A Bayesian Approach for Travel Time Data Fusion

Probes with global positioning system (GPS) devices reveal useful information for traffic conditions, but the high level of noises makes it challenging to estimate travel time from the data collected. This paper proposes a new fusion model for estimating links/routes travel time based on GPS and flow data. The focus on the study is to fully utilise the temporal data from GPS probes and the flow data from loop detectors, and demonstrate the feasibility and potential for large-scale traffic condition estimation. The model calculated the posterior probability and the expectation of travel time given the prior knowledge from GPS and flow modeled travel time respectively. The result is evaluated for different types of roads across Sydney area against travel time provided by Google Map. The approach provides capabilities to fuse broadly available data sources for the compensation of low-quality GPS data regarding traffic network condition estimation.

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  • Supplemental Notes:
    • Abstracts used with permission of ERTICO – ITS Europe.
  • Corporate Authors:

    ERTICO - ITS Europe

    Avenue Louise 326, Blue Tower, 2nd Floor
    Brussels,   Belgium  B-1050
  • Authors:
    • Li, Zhang Gabriel
    • Xu, Yan
    • Cai, Chen
    • Chen, Fang
  • Conference:
  • Publication Date: 2015


  • English

Media Info

  • Media Type: Digital/other
  • Features: Figures; Maps; References; Tables;
  • Pagination: 9p
  • Monograph Title: 22nd ITS World Congress. Proceedings

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Filing Info

  • Accession Number: 01603785
  • Record Type: Publication
  • Report/Paper Numbers: ITS-2308
  • Files: TRIS
  • Created Date: Jun 28 2016 3:54PM