@article{ref1,
title="Injury severity assessment of rear-end crashes via approaches based on generalized estimating equations",
journal="Canadian journal of civil engineering",
year="2022",
author="Wang, Chenzhu and Chen, Fei and Yu, Bin and Cheng, Jianchuan",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Rear-end crashes constitute the predominant type of crashes on highways and may lead to severe injuries and high property damage. Available statistical models primarily focus on injury severity and analyze potential factors that affect it. However, rear-end crashes may also be potentially correlated to vehicle, roadway, environmental, temporal, spatial, traffic, and crash characteristics. Additionally, unobserved heterogeneity regarding the effects may be present, which may be different in different crashes. In this context, multiple generalized estimating equation (GEE)-based models, developed using different working matrices and distributions, are proposed in this study to examine factors that affect injury severity. The proposed models account for both crash-related correlations and unobserved heterogeneity, thereby outperforming traditional models in terms of prediction accuracy. Among the explanatory variables considered in this study, the passenger car, minibus, curvature ratio, rainy weather, foggy weather, early morning, Thursday, autumn, winter, AADT were identified as contributing factors.<p /> <p>Language: en</p>",
language="en",
issn="0315-1468",
doi="10.1139/cjce-2022-0197",
url="http://dx.doi.org/10.1139/cjce-2022-0197"
}