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Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data

Author

Listed:
  • Wenjing Wang

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yanyan Chen

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Haodong Sun

    (College of Metropolitan Transportation, Beijing University of Technology, Beijing 100124, China)

  • Yusen Chen

    (Department of Transport and Planning, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, The Netherlands)

Abstract

Observing and analyzing travel behavior is important, requiring understanding detailed individual trip chains. Existing studies on identifying travel modes have mainly used some travel features based on GPS and survey data from a small number of users. However, few studies have focused on evaluating the effectiveness of these models on large-scale location data. This paper proposes to use travel location data from an Internet company and travel data from transport department to identify travel modes. A multiple binary classification model based on data fusion is used to find out the relationship between travel mode and different features. Firstly, we enlisted volunteers to collect travel data and record their travel trip process using a custom-developed WeChat program. Secondly, we have developed three binary classification models to explain how different attributes can be used to model travel mode. Compared with one multi-classification model, the accuracy of our model improved significantly, with prediction accuracies of 0.839, 0.899, 0.742, 0.799, and 0.799 for walk, metro, bike, bus, and car, respectively. This suggests that the model could be applied not only in engineering practice to identify the trip chain from Internet location data but also in decision support for transportation planners.

Suggested Citation

  • Wenjing Wang & Yanyan Chen & Haodong Sun & Yusen Chen, 2021. "Multiple Binary Classification Model of Trip Chain Based on the Fusion of Internet Location Data and Transport Data," Sustainability, MDPI, vol. 13(21), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:12298-:d:674082
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    References listed on IDEAS

    as
    1. Wen-Long Shang & Yanyan Chen & Huibo Bi & Haoran Zhang & Changxi Ma & Washington Y. Ochieng, 2020. "Statistical Characteristics and Community Analysis of Urban Road Networks," Complexity, Hindawi, vol. 2020, pages 1-21, September.
    2. Bi, Huibo & Shang, Wen-Long & Chen, Yanyan & Wang, Kezhi & Yu, Qing & Sui, Yi, 2021. "GIS aided sustainable urban road management with a unifying queueing and neural network model," Applied Energy, Elsevier, vol. 291(C).
    3. Daisy, Naznin Sultana & Millward, Hugh & Liu, Lei, 2018. "Trip chaining and tour mode choice of non-workers grouped by daily activity patterns," Journal of Transport Geography, Elsevier, vol. 69(C), pages 150-162.
    4. Huang, Yuqiao & Gao, Linjie & Ni, Anning & Liu, Xiaoning, 2021. "Analysis of travel mode choice and trip chain pattern relationships based on multi-day GPS data: A case study in Shanghai, China," Journal of Transport Geography, Elsevier, vol. 93(C).
    5. Shen, Yindong & Xu, Jia & Li, Jingpeng, 2016. "A probabilistic model for vehicle scheduling based on stochastic trip times," Transportation Research Part B: Methodological, Elsevier, vol. 85(C), pages 19-31.
    6. Shang, Wen-Long & Chen, Jinyu & Bi, Huibo & Sui, Yi & Chen, Yanyan & Yu, Haitao, 2021. "Impacts of COVID-19 pandemic on user behaviors and environmental benefits of bike sharing: A big-data analysis," Applied Energy, Elsevier, vol. 285(C).
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