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Application of a random effects negative binomial model to examine crash frequency for freeways in China

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  • Hou, Qinzhong
  • Meng, Xianghai
  • Leng, Junqiang
  • Yu, Lu

Abstract

The freeway system of China is the longest in the world and planned to expand in the following years. However, the safety conditions of freeways are drawing increasing concerns both from the authorities and the public in the country. The safety design and management for freeways are urgently needed considering the current safety situation and great demands of new freeways. The study presented in this paper thoroughly investigated factors affecting safety using detailed data of crashes, traffic characteristics and freeway geometry. A random effects negative binomial (RENB) model was applied to account for spatial variations within groups together with a negative binomial (NB) model. The results indicated a better goodness of fit of RENB model than the NB model. In addition, a good number of factors significantly contributed to crash, such as truck proportion, presence of climbing lane, median barrier offset, curvature and longitudinal grade, were identified. This study was expected to provide a better understanding of how traffic condition and freeway design affect safety and should be useful to freeway engineers to design safe freeways, or develop effective safety countermeasures.

Suggested Citation

  • Hou, Qinzhong & Meng, Xianghai & Leng, Junqiang & Yu, Lu, 2018. "Application of a random effects negative binomial model to examine crash frequency for freeways in China," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 937-944.
    • Handle: RePEc:eee:phsmap:v:509:y:2018:i:c:p:937-944
    DOI: 10.1016/j.physa.2018.06.088
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    Cited by:

    1. Wen Cheng & Fei Ye & Changshuai Wang & Jiping Bai, 2023. "Identifying the Factors Contributing to Freeway Crash Severity Based on Discrete Choice Models," Sustainability, MDPI, vol. 15(3), pages 1-18, January.
    2. Xu, Chengcheng & Xu, Shuoyan & Wang, Chen & Li, Jing, 2019. "Investigating the factors affecting secondary crash frequency caused by one primary crash using zero-inflated ordered probit regression," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 121-129.
    3. Medury, Aditya PhD & Vlachogiannis, Dimitris & Grembek, Offer PhD, 2020. "Assessing the Variation of Curbside Safety at the City Block Level," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt46n9669d, Institute of Transportation Studies, UC Berkeley.
    4. Zeng, Qiang & Wen, Huiying & Huang, Helai & Wang, Jie & Lee, Jinwoo, 2020. "Analysis of crash frequency using a Bayesian underreporting count model with spatial correlation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 545(C).
    5. 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.
    6. Hou, Qinzhong & Meng, Xianghai & Huo, Xiaoyan & Cheng, Yuxing & Leng, Junqiang, 2019. "Effects of freeway climbing lane on crash frequency: Application of propensity scores and potential outcomes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 246-256.

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