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A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data

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  • Han, Yu
  • Zhang, Mingyu
  • Guo, Yanyong
  • Zhang, Le

Abstract

This paper proposes a streaming-data-driven method for freeway traffic state estimation based on probe vehicle trajectory data, which are represented by a series of timestamps, spatial locations, and instantaneous speeds. The flow and density of a freeway section are reconstructed by estimating the numbers of normal vehicles between consecutive probe vehicles. Specifically, freeway traffic process is divided into different episodes based on the occurrence of shockwaves. The speed of a shockwave is assumed stochastic, and its posterior distribution is estimated via Bayesian regression. Based on the estimated shockwave speed, the number of vehicles between the most downstream and most upstream probe vehicles in an episode is estimated based on Newell’s simplified car-following theory. Then the penetration rate of probe vehicles can be obtained and the numbers of normal vehicles among the probe vehicles that are not captured by shockwaves can also be estimated. Finally, the trajectories of the normal vehicles are reconstructed using linear interpolation. The proposed approach is demonstrated by a simulation experiment and a real-world case study. A good estimation accuracy is achieved even when the penetration rates are as low as 5%–20%. The proposed method is also compared with a state-of-the-art method in the simulation study, which also estimates freeway traffic state solely based on probe vehicle trajectory data. It achieves a comparable performance without spacing information in the trajectory data.

Suggested Citation

  • Han, Yu & Zhang, Mingyu & Guo, Yanyong & Zhang, Le, 2022. "A streaming-data-driven method for freeway traffic state estimation using probe vehicle trajectory data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    • Handle: RePEc:eee:phsmap:v:606:y:2022:i:c:s0378437122006537
    DOI: 10.1016/j.physa.2022.128045
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    References listed on IDEAS

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    1. Xiao, Jianli & Wei, Chao & Liu, Yuncai, 2018. "Speed estimation of traffic flow using multiple kernel support vector regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 989-997.
    2. Paul I. Richards, 1956. "Shock Waves on the Highway," Operations Research, INFORMS, vol. 4(1), pages 42-51, February.
    3. He, Zhengbing & Zheng, Liang & Guan, Wei, 2015. "A simple nonparametric car-following model driven by field data," Transportation Research Part B: Methodological, Elsevier, vol. 80(C), pages 185-201.
    4. Kerner, Boris S. & Rehborn, Hubert & Schäfer, Ralf-Peter & Klenov, Sergey L. & Palmer, Jochen & Lorkowski, Stefan & Witte, Nikolaus, 2013. "Traffic dynamics in empirical probe vehicle data studied with three-phase theory: Spatiotemporal reconstruction of traffic phases and generation of jam warning messages," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(1), pages 221-251.
    5. Martin Schönhof & Dirk Helbing, 2007. "Empirical Features of Congested Traffic States and Their Implications for Traffic Modeling," Transportation Science, INFORMS, vol. 41(2), pages 135-166, May.
    6. Chen, Xinqiang & Chen, Huixing & Yang, Yongsheng & Wu, Huafeng & Zhang, Wenhui & Zhao, Jiansen & Xiong, Yong, 2021. "Traffic flow prediction by an ensemble framework with data denoising and deep learning model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    7. Kerner, Boris S., 2004. "Three-phase traffic theory and highway capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 379-440.
    8. Wang, Yibing & Papageorgiou, Markos, 2005. "Real-time freeway traffic state estimation based on extended Kalman filter: a general approach," Transportation Research Part B: Methodological, Elsevier, vol. 39(2), pages 141-167, February.
    9. Newell, G. F., 2002. "A simplified car-following theory: a lower order model," Transportation Research Part B: Methodological, Elsevier, vol. 36(3), pages 195-205, March.
    Full references (including those not matched with items on IDEAS)

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