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Exploring the Individual Travel Patterns Utilizing Large-Scale Highway Transaction Dataset

Author

Listed:
  • Jianmin Jia

    (Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Mingyu Shao

    (Department of Computer Science, Shandong Jianzhu University, Jinan 250101, China)

  • Rong Cao

    (Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China
    Shandong Hi-Speed Company Limited, Jinan 250014, China)

  • Xuehui Chen

    (Shandong Hi-Speed Company Limited, Jinan 250014, China)

  • Hui Zhang

    (Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Baiying Shi

    (Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Xiaohan Wang

    (Department of Transportation Engineering, Shandong Jianzhu University, Jinan 250101, China)

Abstract

With the spread of electronic toll collection (ETC) and electronic payment, it is still a challenging issue to develop a systematic approach to investigate highway travel patterns. This paper proposed to explore spatial–temporal travel patterns to support traffic management. Travel patterns were extracted from the highway transaction dataset, which provides a wealth of individual information. Additionally, this paper constructed the analysis framework, involving individual, and temporal and spatial attributes, on the basis of the RFM (Recency, Frequency, Monetary) model. In addition to the traditional factors, the weekday trip and repeated rate were introduced in the study. Subsequently, various models, involving K-means, Fuzzy C-means and SOM (Self-organizing Map) models, were employed to investigate travel patterns. According to the performance evaluation, the SOM model presented better performance and was utilized in the final analysis. The results indicated that six groups were categorized with a significant difference. Through further investigation, we found that the random traveler occupied over 40% of the samples, while the commuting traveler and long-range freight traveler presented relatively fixed spatial and temporal patterns. The results were also meaningful for highway authority management. The discussion and implication of travel patterns to be integrated with the dynamic pricing strategy were also discussed.

Suggested Citation

  • Jianmin Jia & Mingyu Shao & Rong Cao & Xuehui Chen & Hui Zhang & Baiying Shi & Xiaohan Wang, 2022. "Exploring the Individual Travel Patterns Utilizing Large-Scale Highway Transaction Dataset," Sustainability, MDPI, vol. 14(21), pages 1-13, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:21:p:14196-:d:958759
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    References listed on IDEAS

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    1. Fu, Xin & Xu, Chengyao & Liu, Yuteng & Chen, Chi-Hua & Hwang, F.J. & Wang, Jianwei, 2022. "Spatial heterogeneity and migration characteristics of traffic congestion—A quantitative identification method based on taxi trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    2. Wen-Yu Chiang, 2017. "Discovering customer value for marketing systems: an empirical case study," International Journal of Production Research, Taylor & Francis Journals, vol. 55(17), pages 5157-5167, September.
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    Cited by:

    1. Dong, Xiaoyang & Zhang, Bin & Wang, Zhaohua, 2023. "Impact of land use on bike-sharing travel patterns: Evidence from large scale data analysis in China," Land Use Policy, Elsevier, vol. 133(C).

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