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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

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    File URL: http://nexus.umn.edu/Papers/Multiday.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    multiday travel behavior; day-to-day modeling; route choice behavior; GPS data; neural networks;
    All these 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

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