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原文传递 Predicting Drivers' Injury Occurrence in Vehicle Crashes Occurring at Horizontal Curve and Grade Combination Segments

题名：	Predicting Drivers' Injury Occurrence in Vehicle Crashes Occurring at Horizontal Curve and Grade Combination Segments
正文语种：	eng
作者：	NORRIS NOVAT;SIA MWENDE;HELLEN SHITA;GRACE MBIAJI
作者单位：	Cleveland State University Ohio;Western Michigan University;Tennessee State University (TSU);South Carolina State University
摘要：	Previous research shows that most crashes on horizontal curves occur when drivers are speeding or distracted. Also, the magnitudes of radius of adjacent horizontal curves and tangent lengths between curve were associated with departure crashes.6 Hummer et al., identified these crashes as associated with presence of fixed objects, such as trees and poles; influence of alcohol on drivers; and lighting and roadway surface conditions. Other studies show a higher number of crashes occur at curved segments than tangent segments, and more crashes occur at two-lane than multilane roads. Severity of injuries caused by these crashes varies with radius and length of the curves, where curve radius is negatively correlated with occurrence of crashes. Authors identified 2,500 feet (762 meters) as threshold curve radius below which rate of crash occurrence increases.10 Rush et al., reported that steep gradients and sharp curves along rolling terrain roads induce different driving behavior than curved roads in flatter areas.In light of this review, there remain significant gaps where research is warranted. Most studies in the literature either look at curved or grade alignments; few studies have analyzed safety factors of segments with a combination of the two. Moreover, most of these studies used traditional statistical models which are limited by overfitting and poor performance on non-linear relationship of variables. In addressing these limitations, current study investigates effects of curve and grade combination segments, with other traffic characteristics, in predicting drivers' injury severity. This is achieved by a comparative assessment of robust predictive machine learning models (Random Forest and XGBoost). This study addresses an important road safety issue of crashes along curved and at-grade segments. Road engineers often face challenges in designing and ensuring adequate cross-sectional elements due to constrained road reserves along curved and at-grade roads. A proper understanding of these risk factors in predicting severity of a driver's injury is needed for this reason. Development of an accurate driver's injury severity prediction model along curved and at-grade highways using crash data from Highway Safety Information System (HSIS) for Ohio is therefore of essence.
出版年：	2022
期刊名称：	ITE journal
卷：	92
期：	8
页码：	41-50