Estimating the most likely space-time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method

Jinjin Tang, Ying Song, Harvey J. Miller, Xuesong Zhou

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Global Positioning System and other location-based services record vehicles' spatial locations at discrete time stamps. Considering these recorded locations in space with given specific time stamps, this paper proposes a novel time-dependent graph model to estimate their likely space-time paths and their uncertainties within a transportation network. The proposed model adopts theories in time geography and produces the feasible network-time paths, the expected link travel times and dwell times at possible intermediate stops. A dynamic programming algorithm implements the model for both offline and real-time applications. To estimate the uncertainty, this paper also develops a method based on the potential path area for all feasible network-time paths. This paper uses a set of real-world trajectory data to illustrate the proposed model, prove the accuracy of estimated results and demonstrate the computational efficiency of the estimation algorithm.

Original languageEnglish (US)
Pages (from-to)176-194
Number of pages19
JournalTransportation Research Part C: Emerging Technologies
Volume66
DOIs
StatePublished - May 1 2016

Keywords

  • Dynamic shortest path
  • GPS map matching
  • Traffic state estimation
  • Uncertainty estimation

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications

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