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Inferring alighting bus stops from smart card data combined with cellular signaling data

Author

Listed:
  • Ziqin Lan

    (Guangdong Polytechnic Normal University)

  • Zixuan Zhang

    (Sun Yat-Sen University
    Sun Yat-Sen University)

  • Jiatao Chen

    (Sun Yat-Sen University
    Sun Yat-Sen University)

  • Ming Cai

    (Sun Yat-Sen University
    Sun Yat-Sen University)

Abstract

Alighting bus stops inferring is of great significance for origin–destination estimation. Cellular signaling data (CSD), a kind of individual trajectory generated by mobile phones, provides a new idea for alighting stop identification. To explore the capacity of CSD in this field, this study proposes a method of inferring alighting bus stops by integrating smart card data, bus GPS data, and CSD. Firstly, a correspondence table is generated by individual matching, which correspondingly links mobile phone users in CSD and bus passengers in smart card data. Secondly, the inferred alighting bus stops are determined by the radii of the circumscribed circles of triangles consisting of directly projective points, piecewise projective points, and CSD points. The proposed method is verified by an experimental dataset from a behavioral simulation experiment of 10 volunteers in Foshan, China. The results show that the recognition rate is 92.94% and the inference accuracy is 65.82%, or 93.67% under a one-stop error. In the case of a real dataset in Foshan, the proposed method with a recognition rate of 53.02% highly outperforms the trip-chain-based method. The difference in the recognition rate between the two datasets is due to that the real dataset is more likely to be incomplete than the experimental data, which indicates that the performance and effectiveness of the proposed method are sensitive to the data quality and completeness of CSD and bus GPS data. Having said that, the proposed method can infer both alighting stops of linked bus trips and single unlinked bus trips.

Suggested Citation

  • Ziqin Lan & Zixuan Zhang & Jiatao Chen & Ming Cai, 2024. "Inferring alighting bus stops from smart card data combined with cellular signaling data," Transportation, Springer, vol. 51(4), pages 1433-1465, August.
    • Handle: RePEc:kap:transp:v:51:y:2024:i:4:d:10.1007_s11116-023-10373-5
    DOI: 10.1007/s11116-023-10373-5
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    References listed on IDEAS

    as
    1. Li He & Martin Trépanier & Bruno Agard, 2021. "Space–time classification of public transit smart card users’ activity locations from smart card data," Public Transport, Springer, vol. 13(3), pages 579-595, October.
    2. Behrang Assemi & Azalden Alsger & Mahboobeh Moghaddam & Mark Hickman & Mahmoud Mesbah, 2020. "Improving alighting stop inference accuracy in the trip chaining method using neural networks," Public Transport, Springer, vol. 12(1), pages 89-121, March.
    3. Zhanhong Cheng & Martin Trépanier & Lijun Sun, 2021. "Probabilistic model for destination inference and travel pattern mining from smart card data," Transportation, Springer, vol. 48(4), pages 2035-2053, August.
    Full references (including those not matched with items on IDEAS)

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