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Mining spatiotemporal patterns of urban dwellers from taxi trajectory data

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Abstract

With the widespread adoption of locationaware technology, obtaining long-sequence, massive and high-accuracy spatiotemporal trajectory data of individuals has become increasingly popular in various geographic studies. Trajectory data of taxis, one of the most widely used inner-city travel modes, contain rich information about both road network traffic and travel behavior of passengers. Such data can be used to study the microscopic activity patterns of individuals as well as the macro system of urban spatial structures. This paper focuses on trajectories obtained from GPS-enabled taxis and their applications for mining urban commuting patterns. A novel approach is proposed to discover spatiotemporal patterns of household travel from the taxi trajectory dataset with a large number of point locations. The approach involves three critical steps: spatial clustering of taxi origin-destination (OD) based on urban traffic grids to discover potentially meaningful places, identifying threshold values from statistics of the OD clusters to extract urban jobs-housing structures, and visualization of analytic results to understand the spatial distribution and temporal trends of the revealed urban structures and implied household commuting behavior. A case study with a taxi trajectory dataset in Shanghai, China is presented to demonstrate and evaluate the proposed method.

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Authors and Affiliations

  1. The GIScience Key Lab, Education Ministry of China, East China Normal University, Shanghai, 200241, China

    Feng Mao & Minhe Ji

  2. Research Center for East-West Cooperation in China, East China Normal University, Shanghai, 200241, China

    Minhe Ji

  3. Institute of Remote Sensing and Earth Science, Hangzhou Normal University, Hangzhou, 310036, China

    Ting Liu

Authors
  1. Feng Mao
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  2. Minhe Ji
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  3. Ting Liu
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Correspondence to Minhe Ji.

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Mao, F., Ji, M. & Liu, T. Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Front. Earth Sci. 10, 205–221 (2016). https://doi.org/10.1007/s11707-015-0525-4

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  • DOI: https://doi.org/10.1007/s11707-015-0525-4

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