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Research on the Route Choice Behavior of Urban Freight Vehicles Based on GPS Data

Author

Listed:
  • Lili Zheng

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Tian Gao

    (Automotive Engineering Research Institute, BYD Automobile Industry Co., Ltd., No. 3009 BYD Road, Shenzhen 518118, China)

  • Lin Meng

    (Jilin Provincial Transportation Administration, No. 2518 Jie-fang Road, Changchun 130021, China)

  • Tongqiang Ding

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

  • Wenhao Chen

    (School of Transportation, Jilin University, No. 5988 Renmin Street, Changchun 130022, China)

Abstract

In order to provide urban freight vehicles with navigation routes that better align with their travel preferences, it is necessary to analyze the patterns and characteristics of their route choices. This paper focuses on freight vehicles traveling within the city and examines their route selection behavior. Through an analysis of the GPS data collected from freight truck journeys in Changchun, China, this study outlines the characteristics of freight vehicle travel within the city. Variables that may influence their route selection behavior are defined as feature factors. The study employs the DBSCAN algorithm to identify the hotspots in origin–destination pairs for freight truck travel in Changchun. It also utilizes Breadth First Search Link Elimination to generate a set of route choices and constructs route selection behavior models based on Multinomial Logit and Path Size Logit. Based on the research findings, during navigation within the city road network, these vehicles exhibit a preference for side roads, a tendency to favor right turns at intersections, and a propensity to choose routes with lower duplication compared to alternative options. The study’s conclusions offer theoretical support for guiding urban freight vehicle routes and planning and managing freight traffic within the city.

Suggested Citation

  • Lili Zheng & Tian Gao & Lin Meng & Tongqiang Ding & Wenhao Chen, 2024. "Research on the Route Choice Behavior of Urban Freight Vehicles Based on GPS Data," Mathematics, MDPI, vol. 12(2), pages 1-17, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:2:p:291-:d:1320209
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    References listed on IDEAS

    as
    1. Theo Arentze & Tao Feng & Harry Timmermans & Jops Robroeks, 2012. "Context-dependent influence of road attributes and pricing policies on route choice behavior of truck drivers: results of a conjoint choice experiment," Transportation, Springer, vol. 39(6), pages 1173-1188, November.
    2. Frejinger, E. & Bierlaire, M. & Ben-Akiva, M., 2009. "Sampling of alternatives for route choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 43(10), pages 984-994, December.
    3. Broach, Joseph & Dill, Jennifer & Gliebe, John, 2012. "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(10), pages 1730-1740.
    4. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    5. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
    6. Hess, Stephane & Quddus, Mohammed & Rieser-Schüssler, Nadine & Daly, Andrew, 2015. "Developing advanced route choice models for heavy goods vehicles using GPS data," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 77(C), pages 29-44.
    7. Wen, Chieh-Hua & Koppelman, Frank S., 2001. "The generalized nested logit model," Transportation Research Part B: Methodological, Elsevier, vol. 35(7), pages 627-641, August.
    8. Utpal Vasavada, 1988. "Kmenta, Jan. Elements of Econometrics, 2nd ed. New York: Macmillan Publishing Co., 1986, xii + 786 pp., $26.50," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 70(1), pages 210-211.
    9. Shlomo Bekhor & Moshe Ben-Akiva & M. Ramming, 2006. "Evaluation of choice set generation algorithms for route choice models," Annals of Operations Research, Springer, vol. 144(1), pages 235-247, April.
    Full references (including those not matched with items on IDEAS)

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