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A stochastic wave propagation model

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  • Kim, T.
  • Zhang, H.M.

Abstract

We introduce in this paper gap time, the time taken for a following vehicle to travel at its current speed the distance between its head position and the position of the rear of its leading vehicle, as a fundamental parameter for modeling some prominent features of congested traffic, namely the scatter of the fundamental diagram and the growth and decay of traffic disturbances on highways. In the model, traffic waves propagate in a stochastic manner and their speeds are determined by the relative differences between gap times and the reaction times of drivers. The scatter on the fundamental diagram and the growth and decay of perturbations are explained by random fluctuations of gap time and random transitions of traffic states on the fundamental diagram, respectively. Empirical data are used to validate the model and the evaluation shows close correspondence between model predictions and field observations.

Suggested Citation

  • Kim, T. & Zhang, H.M., 2008. "A stochastic wave propagation model," Transportation Research Part B: Methodological, Elsevier, vol. 42(7-8), pages 619-634, August.
    • Handle: RePEc:eee:transb:v:42:y:2008:i:7-8:p:619-634
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    6. Marija Nikolić & Michel Bierlaire & Matthieu de Lapparent & Riccardo Scarinci, 2019. "Multiclass Speed-Density Relationship for Pedestrian Traffic," Transportation Science, INFORMS, vol. 53(3), pages 642-664, May.
    7. Sumalee, A. & Zhong, R.X. & Pan, T.L. & Szeto, W.Y., 2011. "Stochastic cell transmission model (SCTM): A stochastic dynamic traffic model for traffic state surveillance and assignment," Transportation Research Part B: Methodological, Elsevier, vol. 45(3), pages 507-533, March.
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    9. Yeo, Hwasoo, 2008. "Asymmetric Microscopic Driving Behavior Theory," University of California Transportation Center, Working Papers qt1tn1m968, University of California Transportation Center.
    10. Han, Youngjun & Ahn, Soyoung, 2018. "Stochastic modeling of breakdown at freeway merge bottleneck and traffic control method using connected automated vehicle," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 146-166.
    11. Chen, Yuting & Mao, Jiannan & Zhang, Zhao & Huang, Hao & Lu, Weike & Yan, Qipeng & Liu, Lan, 2022. "A quasi-contagion process modeling and characteristic analysis for real-world urban traffic network congestion patterns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    12. 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.
    13. Zheng, Zuduo & Ahn, Soyoung & Chen, Danjue & Laval, Jorge, 2011. "Freeway traffic oscillations: Microscopic analysis of formations and propagations using Wavelet Transform," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1378-1388.
    14. Nikolić, Marija & Bierlaire, Michel & Farooq, Bilal & de Lapparent, Matthieu, 2016. "Probabilistic speed–density relationship for pedestrian traffic," Transportation Research Part B: Methodological, Elsevier, vol. 89(C), pages 58-81.
    15. Jorge A. Laval & Bhargava R. Chilukuri, 2014. "The Distribution of Congestion on a Class of Stochastic Kinematic Wave Models," Transportation Science, INFORMS, vol. 48(2), pages 217-224, May.
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    18. Xiqun (Michael) Chen & Zhiheng Li & Li Li & Qixin Shi, 2014. "A Traffic Breakdown Model Based on Queueing Theory," Networks and Spatial Economics, Springer, vol. 14(3), pages 485-504, December.

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