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A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS Data

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  • Shuqi Zhong

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Jiatao Chen

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

  • Ming Cai

    (School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
    Guangdong Provincial Key Laboratory of Intelligent Transportation System, School of Intelligent Systems Engineering, Sun Yat-sen University, Guangzhou 510006, China)

Abstract

Transport mode is one of the important travel characteristics for citizens, which is crucial to the planning and management of urban transportation. With the natural advantages of large sample sizes and a wide coverage of people, more and more researchers adopt mobile phone signaling data (MSD) to detect transport modes. However, due to their low positioning accuracy and temporally irregular nature, identifying transport modes with similar spatiotemporal features, such as the bus and car modes, is particularly challenging. We propose a transport detection framework using MSD combined with bus GPS data to distinguish between the car and bus modes. First, a trajectory matching algorithm is proposed to obtain the most probable bus that mobile phone users may take. Then, more features are mined to improve the accuracy of transport mode detection with different classification models. Furthermore, for signaling trajectories identified as the bus mode, more bus travel information is recognized, including the boarding and alighting station and timestamp. Finally, we built a ground truth dataset and compared the recognition accuracies under different features and classification models. The result shows that the transport mode detection accuracies of the proposed framework with the GBDT, XGBoost, and LightGBM algorithms are all higher than 94%.

Suggested Citation

  • Shuqi Zhong & Jiatao Chen & Ming Cai, 2024. "A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS Data," Mathematics, MDPI, vol. 12(23), pages 1-21, December.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:23:p:3843-:d:1537707
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    References listed on IDEAS

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    1. Li, Linchao & Zhu, Jiasong & Zhang, Hailong & Tan, Huachun & Du, Bowen & Ran, Bin, 2020. "Coupled application of generative adversarial networks and conventional neural networks for travel mode detection using GPS data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 282-292.
    2. Longxu Yan & De Wang & Shangwu Zhang & Dongcan Xie, 2019. "Evaluating the multi-scale patterns of jobs-residence balance and commuting time–cost using cellular signaling data: a case study in Shanghai," Transportation, Springer, vol. 46(3), pages 777-792, June.
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