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Smart city article details
| Title | A Method Of User Travel Mode Recognition Based On Convolutional Neural Network And Cell Phone Signaling Data |
|---|---|
| ID_Doc | 2581 |
| Authors | Yang Z.; Xie Z.; Hou Z.; Ji C.; Deng Z.; Li R.; Wu X.; Zhao L.; Ni S. |
| Year | 2023 |
| Published | Electronics (Switzerland), 12, 17 |
| DOI | http://dx.doi.org/10.3390/electronics12173698 |
| Abstract | As urbanization accelerates, traffic congestion in cities has become a problem. Therefore, accurately identifying urban residents’ travel patterns is crucial for urban traffic planning and intelligent transportation systems. In this study, a convolutional neural network (CNN) approach based on multichannel feature extraction using mobile phone signaling data to identify user travel modes is proposed. Here, a trajectory generation method was designed for five types of travel modes. By designing a spatiotemporal threshold screening method, anomalies were identified and processed, combined with the feature analysis method, key points in the signaling extracted, the travel trajectory sliced, and travel sub-trajectory data generated. Next, in the travel mode identification stage, road network information was introduced to improve localization accuracy, and the method for calculating feature values improved. A user travel feature dataset was generated by calculating the feature values, and the travel modes represented by each class were classified and recognized based on the CNN method. Satisfactory results were achieved through experiments using mobile phone signaling and field research data in Kunming, China. The experimental results showed that analysis based on mobile phone signaling data could classify, identify, and obtain different travel category modes. This method’s accuracy was 84.7%. The method provided a feasible way of identifying travel patterns in the context of smart cities and big data, providing strong support for urban transport planning and management, and has the potential for wider application. © 2023 by the authors. |
| Author Keywords | big traffic data; convolutional neural networks; geographic information science (GIS); mobile phone signaling; travel patterns |
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