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

Shuqi Zhong, Jiatao Chen and Ming Cai ()
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Shuqi Zhong: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Jiatao Chen: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China
Ming Cai: School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen 518107, China

Mathematics, 2024, vol. 12, issue 23, 1-21

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%.

Keywords: mobile phone signaling data; transport mode detection; trajectory matching; bus GPS data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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