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Identification of freeway crash-prone traffic conditions for traffic flow at different levels of service

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  • Xu, Chengcheng
  • Liu, Pan
  • Wang, Wei
  • Li, Zhibin

Abstract

The primary objective of this study was to evaluate the risks of crashes associated with the freeway traffic flow operating at various levels of service (LOS) and to identify crash-prone traffic conditions for each LOS. The results showed that the traffic flow operating at LOS E had the highest crash potential, followed by LOS F and D. The traffic flow operating at LOS B and A had the lowest crash potential. For LOS A and B, the vehicle platoon and abrupt change in vehicle speeds were major contributing factors to crash occurrences. For LOS C, crash risks were correlated with lane-change maneuvers, speed variation, and small headways in traffic. For LOS D, crash risks increased with an increase in the temporal change in traffic flow variables and the frequency of lane-change maneuvers. For LOS E, crash risks were mainly affected by high traffic volumes and oscillating traffic conditions. For LOS F, crash risks increased with an increase in the standard deviation of flow rate and the frequency of lane-change maneuvers. The findings suggested that the mechanism of crashes were quite different across various LOS. A Bayesian random-parameters logistic regression model was developed to identify crash-prone traffic conditions for various LOS. The proposed model significantly improved the prediction performance as compared to the conventional logistic regression model.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:transa:v:69:y:2014:i:c:p:58-70
    DOI: 10.1016/j.tra.2014.08.011
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    References listed on IDEAS

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    Cited by:

    1. Yuntao Shi & Ye Li & Qing Cai & Hao Zhang & Dan Wu, 2020. "How Does Heterogeneity Affect Freeway Safety? A Simulation-Based Exploration Considering Sustainable Intelligent Connected Vehicles," Sustainability, MDPI, vol. 12(21), pages 1-18, October.
    2. 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).
    3. 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).
    4. Zhao, Jingya & Liu, Qingchao, 2024. "Quantitative causality assessment between traffic states and crash risk in freeway segments with closely spaced entrance and exit ramps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 637(C).
    5. Xiangyang Cao & Bingzhong Zhou & Qiang Tang & Jiaqi Li & Donghui Shi, 2018. "Urban Wasteful Transport and Its Estimation Methods," Sustainability, MDPI, vol. 10(12), pages 1-15, December.

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