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Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China

Author

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  • Mingwei He

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

  • Jianbo Li

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

  • Zhuangbin Shi

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

  • Yang Liu

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

  • Chunyan Shuai

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

  • Jie Liu

    (Faculty of Transportation Engineering, Kunming University of Science and Technology, Jingming South Road 727, Kunming 650500, China)

Abstract

Examining how travel distance is associated with travel mode choice is essential for understanding traveler travel patterns and the potential mechanisms of behavioral changes. Although existing studies have explored the effect of travel distance on travel mode choice, most overlook their non-linear relationship and the heterogeneity between groups. In this study, the correlation between travel distance and travel mode choice is explored by applying the random forest model based on resident travel survey data in Guiyang, China. The results show that travel distance is far more important than other determinants for understanding the mechanism of travel mode choice. Travel distance contributes to 42.28% of explanation power for predicting travel mode choice and even 63.24% for walking. Significant nonlinear associations and threshold effects are found between travel distance and travel mode choice, and such nonlinear associations vary significantly across different socioeconomic groups. Policymakers are recommended to understand the group heterogeneity of travel mode choice behavior and to make targeted interventions for different groups with different travel distances. These results can provide beneficial guidance for optimizing the spatial layout of transportation infrastructure and improving the operational efficiency of low-carbon transportation systems.

Suggested Citation

  • Mingwei He & Jianbo Li & Zhuangbin Shi & Yang Liu & Chunyan Shuai & Jie Liu, 2022. "Exploring the Nonlinear and Threshold Effects of Travel Distance on the Travel Mode Choice across Different Groups: An Empirical Study of Guiyang, China," IJERPH, MDPI, vol. 19(23), pages 1-23, November.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16045-:d:989619
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    References listed on IDEAS

    as
    1. Ding, Chuan & Cao, Xinyu & Wang, Yunpeng, 2018. "Synergistic effects of the built environment and commuting programs on commute mode choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 104-118.
    2. Scheiner, Joachim, 2010. "Interrelations between travel mode choice and trip distance: trends in Germany 1976–2002," Journal of Transport Geography, Elsevier, vol. 18(1), pages 75-84.
    3. Jones, Tim & Harms, Lucas & Heinen, Eva, 2016. "Motives, perceptions and experiences of electric bicycle owners and implications for health, wellbeing and mobility," Journal of Transport Geography, Elsevier, vol. 53(C), pages 41-49.
    4. Khandker Habib, 2015. "An investigation on mode choice and travel distance demand of older people in the National Capital Region (NCR) of Canada: application of a utility theoretic joint econometric model," Transportation, Springer, vol. 42(1), pages 143-161, January.
    5. Rahul, T.M. & Verma, Ashish, 2014. "A study of acceptable trip distances using walking and cycling in Bangalore," Journal of Transport Geography, Elsevier, vol. 38(C), pages 106-113.
    6. Sungyop Kim & Gudmundur Ulfarsson, 2008. "Curbing automobile use for sustainable transportation: analysis of mode choice on short home-based trips," Transportation, Springer, vol. 35(6), pages 723-737, November.
    7. Zhan, Guangjun & Yan, Xuedong & Zhu, Shanjiang & Wang, Yun, 2016. "Using hierarchical tree-based regression model to examine university student travel frequency and mode choice patterns in China," Transport Policy, Elsevier, vol. 45(C), pages 55-65.
    8. Ding, Chuan & Wang, Donggen & Liu, Chao & Zhang, Yi & Yang, Jiawen, 2017. "Exploring the influence of built environment on travel mode choice considering the mediating effects of car ownership and travel distance," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 65-80.
    9. Lee, Jaehyung & Lee, Euntak & Yun, Jaewoong & Chung, Jin-Hyuk & Kim, Jinhee, 2021. "Latent heterogeneity in autonomous driving preferences and in-vehicle activities by travel distance," Journal of Transport Geography, Elsevier, vol. 94(C).
    10. De Vos, Jonas & Cheng, Long & Kamruzzaman, Md. & Witlox, Frank, 2021. "The indirect effect of the built environment on travel mode choice: A focus on recent movers," Journal of Transport Geography, Elsevier, vol. 91(C).
    11. Rahul, T.M. & Verma, Ashish, 2013. "Economic impact of non-motorized transportation in Indian cities," Research in Transportation Economics, Elsevier, vol. 38(1), pages 22-34.
    12. Caigang, Zhuang & Shaoying, Li & Zhangzhi, Tan & Feng, Gao & Zhifeng, Wu, 2022. "Nonlinear and threshold effects of traffic condition and built environment on dockless bike sharing at street level," Journal of Transport Geography, Elsevier, vol. 102(C).
    13. Yang, Jiawen & Cao, Jason & Zhou, Yufei, 2021. "Elaborating non-linear associations and synergies of subway access and land uses with urban vitality in Shenzhen," Transportation Research Part A: Policy and Practice, Elsevier, vol. 144(C), pages 74-88.
    14. Ton, Danique & Duives, Dorine C. & Cats, Oded & Hoogendoorn-Lanser, Sascha & Hoogendoorn, Serge P., 2019. "Cycling or walking? Determinants of mode choice in the Netherlands," Transportation Research Part A: Policy and Practice, Elsevier, vol. 123(C), pages 7-23.
    15. Nasrin, Sharmin & Bunker, Jonathan, 2021. "Analyzing significant variables for choosing different modes by female travelers," Transport Policy, Elsevier, vol. 114(C), pages 312-329.
    16. He, Mingwei & He, Chengfeng & Shi, Zhuangbin & He, Min, 2022. "Spatiotemporal heterogeneous effects of socio-demographic and built environment on private car usage: An empirical study of Kunming, China," Journal of Transport Geography, Elsevier, vol. 101(C).
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    1. Hudyeron Rocha & António Lobo & José Pedro Tavares & Sara Ferreira, 2023. "Exploring Modal Choices for Sustainable Urban Mobility: Insights from the Porto Metropolitan Area in Portugal," Sustainability, MDPI, vol. 15(20), pages 1-20, October.
    2. Long, Yi & Ao, Yibin & Li, Haimei & Bahmani, Homa & Li, Mingyang, 2024. "Non-linear effects of children's daily travel distance on their travel mode choice considering different destinations," Journal of Transport Geography, Elsevier, vol. 118(C).

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