A Meso-Level Analysis of Factors Contributing to Freeway Crashes on Weekdays and Weekends in China

Guangyu Liu, Shaohua Wang, Qiang Zeng () and Xiaofei Wang ()
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Guangyu Liu: Reconstruction and Expansion Management Office of Zhongjiang Highway, Jiangmen 529000, China
Shaohua Wang: Reconstruction and Expansion Management Office of Zhongjiang Highway, Jiangmen 529000, China
Qiang Zeng: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China
Xiaofei Wang: School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510641, China

Sustainability, 2023, vol. 15, issue 18, 1-9

Abstract: This paper presents an empirical investigation of the factors contributing to freeway crashes on weekdays and weekends, using a Bayesian spatial logistic model. The crash data from Kaiyang Freeway, China, in 2014 are used for the empirical investigation. The deviation information criterion (DIC) values indicate that the proposed spatial logistic model is clearly superior to a logistic model in analyzing weekday and weekend crashes. Additionally, significant spatial effects are found in adjacent freeway segments for both weekday and weekend crashes, which demonstrate the reasonableness of the proposed model. The results of parameter estimation suggest that: traffic volume, roadway segment length, and the proportions of vehicles in Classes 2 and 4 have significant effects on weekday and weekend crash incidences in the same direction; horizontal curvature, presence of a ramp, and average daily precipitation impact weekday crash incidence only; and the proportion of vehicles in Class 3 and vertical grade impact weekend crash incidence only. Some countermeasures from the perspectives of roadway design and traffic management have been proposed to reduce freeway crashes on weekdays and weekends, respectively.

Keywords: freeway crash; weekday and weekend; spatial logistic model; Bayesian estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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