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Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

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  • Deng, Wen
  • Lei, Hao
  • Zhou, Xuesong

Abstract

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.

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  • Deng, Wen & Lei, Hao & Zhou, Xuesong, 2013. "Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach," Transportation Research Part B: Methodological, Elsevier, vol. 57(C), pages 132-157.
    • Handle: RePEc:eee:transb:v:57:y:2013:i:c:p:132-157
    DOI: 10.1016/j.trb.2013.08.015
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    13. Zheng, Zuduo & Su, Dongcai, 2016. "Traffic state estimation through compressed sensing and Markov random field," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 525-554.
    14. Zhang, Kunpeng & Feng, Xiaoliang & Jia, Ning & Zhao, Liang & He, Zhengbing, 2022. "TSR-GAN: Generative Adversarial Networks for Traffic State Reconstruction with Time Space Diagrams," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
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