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A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data

Author

Listed:
  • Zhenbo Lu

    (Intelligent Transportation System Research Center, Southeast University, Nanjing 210000, China)

  • Zhen Long

    (Intelligent Transportation System Research Center, Southeast University, Nanjing 210000, China)

  • Jingxin Xia

    (Intelligent Transportation System Research Center, Southeast University, Nanjing 210000, China)

  • Chengchuan An

    (Intelligent Transportation System Research Center, Southeast University, Nanjing 210000, China)

Abstract

Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents’ travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.

Suggested Citation

  • Zhenbo Lu & Zhen Long & Jingxin Xia & Chengchuan An, 2019. "A Random Forest Model for Travel Mode Identification Based on Mobile Phone Signaling Data," Sustainability, MDPI, vol. 11(21), pages 1-21, October.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:21:p:5950-:d:280356
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    References listed on IDEAS

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    1. Muhammad Shafique & Eiji Hato, 2015. "Use of acceleration data for transportation mode prediction," Transportation, Springer, vol. 42(1), pages 163-188, January.
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    Cited by:

    1. Xiaofeng Lou & Changhai Peng, 2022. "Planning of a comprehensive transportation system in Ma’anshan based on mobile phone signaling data," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(7), pages 9380-9406, July.
    2. Xiaoyu Cai & Yihan Zhang & Xin Zhang & Bo Peng, 2023. "Travel Characteristics Identification Method for Expressway Passenger Cars Based on Electronic Toll Collection Data," Sustainability, MDPI, vol. 15(15), pages 1-28, July.

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