Roadway safety remains a critical issue due to the disproportionate number of crashes in rural areas. According to the National Highway Traffic Safety Administration (NHTSA), from 2007 to 2016, the fatality rate in rural roads has been more than two times higher than in urban areas. This discrepancy in fatalities reveals the urgency to improve the roadway safety conditions in the rural areas to achieve transportation equity. While most rural roadway safety related studies have focused on major facilities, few efforts have been made to investigate the crash injuries on the low-volume roads. This study investigated the potential influential factors, such as roadway geometric characteristics, environmental conditions, human behaviors, traffic conditions, etc., on the injury severity of crashes occurred on low-volume rural roads. Four models, including ordered probit (OP), multinomial logit (MNL), artificial neural network (ANN), and random forest (RF), were trained, tested, and validated using five years of Washington State crash records on low-volume rural roads from 2013 to 2017. It was found that the two machine learning models (ANN and RF) performed better than the two statistical models (OP and MNL), and the RF model had the best performance in predicting crash injury severities. The results also showed that variables such as grade percentage, degrees of curvature, shoulder width, drivers’ gender, roadway width, head on crash, pedestrian/cyclist involved, young driver, truck involved, etc. have significant impact on the crash injury severities on low-volume rural roads.
Identifying Factors Associated with Crash Injury Severities on Low-Volume Rural Roads Using Statistical and Machine Learning Methods
International Conference on Transportation and Development 2022 ; 2022 ; Seattle, Washington
2022-08-31
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Crash Characteristics, Severities and Injury Consequences
British Library Conference Proceedings | 1996
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