IDEAS home Printed from https://ideas.repec.org/a/eee/transb/v111y2018icp356-369.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0191261517308111
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.trb.2018.02.014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Duret, Aurélien & Yuan, Yufei, 2017. "Traffic state estimation based on Eulerian and Lagrangian observations in a mesoscopic modeling framework," Transportation Research Part B: Methodological, Elsevier, vol. 101(C), pages 51-71.
    2. Heydecker, Bejamin, 1994. "Incidents And Intervention On Freeways," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8ts7g87g, Institute of Transportation Studies, UC Berkeley.
    3. Tian, Junfang & Treiber, Martin & Ma, Shoufeng & Jia, Bin & Zhang, Wenyi, 2015. "Microscopic driving theory with oscillatory congested states: Model and empirical verification," Transportation Research Part B: Methodological, Elsevier, vol. 71(C), pages 138-157.
    4. Xiong, Yingge & Tobias, Justin L. & Mannering, Fred L., 2014. "The analysis of vehicle crash injury-severity data: A Markov switching approach with road-segment heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 67(C), pages 109-128.
    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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tomoko Sakiyama & Ikuo Arizono, 2019. "Reversible Transitions in a Cellular Automata-Based Traffic Model with Driver Memory," Complexity, Hindawi, vol. 2019, pages 1-8, December.
    2. Treiber, Martin & Kesting, Arne, 2018. "The Intelligent Driver Model with stochasticity – New insights into traffic flow oscillations," Transportation Research Part B: Methodological, Elsevier, vol. 117(PB), pages 613-623.
    3. Yuan, Zijian & Wang, Tao & Zhang, Jing & Li, Shubin, 2022. "Influences of dynamic safe headway on car-following behavior," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    4. Li, Yongfu & Zhao, Hang & Zhang, Li & Zhang, Chao, 2018. "An extended car-following model incorporating the effects of lateral gap and gradient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 177-189.
    5. Wang, Tao & Li, Guangyao & Zhang, Jing & Li, Shubin & Sun, Tao, 2019. "The effect of Headway Variation Tendency on traffic flow: Modeling and stabilization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 566-575.
    6. Tianjun Feng & Keyi Liu & Chunyan Liang, 2023. "An Improved Cellular Automata Traffic Flow Model Considering Driving Styles," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    7. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Modeling connected and autonomous vehicles in heterogeneous traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 269-277.
    8. Zhang, Jing & Xu, Keyu & Li, Shubin & Wang, Tao, 2020. "A new two-lane lattice hydrodynamic model with the introduction of driver’s predictive effect," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    9. Tian, Junfang & Zhang, H.M. & Treiber, Martin & Jiang, Rui & Gao, Zi-You & Jia, Bin, 2019. "On the role of speed adaptation and spacing indifference in traffic instability: Evidence from car-following experiments and its stochastic model," Transportation Research Part B: Methodological, Elsevier, vol. 129(C), pages 334-350.
    10. Ye, Lanhang & Yamamoto, Toshiyuki, 2018. "Impact of dedicated lanes for connected and autonomous vehicle on traffic flow throughput," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 588-597.
    11. Fu, Ding-Jun & Zhang, Cun-Bao & Liu, Jun & Li, Tao & Li, Qi-Lang, 2024. "Research of the left-turn vehicles lane-changing behaviors at signalized intersections with contraflow lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(C).
    12. 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.
    13. Zhang, Jing & Wang, Bo & Li, Shubin & Sun, Tao & Wang, Tao, 2020. "Modeling and application analysis of car-following model with predictive headway variation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    14. Wang, Jiawen & Zou, Linzhi & Zhao, Jing & Wang, Xinwei, 2024. "Dynamic capacity drop propagation in incident-affected networks: Traffic state modeling with SIS-CTM," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    15. Fu, Ding-Jun & Li, Qi-Lang & Jiang, Rui & Wang, Bing-Hong, 2020. "A simple cellular automaton model with dual cruise-control limit in the framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 559(C).
    16. Zhang, Jing & Xu, Keyu & Li, Guangyao & Li, Shubin & Wang, Tao, 2021. "Dynamics of traffic flow affected by the future motion of multiple preceding vehicles under vehicle-connected environment: Modeling and stabilization," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).
    17. Buddhavarapu, Prasad & Bansal, Prateek & Prozzi, Jorge A., 2021. "A new spatial count data model with time-varying parameters," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 566-586.
    18. Shuaiyang Jiao & Shengrui Zhang & Bei Zhou & Zixuan Zhang & Liyuan Xue, 2020. "An Extended Car-Following Model Considering the Drivers’ Characteristics under a V2V Communication Environment," Sustainability, MDPI, vol. 12(4), pages 1-18, February.
    19. Dong, Chunjiao & Shao, Chunfu & Clarke, David B. & Nambisan, Shashi S., 2018. "An innovative approach for traffic crash estimation and prediction on accommodating unobserved heterogeneities," Transportation Research Part B: Methodological, Elsevier, vol. 118(C), pages 407-428.
    20. Yao, Handong & Li, Qianwen & Li, Xiaopeng, 2020. "A study of relationships in traffic oscillation features based on field experiments," Transportation Research Part A: Policy and Practice, Elsevier, vol. 141(C), pages 339-355.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transb:v:111:y:2018:i:c:p:356-369. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/548/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.