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Inferring Taxi Status Using GPS Trajectories

Yingke Zhu,Yu Zheng,Liuhang Zhang,Darshan Santani,Xing Xie,Qiang Yang

Published 2011 in arXiv.org

ABSTRACT

In this paper, we infer the statuses of a taxi, consisting of occupied, non-occupied and parked, in terms of its GPS trajectory. The status information can enable urban computing for improving a city's transportation systems and land use planning. In our solution, we first identify and extract a set of effective features incorporating the knowledge of a single trajectory, historical trajectories and geographic data like road network. Second, a parking status detection algorithm is devised to find parking places (from a given trajectory), dividing a trajectory into segments (i.e., sub-trajectories). Third, we propose a two-phase inference model to learn the status (occupied or non-occupied) of each point from a taxi segment. This model first uses the identified features to train a local probabilistic classifier and then carries out a Hidden Semi-Markov Model (HSMM) for globally considering long term travel patterns. We evaluated our method with a large-scale real-world trajectory dataset generated by 600 taxis, showing the advantages of our method over baselines.

PUBLICATION RECORD

  • Publication year

    2011

  • Venue

    arXiv.org

  • Publication date

    2011-11-01

  • Fields of study

    Geography, Computer Science, Engineering

  • Identifiers
    arXiv 1205.4378
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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