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A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing

Author

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  • Zhenbao Wang

    (Hebei University of Engineering)

  • Sicheng Wang

    (The State University of New Jersey)

  • Haitao Lian

    (Hebei University of Engineering)

Abstract

With the background of hyperurbanization and a jobs–housing imbalance in Beijing and other megacities in China, this study aims at developing a systematic toolkit of demand estimation and route planning for long-distance commuter bus lines. Taking the express bus services (EBS) in the Changping Corridor in Beijing as an example, this paper presents the use of a route-planning algorithm. Mobile phone location (MPL) data serves as a valid instrument for the origin–destination (OD) estimation, which provides a new perspective to identify the locations of homes and jobs. The OD distribution matrices are specified via geocoded MPL data. To minimize the aggregated travel time and attract potential passengers, this study subsamples long-distance commuting trips among the OD matrices by determining the operation distance threshold. The threshold can be solved by comparing the aggregated travel time of EBS to the travel time of local buses and private cars. Using the distance threshold and other operational parameters, we process an iterative computation to determine the length of routes, number of lines, and stop spacing and location. Compared to local buses and private cars, the planned EBS lines could reduce aggregated travel time by at least 20%. The results demonstrate that the method presented in this study is applicable and flexible. This paper may lead to new research directions for route planning of long-distance commuter buses. The limitations of this study and the future research agenda are also discussed.

Suggested Citation

  • Zhenbao Wang & Sicheng Wang & Haitao Lian, 2021. "A route-planning method for long-distance commuter express bus service based on OD estimation from mobile phone location data: the case of the Changping Corridor in Beijing," Public Transport, Springer, vol. 13(1), pages 101-125, March.
    • Handle: RePEc:spr:pubtra:v:13:y:2021:i:1:d:10.1007_s12469-020-00254-w
    DOI: 10.1007/s12469-020-00254-w
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    2. Rajat Verma & Eunhan Ka & Satish V. Ukkusuri, 2024. "Long-term forecasts of statewide travel demand patterns using large-scale mobile phone GPS data: A case study of Indiana," Papers 2404.13211, arXiv.org.
    3. Zhenbao Wang & Shuyue Liu & Haitao Lian & Xinyi Chen, 2024. "Investigating the Nonlinear Effect of Land Use and Built Environment on Public Transportation Choice Using a Machine Learning Approach," Land, MDPI, vol. 13(8), pages 1-16, August.
    4. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).

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