible; hence, a mode shift from public transportation to automated vehicles is anticipated because of the reduction in driving fatigue and inherent advantage in terms of greater flexibility conferred by autonomous driving. This shift could contribute to more sustainable travel patterns by optimizing route planning and reducing congestion through more efficient vehicle operations. In this study, a survey was conducted on tourism travel to Kagoshima Prefecture. The collected data were used to construct tourist behavior models, including a mid- and long-distance mode choice model that considers driving fatigue and a tourist attraction visit duration model based on a random utility model. The validity of the model is corroborated by statistical tests showing high goodness-of-fit to the observed data. The results of this model forecast a change in the modal share after the introduction of automated vehicles, with a focus on reducing driving fatigue. These predictions can contribute to the development of future transportation policies and the promotion of tourism.

Suggested Citation

  • Ruixiang Zhou & Yoshinao Oeda, 2024. "Anticipating Mode Shifts Owing to Automated Vehicles Based on a Tourist Behavior Model: Case Study on Travel to Kagoshima," Sustainability, MDPI, vol. 16(24), pages 1-23, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:24:p:11097-:d:1546569
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    References listed on IDEAS

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    1. Jonas De Vos & Patricia L. Mokhtarian & Tim Schwanen & Veronique Van Acker & Frank Witlox, 2016. "Travel mode choice and travel satisfaction: bridging the gap between decision utility and experienced utility," Transportation, Springer, vol. 43(5), pages 771-796, September.
    2. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2020. "Modeling Americans’ autonomous vehicle preferences: A focus on dynamic ride-sharing, privacy & long-distance mode choices," Technological Forecasting and Social Change, Elsevier, vol. 150(C).
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