Advancing Fine-Grained Travel Mode Identification in Real Mobile Phone Signaling Data: A Deep Learning Approach

Abstract

Mobile Phone Signaling (MPS) data record the daily traces of urban residents, offering a cost-effective means to obtain travel information for urban traffic management and planning at low cost. However, despite the vast amount of data available, there remains a lag in the development of techniques for identifying fine-grained information such as travel modes. On the one hand, the high positioning error and irregular collection frequency make the identification of fine-grained modes challenging. On the other hand, the difficulties in collecting real labeled data limit the training and evaluation of advanced models. In this paper, we present an advanced Travel Mode Identification (TMI) framework and collect real labeled MPS data for evaluation. Specifically, a fast smoothing method is proposed to enhance noise reduction in large-scale trajectories while effectively mitigating positioning errors. We propose novel point-level bus route alignment features for advanced deep-learning models to improve differentiation between motorized modes. Furthermore, a deep learning model with ensembled feature encoding modules is designed to overcome training instability due to limited data amount. Our proposed framework achieves an accuracy of 83.06% in identifying fine-grained travel modes, including walking, riding, bus, car, and metro, with recall rates exceeding 78% for all modes except walking. We analyze the relationship between accuracy and the spatiotemporal characteristics of trajectories, revealing a significant impact from the collection frequency, while showing insensitivity to distance gaps and positioning errors. This study demonstrates the potential of MPS for TMI and promotes its application in intelligent transportation systems.

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