IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i23p16045-d989619.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/23/16045/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/23/16045/
    Download Restriction: no
    ---><---

    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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. Nasrin, Sharmin & Bunker, Jonathan, 2021. "Analyzing significant variables for choosing different modes by female travelers," Transport Policy, Elsevier, vol. 114(C), pages 312-329.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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).
    12. 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.
    13. 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).
    14. 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).
    15. 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.
    16. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Eldeeb, Gamal & Mohamed, Moataz & Páez, Antonio, 2021. "Built for active travel? Investigating the contextual effects of the built environment on transportation mode choice," Journal of Transport Geography, Elsevier, vol. 96(C).
    2. Faghih-Imani, Ahmadreza & Eluru, Naveen, 2015. "Analysing bicycle-sharing system user destination choice preferences: Chicago’s Divvy system," Journal of Transport Geography, Elsevier, vol. 44(C), pages 53-64.
    3. Altieri, Marcelo & Silva, Cecília & Terabe, Shintaro, 2020. "Give public transit a chance: A comparative analysis of competitive travel time in public transit modal share," Journal of Transport Geography, Elsevier, vol. 87(C).
    4. Aghaabbasi, Mahdi & Shekari, Zohreh Asadi & Shah, Muhammad Zaly & Olakunle, Oloruntobi & Armaghani, Danial Jahed & Moeinaddini, Mehdi, 2020. "Predicting the use frequency of ride-sourcing by off-campus university students through random forest and Bayesian network techniques," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 262-281.
    5. Ha, Jaehyun & Lee, Sugie & Ko, Joonho, 2020. "Unraveling the impact of travel time, cost, and transit burdens on commute mode choice for different income and age groups," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 147-166.
    6. Ton, Danique & Duives, Dorine, 2021. "Understanding long-term changes in commuter mode use of a pilot featuring free e-bike trials," Transport Policy, Elsevier, vol. 105(C), pages 134-144.
    7. Verma, Meghna & Rahul, T.M. & Reddy, Peesari Vamshidhar & Verma, Ashish, 2016. "The factors influencing bicycling in the Bangalore city," Transportation Research Part A: Policy and Practice, Elsevier, vol. 89(C), pages 29-40.
    8. Shen, Tonggaochuan & Cheng, Long & Yang, Yongjiang & Deng, Jialin & Jin, Tanhua & Cao, Mengqiu, 2023. "Do residents living in transit-oriented development station catchment areas travel more sustainably? The impacts of life events," LSE Research Online Documents on Economics 118813, London School of Economics and Political Science, LSE Library.
    9. Charles Raux & Ayana Lamatkhanova & Lény Grassot, 2021. "Does the built environment shape commuting? The case of Lyon (France)," Post-Print halshs-03010833, HAL.
    10. Duan, Sophia Xiaoxia & Tay, Richard & Molla, Alemayehu & Deng, Hepu, 2022. "Predicting Mobility as a Service (MaaS) use for different trip categories: An artificial neural network analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 166(C), pages 135-149.
    11. Dėdelė, Audrius & Miškinytė, Auksė & Andrušaitytė, Sandra & Nemaniūtė-Gužienė, Jolanta, 2020. "Dependence between travel distance, individual socioeconomic and health-related characteristics, and the choice of the travel mode: a cross-sectional study for Kaunas, Lithuania," Journal of Transport Geography, Elsevier, vol. 86(C).
    12. Obregón-Biosca, Saúl A., 2022. "Choice of transport in urban and periurban zones in metropolitan area," Journal of Transport Geography, Elsevier, vol. 100(C).
    13. Charles Raux & Ayana Lamatkhanova & Lény Grassot, 2020. "Does the built environment shape commuting? The case of Lyon (France)," Working Papers halshs-03010833, HAL.
    14. Ji, Shujuan & Wang, Xin & Lyu, Tao & Liu, Xiaojie & Wang, Yuanqing & Heinen, Eva & Sun, Zhenwei, 2022. "Understanding cycling distance according to the prediction of the XGBoost and the interpretation of SHAP: A non-linear and interaction effect analysis," Journal of Transport Geography, Elsevier, vol. 103(C).
    15. Verma, Meghna & Rahul, T.M. & Vinayak, Pragun & Verma, Ashish, 2018. "Influence of childhood and adulthood attitudinal perceptions on bicycle usage in the Bangalore city," Journal of Transport Geography, Elsevier, vol. 72(C), pages 94-105.
    16. Gupta, Akshay & Bivina, G.R. & Parida, Manoranjan, 2022. "Does neighborhood design matter for walk access to metro stations? An integrated SEM-Hybrid discrete mode choice approach," Transport Policy, Elsevier, vol. 121(C), pages 61-77.
    17. Ding, Chuan & Cao, Xinyu & Yu, Bin & Ju, Yang, 2021. "Non-linear associations between zonal built environment attributes and transit commuting mode choice accounting for spatial heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 22-35.
    18. Rahul, T.M. & Verma, Ashish, 2017. "The influence of stratification by motor-vehicle ownership on the impact of built environment factors in Indian cities," Journal of Transport Geography, Elsevier, vol. 58(C), pages 40-51.
    19. Liang Guo & Shuo Yang & Yuqing Peng & Man Yuan, 2023. "Examining the Nonlinear Effects of Residential and Workplace-built Environments on Active Travel in Short-Distance: A Random Forest Approach," IJERPH, MDPI, vol. 20(3), pages 1-21, January.
    20. Ashik, F.R. & Sreezon, A.I.Z. & Rahman, M.H. & Zafri, N.M. & Labib, S.M., 2024. "Built environment influences commute mode choice in a global south megacity context: Insights from explainable machine learning approach," Journal of Transport Geography, Elsevier, vol. 116(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:23:p:16045-:d:989619. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.