IJERPH, Vol. 18, Pages 5271: Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

IJERPH, Vol. 18, Pages 5271: Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes International Journal of Environmental Research and Public Health doi: 10.3390/ijerph18105271 Authors: Xiuguang Song Rendong Pi Yu Zhang Jianqing Wu Yuhuan Dong Han Zhang Xinyuan Zhu Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of t...
Source: International Journal of Environmental Research and Public Health - Category: Environmental Health Authors: Tags: Article Source Type: research