IDEAS home Printed from https://ideas.repec.org/p/nex/wpaper/multiday.html

Analyzing Multiday Route Choice Behavior using GPS Data

Author

Listed:
  • Wenyun Tang
  • Lin Cheng

    (Nexus (Networks, Economics, and Urban Systems) Research Group, Department of Civil Engineering, University of Minnesota)

Abstract

Understanding variability in daily behavior is one of the most important missions in travel behavior modeling. In traditional method, in order to find the differences, respondents were asked to list the used multiday paths. The quality of results is sensitive to the accuracy of respondents’ memories. However, few empirical studies of revealed route characteristics, chosen by the travelers day-to-day, have been reported in the literature. In this study, accurate Global Position Systems (GPS) and Geographic Information System (GIS) data were employed to reveal multiday routes people used, to study multiday route choice behavior for the same origin-destination (OD) trips. Travelers are classified into three kinds based on their route types. A two-stage route choice process is proposed. After analyzing the characteristics of different types of travelers, a neural network was adopted to classify travelers and model route choice behavior. An empirical study using GPS data collected in Minneapolis-St. Paul metropolitan area was carried out in the following part. It finds that most travelers follow the same route during commute trips on successive days. The results indicate that neural network framework can classify travelers and model route choice well.

Suggested Citation

  • Wenyun Tang & Lin Cheng, 2015. "Analyzing Multiday Route Choice Behavior using GPS Data," Working Papers 000135, University of Minnesota: Nexus Research Group.
    • Handle: RePEc:nex:wpaper:multiday
    as

    Download full text from publisher

    File URL: http://nexus.umn.edu/Papers/Multiday.pdf
    File Function: First version, 2015
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shanjiang Zhu & David Levinson, 2015. "Do People Use the Shortest Path? An Empirical Test of Wardrop’s First Principle," PLOS ONE, Public Library of Science, vol. 10(8), pages 1-18, August.
    2. Carrion, Carlos & Levinson, David, 2012. "Value of travel time reliability: A review of current evidence," Transportation Research Part A: Policy and Practice, Elsevier, vol. 46(4), pages 720-741.
    3. Yang, Hai & Kitamura, Ryuichi & Jovanis, Paul P. & Vaughn, Kenneth M. & Abdel-aty, Mohammed A. & Reddy, Prasuna Dvg, 1993. "Exploration Of Driver Route Choice With Advanced Traveler Information Using Neural Network Concepts," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt53d2t6df, Institute of Transportation Studies, UC Berkeley.
    4. 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.
    5. Khattak, Asad J. & De Palma, André, 1997. "The impact of adverse weather conditions on the propensity to change travel decisions: A survey of Brussels commuters," Transportation Research Part A: Policy and Practice, Elsevier, vol. 31(3), pages 181-203, May.
    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. Mengying Cui & David Levinson, 2021. "Shortest paths, travel costs, and traffic," Environment and Planning B, , vol. 48(4), pages 828-844, May.
    2. Bardal, Kjersti Granås & Mathisen, Terje Andreas, 2015. "Winter problems on mountain passes – Implications for cost-benefit analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 74(C), pages 59-72.
    3. Li, Dawei & Feng, Siqi & Song, Yuchen & Lai, Xinjun & Bekhor, Shlomo, 2023. "Asymmetric closed-form route choice models: Formulations and comparative applications," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    4. Manley, Ed & Cheng, Tao, 2018. "Exploring the role of spatial cognition in predicting urban traffic flow through agent-based modelling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 109(C), pages 14-23.
    5. Papinski, Dominik & Scott, Darren M., 2011. "A GIS-based toolkit for route choice analysis," Journal of Transport Geography, Elsevier, vol. 19(3), pages 434-442.
    6. Rasmussen, Thomas Kjær & Duncan, Lawrence Christopher & Watling, David Paul & Nielsen, Otto Anker, 2024. "Local detouredness: A new phenomenon for modelling route choice and traffic assignment," Transportation Research Part B: Methodological, Elsevier, vol. 190(C).
    7. Wang, Zhaohan & Ramezani, Mohsen & Levinson, David, 2024. "How mandatory are ‘Mandatory’ lane changes? An analytical and experimental study on the costs of missing freeway exits," Transportation Research Part B: Methodological, Elsevier, vol. 186(C).
    8. repec:plo:pone00:0196997 is not listed on IDEAS
    9. Hsueh, Chieh & Lin, Jen-Jia, 2023. "Influential factors of the route choices of scooter riders: A GPS-based data study," Journal of Transport Geography, Elsevier, vol. 113(C).
    10. Xuan Di & Henry X. Liu & Shanjiang Zhu & David M. Levinson, 2017. "Indifference bands for boundedly rational route switching," Transportation, Springer, vol. 44(5), pages 1169-1194, September.
    11. Wenyun Tang & David Levinson, 2014. "An empirical study of the deviation between actual and shortest travel time paths," Working Papers 000125, University of Minnesota: Nexus Research Group.
    12. Bogyrbayeva, Aigerim & Kwon, Changhyun, 2021. "Pessimistic evasive flow capturing problems," European Journal of Operational Research, Elsevier, vol. 293(1), pages 133-148.
    13. Janson, Michael & Levinson, David, 2014. "HOT or not," Research in Transportation Economics, Elsevier, vol. 44(C), pages 21-32.
    14. Jia Guo & Yusak Susilo & Constantinos Antoniou & Anna Pernestål Brenden, 2020. "Influence of Individual Perceptions on the Decision to Adopt Automated Bus Services," Sustainability, MDPI, vol. 12(16), pages 1-13, August.
    15. Ashley R. Coles & Kyle E. Walker, 2021. "Assessing motorist behavior during flash floods in Tucson, Arizona," Transportation, Springer, vol. 48(6), pages 3037-3057, December.
    16. Nijkamp, Peter & Reggiani, Aura & Tsang, Wai Fai, 2004. "Comparative modelling of interregional transport flows: Applications to multimodal European freight transport," European Journal of Operational Research, Elsevier, vol. 155(3), pages 584-602, June.
    17. Li, Hao & Gao, Kun & Tu, Huizhao, 2017. "Variations in mode-specific valuations of travel time reliability and in-vehicle crowding: Implications for demand estimation," Transportation Research Part A: Policy and Practice, Elsevier, vol. 103(C), pages 250-263.
    18. Cui, Jingshi & Wu, Jiaman & Wu, Chenye & Moura, Scott, 2023. "Electric vehicles embedded virtual power plants dispatch mechanism design considering charging efficiencies," Applied Energy, Elsevier, vol. 352(C).
    19. Katrien Ramaekers & Sofie Reumers & Geert Wets & Mario Cools, 2013. "Modelling Route Choice Decisions of Car Travellers Using Combined GPS and Diary Data," Networks and Spatial Economics, Springer, vol. 13(3), pages 351-372, September.
    20. Tao, Xuezong & Zhu, Lichao, 2020. "Meta-analysis of value of time in freight transportation: A comprehensive review based on discrete choice models," Transportation Research Part A: Policy and Practice, Elsevier, vol. 138(C), pages 213-233.
    21. Hongcheng Gan & Yang Bai, 2014. "The effect of travel time variability on route choice decision: a generalized linear mixed model based analysis," Transportation, Springer, vol. 41(2), pages 339-350, March.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • R41 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Transportation: Demand, Supply, and Congestion; Travel Time; Safety and Accidents; Transportation Noise
    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:nex:wpaper:multiday. 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: David Levinson (email available below). General contact details of provider: https://edirc.repec.org/data/nexmnus.html .

    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.