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

Ang Li, William H.K. Lam, Wei Ma, S. C. Wong, Andy H.F. Chow, Mei Lam Tam

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Article number121613
JournalExpert Systems with Applications
Volume237
DOIs
Publication statusPublished - 1 Mar 2024

Keywords

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

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Real-time estimation of multi-class path travel times using multi-source traffic data'. Together they form a unique fingerprint.

Cite this