IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12165-d925321.html
   My bibliography  Save this article

A Simulation-Based Study of the Influence of Low-Speed Vehicles on Expressway Traffic Safety

Author

Listed:
  • Chubo Xu

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Jianxiao Ma

    (College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, China)

  • Xiang Tang

    (College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China)

Abstract

To reveal the impact mechanism of low-speed vehicles (LSVs) on expressway traffic safety, this paper uses the polynomial fitting method to establish evolution models of traffic density and average speed at different LSV speeds in order to explore the queuing and dissipation characteristics of vehicles affected by LSVs and investigate the impact range of LSVs on expressways. Based on the findings above, this paper builds a Surrogate Safety Assessment Model (SSAM)-based model to quantify driving safety and further explore the differences in vehicle conflicts when an LSV moves in different lanes at the same speed. The simulation experiment is conducted based on the field data from the Inner Ring North Road located along the Nanjing Inner Ring High Speed Road. The results show that the evolutionary features of lane traffic density and average speed under different LSV speeds satisfy the octuple polynomial law, reflecting the spatial heterogeneity of vehicle distribution at different LSV driving speeds. Meanwhile, LSVs with different speeds produced the most significant negative impact on the roadway within 400 m of the expressway entrance. The lower the speed of the LSV, the more significant the adverse effect. In addition, this paper finds that when an LSV travels in different lanes at the same speed, the inner, middle, and outer lanes have the highest number of total conflicts, rear-end conflicts, and lane-change conflicts, respectively. Meanwhile, vehicles in the outer lane are the most significantly affected by LSVs, while vehicles in the middle lane are the least affected with the highest traffic efficiency. Additionally, the Maximum Speed (MaxS) and Difference in Vehicle Speed (DeltaS) for the middle lane are 47.9% and 60.5% higher than the outer lane, respectively. Nevertheless, based on the Probability of Unsuccessful Evasive Actions, i.e., P(UEA), vehicles in the middle lane have the highest probability of potential traffic conflicts. The methods used in this paper will have positive implications for establishing autonomous vehicle risk avoidance systems which can improve the safety levels of expressways.

Suggested Citation

  • Chubo Xu & Jianxiao Ma & Xiang Tang, 2022. "A Simulation-Based Study of the Influence of Low-Speed Vehicles on Expressway Traffic Safety," Sustainability, MDPI, vol. 14(19), pages 1-19, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12165-:d:925321
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12165/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12165/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Newell, G. F., 1998. "A moving bottleneck," Transportation Research Part B: Methodological, Elsevier, vol. 32(8), pages 531-537, November.
    2. Daganzo, Carlos F. & Laval, Jorge A., 2005. "Moving bottlenecks: A numerical method that converges in flows," Transportation Research Part B: Methodological, Elsevier, vol. 39(9), pages 855-863, November.
    3. Simoni, Michele D. & Claudel, Christian G., 2017. "A fast simulation algorithm for multiple moving bottlenecks and applications in urban freight traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 238-255.
    4. Denos C. Gazis & Robert Herman, 1992. "The Moving and “Phantom” Bottlenecks," Transportation Science, INFORMS, vol. 26(3), pages 223-229, August.
    5. Song Fang & Linghong Shen & Jianxiao Ma & Chubo Xu, 2022. "Study on the Design of Variable Lane Demarcation in Urban Tunnels," Sustainability, MDPI, vol. 14(9), pages 1-12, May.
    6. Yang, Yang & He, Kun & Wang, Yun-peng & Yuan, Zhen-zhou & Yin, Yong-hao & Guo, Man-ze, 2022. "Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    7. Jia, Bin & Jiang, Rui & Wu, Qing-Song & Hu, Mao-bin, 2005. "Honk effect in the two-lane cellular automaton model for traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 544-552.
    8. Song Fang & Jianxiao Ma, 2021. "Influence Range and Traffic Risk Analysis of Moving Work Zones on Urban Roads," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    9. Daganzo, Carlos F. & Laval, Jorge A., 2005. "On the numerical treatment of moving bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 39(1), pages 31-46, January.
    10. Hunter-Zaworski, K. M., 2012. "Impacts of low-speed vehicles on transportation infrastructure and safety," The Journal of Transport and Land Use, Center for Transportation Studies, University of Minnesota, vol. 5(2), pages 68-76.
    11. Xueyan Wei & Chengcheng Xu & Wei Wang & Menglin Yang & Xiaoma Ren, 2017. "Evaluation of average travel delay caused by moving bottlenecks on highways," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-25, August.
    12. Hu, Xiaojian & Lin, Chenxi & Hao, Xiatong & Lu, RuiYing & Liu, TengHui, 2021. "Influence of tidal lane on traffic breakdown and spatiotemporal congested patterns at moving bottleneck in the framework of Kerner’s three-phase traffic theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    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. Wang, Xuan & Zeng, Junwei & Qian, Yongsheng & Wei, Xuting & Zhang, Futao, 2024. "Heterogeneous traffic flow of expressway with Level 2 autonomous vehicles considering moving bottlenecks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    2. Laval, Jorge A., 2009. "Effects of geometric design on freeway capacity: Impacts of truck lane restrictions," Transportation Research Part B: Methodological, Elsevier, vol. 43(6), pages 720-728, July.
    3. Laval, Jorge A. & Daganzo, Carlos F., 2006. "Lane-changing in traffic streams," Transportation Research Part B: Methodological, Elsevier, vol. 40(3), pages 251-264, March.
    4. Simoni, Michele D. & Claudel, Christian G., 2017. "A fast simulation algorithm for multiple moving bottlenecks and applications in urban freight traffic management," Transportation Research Part B: Methodological, Elsevier, vol. 104(C), pages 238-255.
    5. He, Yifan & Zeng, An, 2024. "Expanding bottlenecks reveals hidden bottlenecks and leads to more congested city centers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 640(C).
    6. Leclercq, Ludovic, 2007. "Bounded acceleration close to fixed and moving bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 41(3), pages 309-319, March.
    7. Yang, Liu & Zheng, Jianlong & Cheng, Yang & Ran, Bin, 2019. "An asymmetric cellular automata model for heterogeneous traffic flow on freeways with a climbing lane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 535(C).
    8. Chow, Andy H.F. & Li, Shuai & Zhong, Renxin, 2017. "Multi-objective optimal control formulations for bus service reliability with traffic signals," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 248-268.
    9. Xueyan Wei & Chengcheng Xu & Wei Wang & Menglin Yang & Xiaoma Ren, 2017. "Evaluation of average travel delay caused by moving bottlenecks on highways," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-25, August.
    10. Shiomi, Yasuhiro & Yoshii, Toshio & Kitamura, Ryuichi, 2011. "Platoon-based traffic flow model for estimating breakdown probability at single-lane expressway bottlenecks," Transportation Research Part B: Methodological, Elsevier, vol. 45(9), pages 1314-1330.
    11. Zhu, Chenqiang & Zhong, Shiquan & Li, Guangyu & Ma, Shoufeng, 2017. "New control strategy for the lattice hydrodynamic model of traffic flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 445-453.
    12. Chen Zhang & Yichen Liang & Tian Tian & Peng Peng, 2024. "Sustainable Transportation: Exploring the Node Importance Evolution of Rail Transit Networks during Peak Hours," Sustainability, MDPI, vol. 16(16), pages 1-22, August.
    13. Rehborn, Hubert & Klenov, Sergey L. & Palmer, Jochen, 2011. "An empirical study of common traffic congestion features based on traffic data measured in the USA, the UK, and Germany," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4466-4485.
    14. Zhao, Jiansen & Yan, Zhongwei & Chen, Xinqiang & Han, Bing & Wu, Shubo & Ke, Ranxuan, 2022. "k-GCN-LSTM: A k-hop Graph Convolutional Network and Long–Short-Term Memory for ship speed prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 606(C).
    15. Chen, Danjue & Ahn, Soyoung, 2018. "Capacity-drop at extended bottlenecks: Merge, diverge, and weave," Transportation Research Part B: Methodological, Elsevier, vol. 108(C), pages 1-20.
    16. 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.
    17. Ramirez Ibarra, Monica & Saphores, Jean-Daniel M., 2023. "1,000 HP electric drayage trucks as a substitute for new freeway lanes construction," Transportation Research Part A: Policy and Practice, Elsevier, vol. 171(C).
    18. Laval, Jorge A. & Daganzo, Carlos F., 2004. "Multi-Lane Hybrid Traffic Flow Model: Quantifying the Impacts of Lane-Changing Maneuvers on Traffic Flow," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt8w70q261, Institute of Transportation Studies, UC Berkeley.
    19. Čičić, Mladen & Johansson, Karl Henrik, 2022. "Front-tracking transition system model for traffic state reconstruction, model learning, and control with application to stop-and-go wave dissipation," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 212-236.
    20. Yao, Zhouzhou & Wu, Xianyu & Yang, Yang & Li, Ning, 2024. "A digital decision approach for indirect-reciprocity based cooperative lane-changing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 633(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:gam:jsusta:v:14:y:2022:i:19:p:12165-:d:925321. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.