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

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

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.

Suggested Citation

  • Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    • Handle: RePEc:eee:phsmap:v:595:y:2022:i:c:s037843712200125x
    DOI: 10.1016/j.physa.2022.127079
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