Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights

Jiping Xing, Wei Wu, Qixiu Cheng, Ronghui Liu

Research output: Journal article publicationReview articleAcademic researchpeer-review

8 Citations (Scopus)

Abstract

Accurate traffic state (i.e., flow, speed, density, etc.) on an urban road network is important information for urban traffic control and management strategies. However, due to the limitation of detector installation cost, it is difficult to obtain accurate traffic states through detectors in the whole urban road network with limited detector equipment. In this paper, we review the studies that focus on the missing traffic state estimation problem, especially for the traffic state estimation on the segments without detectors. We provide a way to summarize for readers who have an interest in the different modelling and application of missing traffic state estimation. We first divide the existing studies into three categories: estimation under different missing scenarios, estimation with multi-source data, estimation by fusing different detector types. Then, we summary some existing challenges by the different missing scenarios, data applications, and methodologies. Finally, this work also discusses some future research directions.

Original languageEnglish
Article number127079
JournalPhysica A: Statistical Mechanics and its Applications
Volume595
DOIs
Publication statusPublished - 1 Jun 2022

Keywords

  • Data fusion
  • Missing traffic state estimation
  • Multi-source data application
  • Systematic review
  • Urban road network

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Statistics and Probability

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