IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i9p1528-d1650201.html
   My bibliography  Save this article

United Prediction of Travel Modes and Purposes in Travel Chains Based on Multitask Learning Deep Neural Networks

Author

Listed:
  • Chenxi Xiao

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Zhitao Li

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Jinjun Tang

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Jeanyoung Jay Lee

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

Abstract

Predicting and analyzing travel mode choices and purposes are significant to improve urban travel mobility and transportation planning. Previous research has ignored the interconnection between travel mode choices and purposes and thus overlooked their potential contributions to predictions. Using individual travel chain data collected in South Korea, this study proposes a Multi-Task Learning Deep Neural Network (MTLDNN) framework, integrating RFM (Recency, Frequency, Monetary) to achieve a joint prediction of travel mode choices and purposes. The MTLDNN is constructed to share a common hidden layer that extracts general features from the input data, while task-specific output layers are dedicated to predicting travel modes and purposes separately. This structure allows for efficient learning of shared representations while maintaining the capacity to model task-specific relationships. RFM is then integrated to optimize the extraction of users’ behavioral features, which helps in better understanding the temporal and financial patterns of users’ travel activities. The results show that the MTLDNN demonstrates consistent input variable replacement modes and selection probabilities in generating behavioral replacement patterns. Compared to the multinomial logit model (MNL), the MTLDNN achieves lower cross-entropy loss and higher prediction accuracy. The proposed framework could enhance transportation planning, efficiency, and user satisfaction by enabling more accurate predictions.

Suggested Citation

  • Chenxi Xiao & Zhitao Li & Jinjun Tang & Jeanyoung Jay Lee, 2025. "United Prediction of Travel Modes and Purposes in Travel Chains Based on Multitask Learning Deep Neural Networks," Mathematics, MDPI, vol. 13(9), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:9:p:1528-:d:1650201
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/9/1528/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/9/1528/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patricia K. Lyon, 1984. "Time-Dependent Structural Equations Modeling: A Methodology for Analyzing the Dynamic Attitude-Behavior Relationship," Transportation Science, INFORMS, vol. 18(4), pages 395-414, November.
    2. Kenneth Train, 1980. "A Structured Logit Model of Auto Ownership and Mode Choice," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 47(2), pages 357-370.
    3. Bhat, Chandra R., 2005. "A multiple discrete-continuous extreme value model: formulation and application to discretionary time-use decisions," Transportation Research Part B: Methodological, Elsevier, vol. 39(8), pages 679-707, September.
    4. Ye, Xin & Pendyala, Ram M. & Gottardi, Giovanni, 2007. "An exploration of the relationship between mode choice and complexity of trip chaining patterns," Transportation Research Part B: Methodological, Elsevier, vol. 41(1), pages 96-113, January.
    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. Wang, Shenhao & Wang, Qingyi & Zhao, Jinhua, 2020. "Multitask learning deep neural networks to combine revealed and stated preference data," Journal of choice modelling, Elsevier, vol. 37(C).
    2. Shenhao Wang & Qingyi Wang & Jinhua Zhao, 2019. "Multitask Learning Deep Neural Networks to Combine Revealed and Stated Preference Data," Papers 1901.00227, arXiv.org, revised Aug 2019.
    3. Xuemei Fu & Zhicai Juan, 2017. "An integrated framework to jointly model decisions of activity time allocation and work-related travel," Transportation Planning and Technology, Taylor & Francis Journals, vol. 40(6), pages 689-705, August.
    4. Steven Farber & Antonio Páez & Ruben Mercado & Matthew Roorda & Catherine Morency, 2011. "A time-use investigation of shopping participation in three Canadian cities: is there evidence of social exclusion?," Transportation, Springer, vol. 38(1), pages 17-44, January.
    5. Andrea Pellegrini & Stefano Scagnolari, 2021. "The relationship between length of stay and land transportation mode in the tourism sector: A discrete–continuous framework applied to Swiss data," Tourism Economics, , vol. 27(1), pages 243-259, February.
    6. Hakim Hammadou & Claire Papaix, 2015. "Policy packages for modal shift and CO2 reduction in Lille, France," Working Papers 1501, Chaire Economie du climat.
    7. Ruifen Sun & Min Li & Qunqi Wu, 2018. "Research on Commuting Travel Mode Choice of Car Owners Considering Return Trip Containing Activities," Sustainability, MDPI, vol. 10(10), pages 1-12, September.
    8. Jara-Díaz, Sergio & Rosales-Salas, Jorge, 2017. "Beyond transport time: A review of time use modeling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 97(C), pages 209-230.
    9. Ye, Xin & Garikapati, Venu M. & You, Daehyun & Pendyala, Ram M., 2017. "A practical method to test the validity of the standard Gumbel distribution in logit-based multinomial choice models of travel behavior," Transportation Research Part B: Methodological, Elsevier, vol. 106(C), pages 173-192.
    10. Ozonder, Gozde & Miller, Eric J., 2021. "Longitudinal investigation of skeletal activity episode timing decisions – A copula approach," Journal of choice modelling, Elsevier, vol. 40(C).
    11. Vytautas Dumbliauskas & Vytautas Grigonis, 2020. "An Empirical Activity Sequence Approach for Travel Behavior Analysis in Vilnius City," Sustainability, MDPI, vol. 12(2), pages 1-22, January.
    12. Ta, Na & Zhao, Ying & Chai, Yanwei, 2016. "Built environment, peak hours and route choice efficiency: An investigation of commuting efficiency using GPS data," Journal of Transport Geography, Elsevier, vol. 57(C), pages 161-170.
    13. Longden, Thomas, 2016. "The Regularity and Irregularity of Travel: an Analysis of the Consistency of Travel Times Associated with Subsistence, Maintenance and Discretionary Activities," ET: Economic Theory 243150, Fondazione Eni Enrico Mattei (FEEM).
    14. Chih-Wen Yang & Cheng-Lung (Richard) Wu & Jin-Long Lu, 2021. "Exploring the interdependency and determinants of tourism participation, expenditure, and duration: An analysis of Taiwanese citizens traveling abroad," Tourism Economics, , vol. 27(4), pages 649-669, June.
    15. Schuster, Monica & Vranken, Liesbet & Maertens, Miet, 2017. "You Can(’t) Always Get the Job You Want: Stated versus Revealed Employment Preferences in the Peruvian Agro-industry," Working Papers 254076, Katholieke Universiteit Leuven, Centre for Agricultural and Food Economics.
    16. Kevin Maréchal, 2018. "Recasting the understanding of habits for behaviour-oriented policies in transportation," ULB Institutional Repository 2013/270475, ULB -- Universite Libre de Bruxelles.
    17. Marcela Munizaga & Sergio Jara-Díaz & Paulina Greeven & Chandra Bhat, 2008. "Econometric Calibration of the Joint Time Assignment--Mode Choice Model," Transportation Science, INFORMS, vol. 42(2), pages 208-219, May.
    18. Kidokoro, Yukihiro, 2016. "A micro foundation for discrete choice models with multiple categories of goods," Journal of choice modelling, Elsevier, vol. 19(C), pages 54-72.
    19. Richards, Timothy J. & Mancino, Lisa, 2011. "Demand for Food-Away-From-Home: A Multiple Discrete/Continuous Extreme Value Model," 2012 AAEA/EAAE Food Environment Symposium 123390, Agricultural and Applied Economics Association.
    20. Donna, Javier D., 2018. "Measuring Long-Run Price Elasticities in Urban Travel Demand," MPRA Paper 90059, University Library of Munich, Germany.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:jmathe:v:13:y:2025:i:9:p:1528-:d:1650201. 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.