Heterogeneous data fusion method to estimate travel time distributions in congested road networks

Chaoyang Shi, Bi Yu Chen, Hing Keung William Lam, Qingquan Li

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)


Licensee MDPI, Basel, Switzerland. Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.
Original languageEnglish
Article number2822
JournalSensors (Switzerland)
Issue number12
Publication statusPublished - 6 Dec 2017


  • Data fusion
  • Evidence theory
  • Spatial correlation
  • Travel time distribution
  • Uncertainty

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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