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Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis

Author

Listed:
  • Qiang Zeng

    (School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
    Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies, Nanjing 211189, China)

  • Wei Hao

    (School of Traffic and Transportation, Changsha University of Science and Technology, Changsha 410114, China)

  • Jaeyoung Lee

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Feng Chen

    (Key Laboratory of Road & Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

Abstract

This study presents an empirical investigation of the impacts of real-time weather conditions on the freeway crash severity. A Bayesian spatial generalized ordered logit model was developed for modeling the crash severity using the hourly wind speed, air temperature, precipitation, visibility, and humidity, as well as other observed factors. A total of 1424 crash records from Kaiyang Freeway, China in 2014 and 2015 were collected for the investigation. The proposed model can simultaneously accommodate the ordered nature in severity levels and spatial correlation across adjacent crashes. Its strength is demonstrated by the existence of significant spatial correlation and its better model fit and more reasonable estimation results than the counterparts of a generalized ordered logit model. The estimation results show that an increase in the precipitation is associated with decreases in the probabilities of light and severe crashes, and an increase in the probability of medium crashes. Additionally, driver type, vehicle type, vehicle registered province, crash time, crash type, response time of emergency medical service, and horizontal curvature and vertical grade of the crash location, were also found to have significant effects on the crash severity. To alleviate the severity levels of crashes on rainy days, some engineering countermeasures are suggested, in addition to the implemented strategies.

Suggested Citation

  • Qiang Zeng & Wei Hao & Jaeyoung Lee & Feng Chen, 2020. "Investigating the Impacts of Real-Time Weather Conditions on Freeway Crash Severity: A Bayesian Spatial Analysis," IJERPH, MDPI, vol. 17(8), pages 1-15, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:8:p:2768-:d:346722
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    References listed on IDEAS

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    1. Clifford Winston & Vikram Maheshri & Fred Mannering, 2006. "An exploration of the offset hypothesis using disaggregate data: The case of airbags and antilock brakes," Journal of Risk and Uncertainty, Springer, vol. 32(2), pages 83-99, March.
    2. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    3. Feng Chen & Mingtao Song & Xiaoxiang Ma, 2019. "Investigation on the Injury Severity of Drivers in Rear-End Collisions Between Cars Using a Random Parameters Bivariate Ordered Probit Model," IJERPH, MDPI, vol. 16(14), pages 1-12, July.
    4. 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.
    5. Huang, Helai & Song, Bo & Xu, Pengpeng & Zeng, Qiang & Lee, Jaeyoung & Abdel-Aty, Mohamed, 2016. "Macro and micro models for zonal crash prediction with application in hot zones identification," Journal of Transport Geography, Elsevier, vol. 54(C), pages 248-256.
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    Cited by:

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    2. Sheng Dong & Afaq Khattak & Irfan Ullah & Jibiao Zhou & Arshad Hussain, 2022. "Predicting and Analyzing Road Traffic Injury Severity Using Boosting-Based Ensemble Learning Models with SHAPley Additive exPlanations," IJERPH, MDPI, vol. 19(5), pages 1-23, March.
    3. Shuaiming Chen & Haipeng Shao & Ximing Ji, 2021. "Insights into Factors Affecting Traffic Accident Severity of Novice and Experienced Drivers: A Machine Learning Approach," IJERPH, MDPI, vol. 18(23), pages 1-20, December.
    4. Zhi Zhang & Yingshi Guo & Rui Fu & Wei Yuan & Chang Wang, 2020. "Linking executive functions to distracted driving, does it differ between young and mature drivers?," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-12, September.

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