IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v12y2020i18p7386-d410881.html

Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS

Author

Listed:
  • Bo Yang

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China)

  • Yao Wu

    (Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Southeast University, Nanjing 210096, China
    Department of Civil Engineering, The University of British Columbia, Vancouver, BC V6T 1Z4, Canada)

  • Weihua Zhang

    (School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei 230009, China)

  • Jie Bao

    (Civil Aviation College, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China)

Abstract

In this study, collision-related data were collected on the I-880 freeway of California in the United States from 2006 to 2011. Our objective was to study the collision probability of different collision types and severities in different traffic states. The traffic states were divided by the traditional level of service (LOS) method. Various Bayesian conditional logit models have been established to analyze the relationship between the collision probability of different collision patterns and LOSs. The results showed that LOS A had the best safety performance associated with all of the collision types and severities, LOS C had the worst safety performance associated with hit object collisions, LOS D had the worst safety performance associated with sideswipe collisions and rear end collisions, and LOS F had the worst safety performance associated with injury collisions. The five-stage Bayesian random parameter sequential logit model was established to quantify the effects of different variables on the collision probability of various collision types and severities. In addition to LOS, the visibility, road surface, weather, ramp, and number of lanes had significant effects on different collision types and severities.

Suggested Citation

  • Bo Yang & Yao Wu & Weihua Zhang & Jie Bao, 2020. "Modeling Collision Probability on Freeway: Accounting for Different Types and Severities in Various LOS," Sustainability, MDPI, vol. 12(18), pages 1-13, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7386-:d:410881
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/12/18/7386/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/12/18/7386/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xu, Chengcheng & Wang, Yong & Liu, Pan & Wang, Wei & Bao, Jie, 2018. "Quantitative risk assessment of freeway crash casualty using high-resolution traffic data," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 299-311.
    2. Golob, Thomas F. & Recker, Wilfred W., 2004. "A method for relating type of crash to traffic flow characteristics on urban freeways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 38(1), pages 53-80, January.
    3. Xiaodong Zhang & Jinliang Xu & Qianqian Liang & Fangchen Ma, 2020. "Modeling Impacts of Speed Reduction on Traffic Efficiency on Expressway Uphill Sections," Sustainability, MDPI, vol. 12(2), pages 1-12, January.
    4. Lord, Dominique & Mannering, Fred, 2010. "The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives," Transportation Research Part A: Policy and Practice, Elsevier, vol. 44(5), pages 291-305, June.
    5. Wu, Ning, 2002. "A new approach for modeling of Fundamental Diagrams," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(10), pages 867-884, December.
    6. Guangnian Xiao & Zihao Wang, 2020. "Empirical Study on Bikesharing Brand Selection in China in the Post-Sharing Era," Sustainability, MDPI, vol. 12(8), pages 1-16, April.
    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. Feng Chen & Xiaoxiang Ma & Suren Chen & Lin Yang, 2016. "Crash Frequency Analysis Using Hurdle Models with Random Effects Considering Short-Term Panel Data," IJERPH, MDPI, vol. 13(11), pages 1-11, October.
    2. Xu, Chengcheng & Liu, Pan & Wang, Wei & Li, Zhibin, 2014. "Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service," Transportation Research Part A: Policy and Practice, Elsevier, vol. 69(C), pages 58-70.
    3. Feng Chen & Suren Chen & Xiaoxiang Ma, 2016. "Crash Frequency Modeling Using Real-Time Environmental and Traffic Data and Unbalanced Panel Data Models," IJERPH, MDPI, vol. 13(6), pages 1-16, June.
    4. Najaf, Pooya & Thill, Jean-Claude & Zhang, Wenjia & Fields, Milton Greg, 2018. "City-level urban form and traffic safety: A structural equation modeling analysis of direct and indirect effects," Journal of Transport Geography, Elsevier, vol. 69(C), pages 257-270.
    5. 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.
    6. 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.
    7. Jiang, Jiwan & Zhou, Yang & Wang, Xin & Ahn, Soyoung, 2024. "On dynamic fundamental diagrams: Implications for automated vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    8. Dugan, Spencer August & Utne, Ingrid Bouwer, 2025. "Improved identification of maritime risk-influencing factors using AIS data in regression analysis," Reliability Engineering and System Safety, Elsevier, vol. 262(C).
    9. 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.
    10. Lv, Jinpeng & Lord, Dominique & Zhang, Yunlong & Chen, Zhi, 2015. "Investigating Peltzman effects in adopting mandatory seat belt laws in the US: Evidence from non-occupant fatalities," Transport Policy, Elsevier, vol. 44(C), pages 58-64.
    11. Ye, Wei & Xu, Yueru & Shi, Xiaomeng & Shiwakoti, Nirajan & Ye, Zhirui & Zheng, Yuan, 2024. "A macroscopic safety indicator for road segment: application of entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    12. Ruru Xing & Zimu Li & Xiaoyu Cai & Zepeng Yang & Ningning Zhang & Tao Yang, 2023. "Accident Rate Prediction Model for Urban Expressway Underwater Tunnel," Sustainability, MDPI, vol. 15(13), pages 1-28, July.
    13. Ulak, Mehmet Baran & Ozguven, Eren Erman & Spainhour, Lisa & Vanli, Omer Arda, 2017. "Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida," Journal of Transport Geography, Elsevier, vol. 58(C), pages 71-91.
    14. Ren, Qiaoqiao & Hu, Chengxuan & Xu, Min & Song, Jiatong, 2026. "Association rule mining of damage severities in autonomous vehicle rear-end crashes with supplementary built environment data," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    15. Miao, Congcong & Chen, Xiang & Zhang, Chuanrong, 2025. "Perceived built environment and non-motorist crashes: An exploration with street view imagery," Journal of Transport Geography, Elsevier, vol. 128(C).
    16. Milhan Moomen & Amirarsalan Mehrara Molan & Khaled Ksaibati, 2023. "A Random Parameters Multinomial Logit Model Analysis of Median Barrier Crash Injury Severity on Wyoming Interstates," Sustainability, MDPI, vol. 15(14), pages 1-17, July.
    17. Yan, Ying & Zhang, Ying & Yang, Xiangli & Hu, Jin & Tang, Jinjun & Guo, Zhongyin, 2020. "Crash prediction based on random effect negative binomial model considering data heterogeneity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 547(C).
    18. Hwachyi Wang & S. K. Jason Chang & Hans De Backer & Dirk Lauwers & Philippe De Maeyer, 2019. "Integrating Spatial and Temporal Approaches for Explaining Bicycle Crashes in High-Risk Areas in Antwerp (Belgium)," Sustainability, MDPI, vol. 11(13), pages 1-28, July.
    19. Sun, Chenshuo & Pei, Xin & Hao, Junheng & Wang, Yewen & Zhang, Zuo & Wong, S.C., 2018. "Role of road network features in the evaluation of incident impacts on urban traffic mobility," Transportation Research Part B: Methodological, Elsevier, vol. 117(PA), pages 101-116.
    20. Wu, Xuelian & Postorino, Maria Nadia & Mantecchini, Luca, 2024. "Impacts of connected autonomous vehicle platoon breakdown on highway," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:12:y:2020:i:18:p:7386-:d:410881. 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.