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Decision tree method for modeling travel mode switching in a dynamic behavioral process

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  • Liang Tang
  • Chenfeng Xiong
  • Lei Zhang

Abstract

As road congestion is exacerbated in most metropolitan areas, many transportation policies and planning strategies try to nudge travelers to switch to other more sustainable modes of transportation. In order to better analyze these strategies, there is a need to accurately model travelers' mode-switching behavior. In this paper, a popular artificial intelligence approach, the decision tree (DT), is used to explore the underlying rules of travelers' switching decisions between two modes under a proposed framework of dynamic mode searching and switching. An effective and practical method for a mode-switching DT induction is proposed. A loss matrix is introduced to handle class imbalance issues. Important factors and their relative importance are analyzed through information gains and feature selections. Household Travel Survey data are used to implement and validate the proposed DT induction method. Through comparison with logit models, the improved prediction ability of the DT models is demonstrated.

Suggested Citation

  • Liang Tang & Chenfeng Xiong & Lei Zhang, 2015. "Decision tree method for modeling travel mode switching in a dynamic behavioral process," Transportation Planning and Technology, Taylor & Francis Journals, vol. 38(8), pages 833-850, December.
    • Handle: RePEc:taf:transp:v:38:y:2015:i:8:p:833-850
    DOI: 10.1080/03081060.2015.1079385
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Georges Sfeir & Filipe Rodrigues & Maya Abou-Zeid, 2021. "Gaussian Process Latent Class Choice Models," Papers 2101.12252, arXiv.org.
    3. Wang, Shenhao & Wang, Qingyi & Bailey, Nate & Zhao, Jinhua, 2021. "Deep neural networks for choice analysis: A statistical learning theory perspective," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 60-81.
    4. Ortelli, Nicola & Hillel, Tim & Pereira, Francisco C. & de Lapparent, Matthieu & Bierlaire, Michel, 2021. "Assisted specification of discrete choice models," Journal of choice modelling, Elsevier, vol. 39(C).
    5. Gutiérrez-Vargas, Álvaro A. & Meulders, Michel & Vandebroek, Martina, 2023. "Modeling preference heterogeneity using model-based decision trees," Journal of choice modelling, Elsevier, vol. 46(C).
    6. Zheng Zhu & Xiqun Chen & Chenfeng Xiong & Lei Zhang, 2018. "A mixed Bayesian network for two-dimensional decision modeling of departure time and mode choice," Transportation, Springer, vol. 45(5), pages 1499-1522, September.
    7. Jiajia Zhang & Tao Feng & Harry Timmermans & Zhengkui Lin, 2023. "Improved imputation of rule sets in class association rule modeling: application to transportation mode choice," Transportation, Springer, vol. 50(1), pages 63-106, February.
    8. Shenhao Wang & Baichuan Mo & Stephane Hess & Jinhua Zhao, 2021. "Comparing hundreds of machine learning classifiers and discrete choice models in predicting travel behavior: an empirical benchmark," Papers 2102.01130, arXiv.org.
    9. Sfeir, Georges & Abou-Zeid, Maya & Rodrigues, Filipe & Pereira, Francisco Camara & Kaysi, Isam, 2021. "Latent class choice model with a flexible class membership component: A mixture model approach," Journal of choice modelling, Elsevier, vol. 41(C).
    10. Zhang, Yu & Li, Leiming, 2022. "Research on travelers’ transportation mode choice between carsharing and private cars based on the logit dynamic evolutionary game model," Economics of Transportation, Elsevier, vol. 29(C).
    11. Izabela Kotowska & Marta Mankowska & Michal Plucinski, 2020. "The Decision Tree Approach for the Choice of Freight Transport Mode: The Shippers’ Perspective in Terms of Seaport Hinterland Connections," European Research Studies Journal, European Research Studies Journal, vol. 0(3), pages 446-459.

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