IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v629y2023ics0378437123007471.html
   My bibliography  Save this article

Risky driving behavior propagation: A novel stochastic SIR model and two-stage risk quantification method

Author

Listed:
  • Wen, Jianghui
  • Zhan, Xiaomei
  • Wu, Chaozhong
  • Xiao, Xinping
  • Lyu, Nengchao

Abstract

Risky driving behavior (RDB) can cause panic and anger among other drivers. Under the influence of irrational emotions, RDB can reoccur, resulting in a phenomenon known as risky driving behavior propagation (RDBP). RDBP is a dangerous phenomenon, which can easily lead to traffic conflicts and accidents. It is significant to determine the propagation mechanism of RDBP to road safety. Firstly, RDBP can be considered a type of disease because three essential components of RDBP share similarities with an epidemic. A stochastic SIR model for risky driving behavior propagation (SSIR-RDBP) is constructed to quantify the mechanism of RDBP. Additionally, a Lyapunov function is adopted to prove the existence and uniqueness of the model solution. Secondly, based on current moment risk and cumulative risk, a two-stage risk approach is adopted to quantify the risk of RDBP. The Conditional Value at Risk (CVaR) is used to quantify the current moment risk, and the probability of the Markov state transition matrix is introduced to describe the cumulative risk. Finally, sensitivity analysis of the model parameters is carried out to explore the intrinsic variables of the parameters. The results suggest that the risk of RDBP increases rapidly to its peak value within the first 5 s. As traffic saturation increases, the decline rate of sustainable drivers and the rise rate of recovered individuals grows cubically, and the rate of reaching peak infection increases linearly. Furthermore, the fluctuation range of risk and the maximum risk both increase quadratically. The driver’s emotional control ability is strongest when the infection rate is set to 4, and the recovery rate increases linearly with road safety education. Improving the driver’s emotional control ability, and increasing awareness of road safety education, can help to control the risk in RDBP.

Suggested Citation

  • Wen, Jianghui & Zhan, Xiaomei & Wu, Chaozhong & Xiao, Xinping & Lyu, Nengchao, 2023. "Risky driving behavior propagation: A novel stochastic SIR model and two-stage risk quantification method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 629(C).
  • Handle: RePEc:eee:phsmap:v:629:y:2023:i:c:s0378437123007471
    DOI: 10.1016/j.physa.2023.129192
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437123007471
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

    File URL: https://libkey.io/10.1016/j.physa.2023.129192?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. Shengdi Chen & Qingwen Xue & Xiaochen Zhao & Yingying Xing & Jian John Lu, 2021. "Risky Driving Behavior Recognition Based on Vehicle Trajectory," IJERPH, MDPI, vol. 18(23), pages 1-14, November.
    2. Saha, Arpita & Chakraborty, Souvik & Chandra, Satish & Ghosh, Indrajit, 2018. "Kriging based saturation flow models for traffic conditions in Indian cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 38-51.
    3. Wen, Jianghui & Hong, Lijiang & Dai, Min & Xiao, Xinping & Wu, Chaozhong, 2023. "A stochastic model for stop-and-go phenomenon in traffic oscillation: On the prospective of macro and micro traffic flow," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    4. Guo, Miao & Zhao, Xiaohua & Yao, Ying & Bi, Chaofan & Su, Yuelong, 2022. "Application of risky driving behavior in crash detection and analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    5. Qian, Wei-Liang & F. Siqueira, Adriano & F. Machado, Romuel & Lin, Kai & Grant, Ted W., 2017. "Dynamical capacity drop in a nonlinear stochastic traffic model," Transportation Research Part B: Methodological, Elsevier, vol. 105(C), pages 328-339.
    6. Li, Bin & Dong, Xujun & Wen, Jianghui, 2022. "Cooperative-driving control for mixed fleets at wireless charging sections for lane changing behaviour," Energy, Elsevier, vol. 243(C).
    7. Ke Wang & Qingwen Xue & Jian John Lu, 2021. "Risky Driver Recognition with Class Imbalance Data and Automated Machine Learning Framework," IJERPH, MDPI, vol. 18(14), pages 1-18, July.
    Full references (including those not matched with items on IDEAS)

    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. Bai, Lu & Wong, S.C. & Xu, Pengpeng & Chow, Andy H.F. & Lam, William H.K., 2021. "Calibration of stochastic link-based fundamental diagram with explicit consideration of speed heterogeneity," Transportation Research Part B: Methodological, Elsevier, vol. 150(C), pages 524-539.
    2. Li, Hui & Nie, Weige & Duan, Huiming, 2024. "A Haavelmo grey model based on economic growth and its application to energy industry investments," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Antonis Kostopoulos & Thodoris Garefalakis & Eva Michelaraki & Christos Katrakazas & George Yannis, 2024. "Modeling and Sustainability Implications of Harsh Driving Events: A Predictive Machine Learning Approach," Sustainability, MDPI, vol. 16(14), pages 1-20, July.
    4. Zheng, Shi-Teng & Jiang, Rui & Tian, Jun-Fang & Zhang, H.M. & Li, Zhen-Hua & Gao, Lan-Da & Jia, Bin, 2021. "Experimental study on properties of lightly congested flow," Transportation Research Part B: Methodological, Elsevier, vol. 149(C), pages 1-19.
    5. Jianfeng Xi & Yunhe Zhao & Zhiqiang Li & Yizhou Jiang & Wenwen Feng & Tongqiang Ding, 2022. "A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec," IJERPH, MDPI, vol. 19(23), pages 1-13, November.
    6. Vulfovich, Andrey & Kuperman, Alon, 2024. "Extending the lower bound of attainable load-independent voltage gain values range in contactless, feedbackless and sensorless power delivery links," Energy, Elsevier, vol. 293(C).
    7. Hall, Jonathan D. & Savage, Ian, 2019. "Tolling roads to improve reliability," Journal of Urban Economics, Elsevier, vol. 113(C).
    8. Taghreed Alghamdi & Khalid Elgazzar & Taysseer Sharaf, 2021. "Spatiotemporal Traffic Prediction Using Hierarchical Bayesian Modeling," Future Internet, MDPI, vol. 13(9), pages 1-18, August.
    9. Bouadi, Marouane & Jia, Bin & Jiang, Rui & Li, Xingang & Gao, Zi-You, 2022. "Stability analysis of stochastic second-order macroscopic continuum models and numerical simulations," Transportation Research Part B: Methodological, Elsevier, vol. 164(C), pages 193-209.
    10. Adnan Yousaf & Jianping Wu, 2023. "Motorcycle-Riding Experience: Friend or Foe? Understanding Its Effects on Driving Behavior and Accident Risk," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
    11. Huacai Xian & Yujia Hou & Yu Wang & Shunzhong Dong & Junying Kou & Zewen Li, 2022. "Influence of Risky Driving Behavior and Road Section Type on Urban Expressway Driving Safety," Sustainability, MDPI, vol. 15(1), pages 1-15, December.
    12. Ma, Guangyi & Li, Keping, 2024. "Analysis and simulation of vehicle following behavior with consideration of multiple time delays," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(C).
    13. Chen, Yi-Ting & Sun, Edward W. & Chang, Ming-Feng & Lin, Yi-Bing, 2021. "Pragmatic real-time logistics management with traffic IoT infrastructure: Big data predictive analytics of freight travel time for Logistics 4.0," International Journal of Production Economics, Elsevier, vol. 238(C).
    14. Vulfovich, Andrey & Kuperman, Alon, 2024. "Increasing tolerable coupling coefficients range of series-series compensated inductive wireless power transfer systems operating in restricted sub-resonant frequency region with constant current outp," Energy, Elsevier, vol. 292(C).
    15. Zhao, Fangxia & Shang, HuaYan & Cui, JiHui, 2023. "Role of electric vehicle driving behavior on optimal setting of wireless charging lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).

    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:phsmap:v:629:y:2023:i:c:s0378437123007471. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.