Transportation Modes Identification from Mobile Phone Data Using Probabilistic Models

  • Dafeng Xu
  • Guojie Song
  • Peng Gao
  • Rongzeng Cao
  • Xinwei Nie
  • Kunqing Xie
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)

Abstract

Transportation modes identification is an important transportation research problem with wide applications. Traditional methods are mainly done based on GPS, WiFi and some other electronic devices, which are actually not in adequately widespread use. The popularity of mobile phones makes the work of identification by mobile phone data valuable. In this paper, based on mobile phone data without other equipment for assistance, we design a probabilistic method to identify transportation modes. The method consists of a Hidden Markov Model with two sub-models for different traffic conditions. The Speed Distribution Law (SDL) based approach is used under the normal condition; to improve the performance of our method under the congested condition, the Cumulative Prospect Theory (CPT) based approach is adopted as a supplementary way to do identification. Experiments on real data show that our method can reach high accuracy in the normal and congested condition alike.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dafeng Xu
    • 1
  • Guojie Song
    • 1
  • Peng Gao
    • 2
  • Rongzeng Cao
    • 2
  • Xinwei Nie
    • 1
  • Kunqing Xie
    • 1
  1. 1.Key Laboratory of Machine Perception, Ministry of EducationPeking UniversityChina
  2. 2.IBM ResearchBeijingChina

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