IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i2p738-d1569948.html
   My bibliography  Save this article

A New Breakthrough in Travel Behavior Modeling Using Deep Learning: A High-Accuracy Prediction Method Based on a CNN

Author

Listed:
  • Xuli Wen

    (School of Civil and Transportation Engineering, Southeast University Chengxian College, Nanjing 210088, China)

  • Xin Chen

    (School of Management Science and Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China)

Abstract

Accurately predicting travel mode choice is crucial for effective transportation planning and policymaking. While traditional approaches rely on discrete choice models, recent advancements in machine learning offer promising alternatives. This study proposes a novel convolutional neural network (CNN) architecture optimized using orthogonal experimental design to predict travel mode choice. Using the SwissMetro dataset, which represents a specific intercity travel context in Switzerland, we evaluate our CNN model’s performance and compare it with traditional machine learning algorithms and previous studies. The key innovations of our study include: (1) an optimized CNN architecture designed to capture complex patterns in travel behavior data, and (2) the application of orthogonal experimental design to efficiently identify optimal hyperparameter settings. The results demonstrate that the proposed CNN model significantly outperforms logit models, support vector machines, random forests, gradient boosting, and even state-of-the-art techniques combining discrete choice models with neural networks. The optimized CNN achieves a remarkable 95% accuracy, surpassing the best-performing benchmarks by 14–25%. The proposed methodology offers a powerful tool for understanding travel behavior, improving travel demand forecasting, and informing transportation planning decisions. Our findings contribute to the growing body of literature on machine learning applications in transportation and pave the way for further advancements in this field.

Suggested Citation

  • Xuli Wen & Xin Chen, 2025. "A New Breakthrough in Travel Behavior Modeling Using Deep Learning: A High-Accuracy Prediction Method Based on a CNN," Sustainability, MDPI, vol. 17(2), pages 1-14, January.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:2:p:738-:d:1569948
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/2/738/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/2/738/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, Enero.
    2. Arkoudi, Ioanna & Krueger, Rico & Azevedo, Carlos Lima & Pereira, Francisco C., 2023. "Combining discrete choice models and neural networks through embeddings: Formulation, interpretability and performance," Transportation Research Part B: Methodological, Elsevier, vol. 175(C).
    3. 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.
    4. Hillel, Tim & Bierlaire, Michel & Elshafie, Mohammed Z.E.B. & Jin, Ying, 2021. "A systematic review of machine learning classification methodologies for modelling passenger mode choice," Journal of choice modelling, Elsevier, vol. 38(C).
    5. Han, Yafei & Pereira, Francisco Camara & Ben-Akiva, Moshe & Zegras, Christopher, 2022. "A neural-embedded discrete choice model: Learning taste representation with strengthened interpretability," Transportation Research Part B: Methodological, Elsevier, vol. 163(C), pages 166-186.
    6. Mabit, Stefan L., 2017. "Empirical analyses of a choice model that captures ordering among attribute values," Journal of choice modelling, Elsevier, vol. 25(C), pages 3-10.
    Full references (including those not matched with items on IDEAS)

    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. Kim, Eui-Jin & Bansal, Prateek, 2024. "A new flexible and partially monotonic discrete choice model," Transportation Research Part B: Methodological, Elsevier, vol. 183(C).
    2. Daniel F. Villarraga & Ricardo A. Daziano, 2025. "Designing Graph Convolutional Neural Networks for Discrete Choice with Network Effects," Papers 2503.09786, arXiv.org.
    3. Ali, Azam & Kalatian, Arash & Choudhury, Charisma F., 2023. "Comparing and contrasting choice model and machine learning techniques in the context of vehicle ownership decisions," Transportation Research Part A: Policy and Practice, Elsevier, vol. 173(C).
    4. Tanmay Ghosh & Nithin Nagaraj, 2024. "Evaluating the Determinants of Mode Choice Using Statistical and Machine Learning Techniques in the Indian Megacity of Bengaluru," Papers 2401.13977, arXiv.org.
    5. Kim, Kyungah & Kim, Jinseok & Park, Subin & Lee, Jongsu & Kim, Junghun, 2025. "A machine learning technique embedded reference-dependent choice model for explanatory power improvement: Shifting of reference point as a key factor in vehicle purchase decision-making," Transportation Research Part B: Methodological, Elsevier, vol. 191(C).
    6. Gu, Yu & Chen, Anthony & Kitthamkesorn, Songyot & Jang, Sunghoon, 2024. "Alternate closed-form weibit-based model for assessing travel choice with an oddball alternative," Transportation Research Part B: Methodological, Elsevier, vol. 179(C).
    7. Niousha Bagheri & Milad Ghasri & Michael Barlow, 2025. "RUM-NN: A Neural Network Model Compatible with Random Utility Maximisation for Discrete Choice Setups," Papers 2501.05221, arXiv.org.
    8. Lahoz, Lorena Torres & Pereira, Francisco Camara & Sfeir, Georges & Arkoudi, Ioanna & Monteiro, Mayara Moraes & Azevedo, Carlos Lima, 2023. "Attitudes and Latent Class Choice Models using Machine Learning," Journal of choice modelling, Elsevier, vol. 49(C).
    9. Dubey, Subodh & Cats, Oded & Hoogendoorn, Serge & Bansal, Prateek, 2022. "A multinomial probit model with Choquet integral and attribute cut-offs," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 140-163.
    10. 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).
    11. Zhifeng Gao & Ted C. Schroeder, 2009. "Consumer responses to new food quality information: are some consumers more sensitive than others?," Agricultural Economics, International Association of Agricultural Economists, vol. 40(3), pages 339-346, May.
    12. Cheng, Leilei & Yin, Changbin & Chien, Hsiaoping, 2015. "Demand for milk quantity and safety in urban China: evidence from Beijing and Harbin," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 59(2), April.
    13. Wen, Chieh-Hua & Huang, Chia-Jung & Fu, Chiang, 2020. "Incorporating continuous representation of preferences for flight departure times into stated itinerary choice modeling," Transport Policy, Elsevier, vol. 98(C), pages 10-20.
    14. Johannes Buggle & Thierry Mayer & Seyhun Orcan Sakalli & Mathias Thoenig, 2023. "The Refugee’s Dilemma: Evidence from Jewish Migration out of Nazi Germany," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 138(2), pages 1273-1345.
    15. Christelis, Dimitris & Dobrescu, Loretti I. & Motta, Alberto, 2020. "Early life conditions and financial risk-taking in older age," The Journal of the Economics of Ageing, Elsevier, vol. 17(C).
    16. Ortega, David L. & Wang, H. Holly & Wu, Laping & Hong, Soo Jeong, 2015. "Retail channel and consumer demand for food quality in China," China Economic Review, Elsevier, vol. 36(C), pages 359-366.
    17. Tina Birgitte Hansen & Jes Sanddal Lindholt & Axel Diederichsen & Rikke Søgaard, 2019. "Do Non-participants at Screening have a Different Threshold for an Acceptable Benefit–Harm Ratio than Participants? Results of a Discrete Choice Experiment," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 12(5), pages 491-501, October.
    18. Doyle, Orla & Fidrmuc, Jan, 2006. "Who favors enlargement?: Determinants of support for EU membership in the candidate countries' referenda," European Journal of Political Economy, Elsevier, vol. 22(2), pages 520-543, June.
    19. Tovar, Jorge, 2012. "Consumers’ Welfare and Trade Liberalization: Evidence from the Car Industry in Colombia," World Development, Elsevier, vol. 40(4), pages 808-820.
    20. Pereira, Pedro & Ribeiro, Tiago, 2011. "The impact on broadband access to the Internet of the dual ownership of telephone and cable networks," International Journal of Industrial Organization, Elsevier, vol. 29(2), pages 283-293, March.

    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:jsusta:v:17:y:2025:i:2:p:738-:d:1569948. 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.