Real-time estimation of multi-class path travel times using multi-source traffic data

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Ang Li
  • William H.K. Lam
  • Wei Ma
  • S. C. Wong
  • Mei Lam Tam

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number121613
Journal / PublicationExpert Systems with Applications
Volume237
Issue numberPart C
Online published17 Sept 2023
Publication statusPublished - 1 Mar 2024

Abstract

In practice, most of the intelligent transportation systems provide average travel times of all vehicles on selected paths in real time on a regular basis. However, path travel times of different vehicles could vary widely under different traffic conditions. There is a need to consider the differences in vehicle classes for path travel time estimation. This paper proposes a novel modeling framework that considers variance–covariance relationships between vehicle classes for real-time estimation of multi-class path travel times with use of multi-source traffic data collected from various types of sensors. The proposed methodology is examined with a case study of a selected urban expressway in Hong Kong with data obtained from multiple sources. The path travel time estimates by vehicle class are validated and the results demonstrate the merits and performance of the proposed framework. © 2023 Elsevier Ltd

Research Area(s)

  • AVI data, GPS data, Multiple vehicle classes, Point sensor data, Real-time travel time estimation

Citation Format(s)

Real-time estimation of multi-class path travel times using multi-source traffic data. / Li, Ang; Lam, William H.K.; Ma, Wei et al.
In: Expert Systems with Applications, Vol. 237, No. Part C, 121613, 01.03.2024.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review