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Estimating the spatiotemporal impact of traffic incidents: An integer programming approach consistent with the propagation of shockwaves

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  • Wang, Zhengli
  • Qi, Xin
  • Jiang, Hai

Abstract

A fundamental issue in estimating the spatiotemporal impact of an incident is to ensure that the shape of the affected region in the speed map is consistent with the propagation of shockwaves. In this research, we develop an integer programming model with a set of novel constraints to guarantee such consistency, which is new to the literature. The input to our model includes the historical speed on a given road as well as the location and starting time of a known incident. The model then outputs the spatiotemporal region impacted by this incident. We prove that our model produces results that are consistent with the propagation of shockwaves. We then show that our model is computationally more efficient than the current state-of-the-art model because ours requires substantially fewer constraints. Numerical experiments using both simulation and real data demonstrate that the reduction in computational time can be as large as 95–98% on average.

Suggested Citation

  • Wang, Zhengli & Qi, Xin & Jiang, Hai, 2018. "Estimating the spatiotemporal impact of traffic incidents: An integer programming approach consistent with the propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 356-369.
    • Handle: RePEc:eee:transb:v:111:y:2018:i:c:p:356-369
    DOI: 10.1016/j.trb.2018.02.014
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    References listed on IDEAS

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    1. Ng, ManWo & Khattak, Asad & Talley, Wayne K., 2013. "Modeling the time to the next primary and secondary incident: A semi-Markov stochastic process approach," Transportation Research Part B: Methodological, Elsevier, vol. 58(C), pages 44-57.
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    5. Tian, Junfang & Li, Guangyu & Treiber, Martin & Jiang, Rui & Jia, Ning & Ma, Shoufeng, 2016. "Cellular automaton model simulating spatiotemporal patterns, phase transitions and concave growth pattern of oscillations in traffic flow," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 560-575.
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    Cited by:

    1. Wang, Zhengli & Zhu, Liyun & Ran, Bin & Jiang, Hai, 2020. "Queue profile estimation at a signalized intersection by exploiting the spatiotemporal propagation of shockwaves," Transportation Research Part B: Methodological, Elsevier, vol. 141(C), pages 59-71.
    2. Wang, Zhengli & Jiang, Hai, 2019. "Simultaneous correction of the time and location bias associated with a reported crash by exploiting the spatiotemporal evolution of travel speed," Transportation Research Part B: Methodological, Elsevier, vol. 123(C), pages 199-223.
    3. Jianjun Wang & Sai Wang & Xueqin Long & Dongyi Li & Chicheng Ma & Peng Li, 2022. "Ellipse-Like Radiation Range Grading Method of Traffic Accident Influence on Mountain Highways," Sustainability, MDPI, vol. 14(21), pages 1-21, October.
    4. Sheikh, Muhammad Sameer & Regan, Amelia, 2022. "A complex network analysis approach for estimation and detection of traffic incidents based on independent component analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).

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