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2020-11-12
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Special Issue: Innovations for Transport Planning in China
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Copyright (c) 2020 Xiaoxu Chen, Xiangdong Xu, Chao Yang
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How to Cite
Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model
Xiaoxu Chen
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University
Chao Yang
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University
Xiangdong Xu
Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University
DOI: https://doi.org/10.5198/jtlu.2020.1554
Keywords: trip mode inference, mobile phone signaling data, Logarithm Gaussian Mixed Model
Abstract
Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.
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