Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model

Authors

  • Xiaoxu Chen 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
  • Chao Yang 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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Published

2020-11-12

How to Cite

Chen, X., Xu, X., & Yang, C. (2020). Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model. Journal of Transport and Land Use, 13(1), 429-445. https://doi.org/10.5198/jtlu.2020.1554

Issue

Section

Special Issue: Innovations for Transport Planning in China