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Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making

Author

Listed:
  • Rui Zhao

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Linchuan Yang

    (Department of Urban and Rural Planning, School of Architecture and Design, Southwest Jiaotong University, Chengdu 611756, China)

  • Xinrong Liang

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Yuanyuan Guo

    (Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong, China)

  • Yi Lu

    (Department of Architecture and Civil Engineering, City University of Hong Kong, Hong Kong, China)

  • Yixuan Zhang

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

  • Xinyun Ren

    (Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China)

Abstract

Transit offers stop-to-stop services rather than door-to-door services. The trip from a transit hub to the final destination is often entitled as the “last-mile” trip. This study innovatively proposes a hybrid approach by combining the data mining technique and multiple attribute decision making to identify the optimal travel mode for last-mile, in which the data mining technique is applied in order to objectively determine the weights. Four last-mile travel modes, including walking, bike-sharing, community bus, and on-demand ride-sharing service, are ranked based upon three evaluation criteria: travel time, monetary cost, and environmental performance. The selection of last-mile trip modes in Chengdu, China, is taken as a typical case example, to demonstrate the application of the proposed approach. Results show that the optimal travel mode highly varies by the distance of the “last-mile” and that bike-sharing serves as the optimal travel mode if the last-mile distance is no more than 3 km, whilst the community bus becomes the optimal mode if the distance equals 4 and 5 km. It is expected that this study offers an evidence-based approach to help select the reasonable last-mile travel mode and provides insights into developing a sustainable urban transport system.

Suggested Citation

  • Rui Zhao & Linchuan Yang & Xinrong Liang & Yuanyuan Guo & Yi Lu & Yixuan Zhang & Xinyun Ren, 2019. "Last-Mile Travel Mode Choice: Data-Mining Hybrid with Multiple Attribute Decision Making," Sustainability, MDPI, vol. 11(23), pages 1-15, November.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:23:p:6733-:d:291537
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    3. Guo, Yuanyuan & He, Sylvia Y., 2021. "The role of objective and perceived built environments in affecting dockless bike-sharing as a feeder mode choice of metro commuting," Transportation Research Part A: Policy and Practice, Elsevier, vol. 149(C), pages 377-396.
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    5. Yongqing Xiong & Shufeng Qin, 2021. "Differences in the effects of China’s new energy vehicle industry policies on market growth from the perspective of policy mix," Energy & Environment, , vol. 32(3), pages 542-561, May.
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