Probabilistic travel time progression and its application to automatic vehicle identification data

A. Nantes, D. Ngoduy, M. Miska, Edward Chin Shin Chung

Research output: Journal article publicationJournal articleAcademic researchpeer-review

12 Citations (Scopus)


© 2015 Elsevier Ltd. Travel time has been identified as an important variable to evaluate the performance of a transportation system. Based on the travel time prediction, road users can make their optimal decision in choosing route and departure time. In order to utilise adequately the advanced data collection methods that provide real-time different types of information, this paper is aimed at a novel approach to the estimation of long roadway travel times, using Automatic Vehicle Identification (AVI) technology. Since the long roads contain a large number of scanners, the AVI sample size tends to reduce and, as such, computing the distribution for the total road travel time becomes difficult. In this work, we introduce a probabilistic framework that extends the deterministic travel time progression method to dependent random variables and enables the off-line estimation of road travel time distributions. In the proposed method, the accuracy of the estimation does not depend on the size of the sample over the entire corridor, but only on the amount of historical data that is available for each link. In practice, the system is also robust to small link samples and can be used to detect outliers within the AVI data.
Original languageEnglish
Pages (from-to)131-145
Number of pages15
JournalTransportation Research Part B: Methodological
Issue numberP1
Publication statusPublished - 1 Nov 2015
Externally publishedYes


  • Automatic Vehicle Identification (AVI)
  • Data impoverishment problem
  • Long corridor
  • Off-line travel time estimation
  • Probabilistic travel time progression

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Transportation


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