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Will higher traffic flow lead to more traffic conflicts? A crash surrogate metric based analysis

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  • Yan Kuang
  • Xiaobo Qu
  • Yadan Yan

Abstract

In this paper, we aim to examine the relationship between traffic flow and potential conflict risks by using crash surrogate metrics. It has been widely recognized that one traffic flow corresponds to two distinct traffic states with different speeds and densities. In view of this, instead of simply aggregating traffic conditions with the same traffic volume, we represent potential conflict risks at a traffic flow fundamental diagram. Two crash surrogate metrics, namely, Aggregated Crash Index and Time to Collision, are used in this study to represent the potential conflict risks with respect to different traffic conditions. Furthermore, Beijing North Ring III and Next Generation SIMulation Interstate 80 datasets are utilized to carry out case studies. By using the proposed procedure, both datasets generate similar trends, which demonstrate the applicability of the proposed methodology and the transferability of our conclusions.

Suggested Citation

  • Yan Kuang & Xiaobo Qu & Yadan Yan, 2017. "Will higher traffic flow lead to more traffic conflicts? A crash surrogate metric based analysis," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-11, August.
    • Handle: RePEc:plo:pone00:0182458
    DOI: 10.1371/journal.pone.0182458
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    References listed on IDEAS

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    1. Qu, Xiaobo & Wang, Shuaian & Zhang, Jin, 2015. "On the fundamental diagram for freeway traffic: A novel calibration approach for single-regime models," Transportation Research Part B: Methodological, Elsevier, vol. 73(C), pages 91-102.
    2. 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.
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    Cited by:

    1. 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).
    2. Ke Ji & Jinjun Tang & Min Li & Cheng Hu, 2023. "Distributed Traffic Control Based on Road Network Partitioning Using Normalization Algorithm," Sustainability, MDPI, vol. 15(14), pages 1-20, July.

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