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

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  • NSTL
  • Elsevier
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.

Urban road networkMissing traffic state estimationData fusionMulti-source data applicationSystematic reviewTRAVEL-TIME ESTIMATIONAUTOMATIC VEHICLE IDENTIFICATIONWIRELESS LOCATION TECHNOLOGYQUEUE LENGTH ESTIMATIONPLATE RECOGNITION DATAREAL-TIMESENSOR-LOCATIONFLOW PREDICTIONOPTIMIZATION APPROACHROBUST OPTIMIZATION

Xing, Jiping、Wu, Wei、Cheng, Qixiu、Liu, Ronghui

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Southeast Univ

Zhejiang Inst Commun Co Ltd

Univ Leeds

2022

10.1016/j.physa.2022.127079

Physica

Physica

ISSN：0378-4371
年,卷(期)：2022.595
  • 14
  • 112