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Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation

Author

Listed:
  • Zhenggan Cai

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Fulu Wei

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China
    Center for Urban Transportation Research, University of South Florida, Tampa, FL 33620, USA)

  • Zhenyu Wang

    (Center for Urban Transportation Research, University of South Florida, Tampa, FL 33620, USA)

  • Yongqing Guo

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Long Chen

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

  • Xin Li

    (School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China)

Abstract

Accident analysis and prevention are helpful to ensure the sustainable development of transportation. The aim of this research was to investigate the factors associated with the severity of low-visibility-related rural single-vehicle crashes. Firstly, a latent class clustering model was implemented to partition the whole-dataset into a relatively homogeneous sub-dataset. Then, a spatial random parameters logit model was established for each dataset to capture unobserved heterogeneity and spatial correlation. Analysis was conducted based on the crash data (2014–2019) from 110 two-lane road segments. The results show that the proposed method is a superior crash severity modeling approach to accommodate the unobserved heterogeneity and spatial correlation. Three variables—seatbelt not used, motorcycle, and collision with fixed object—have a stable positive correlation with crash severity. Motorcycle leads to a 12.8%, 23.8%, and 12.6% increase in the risk of serious crashes in the whole-dataset, cluster 3, and cluster 4, respectively. In the whole-dataset, cluster 2, and cluster 3, the risk of serious crashes caused by seatbelt not used increased by 5.5%, 0.1%, and 30.6%, respectively, and caused by collision with fixed object increased by 33.2%, 1.2%, and 13.2%, respectively. The results can provide valuable information for engineers and policy makers to develop targeted measures.

Suggested Citation

  • Zhenggan Cai & Fulu Wei & Zhenyu Wang & Yongqing Guo & Long Chen & Xin Li, 2021. "Modeling of Low Visibility-Related Rural Single-Vehicle Crashes Considering Unobserved Heterogeneity and Spatial Correlation," Sustainability, MDPI, vol. 13(13), pages 1-24, July.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:13:p:7438-:d:587539
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    References listed on IDEAS

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    1. 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).
    2. Buddhavarapu, Prasad & Scott, James G. & Prozzi, Jorge A., 2016. "Modeling unobserved heterogeneity using finite mixture random parameters for spatially correlated discrete count data," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 492-510.
    3. Yanyong Guo & Yao Wu & Jian Lu & Jibiao Zhou, 2019. "Modeling the Unobserved Heterogeneity in E-bike Collision Severity Using Full Bayesian Random Parameters Multinomial Logit Regression," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
    4. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
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    Cited by:

    1. Masayoshi Tanishita & Yuta Sekiguchi, 2023. "Impact Analysis of Road Infrastructure and Traffic Control on Injury Severity of Single- and Multi-Vehicle Crashes," Sustainability, MDPI, vol. 15(17), pages 1-17, September.
    2. Fulu Wei & Danping Dong & Pan Liu & Yongqing Guo & Zhenyu Wang & Qingyin Li, 2022. "Quarterly Instability Analysis of Injury Severities in Truck Crashes," Sustainability, MDPI, vol. 14(21), pages 1-23, October.

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