Abstract
To predict the probability of roadside accidents for curved sections on highways, we chose eight risk factors that may contribute to the probability of roadside accidents to conduct simulation tests and collected a total of 12,800 data obtained from the PC-crash software. The chi-squared automatic interaction detection (CHAID) decision tree technique was employed to identify significant risk factors and explore the influence of different combinations of significant risk factors on roadside accidents according to the generated decision rules, so as to propose specific improved countermeasures as the reference for the revision of the Design Specification for Highway Alignment (JTG D20-2017) of China. Considering the effects of related interactions among different risk factors on roadside accidents, path analysis was applied to investigate the importance of the significant risk factors. The results showed that the significant risk factors were in decreasing order of importance, vehicle speed, horizontal curve radius, vehicle type, adhesion coefficient, hard shoulder width, and longitudinal slope. The first five important factors were chosen as predictors of the probability of roadside accidents in the Bayesian network analysis to establish the probability prediction model of roadside accidents. Eventually, the thresholds of the various factors for roadside accident blackspot identification were given according to probabilistic prediction results.
Highlights
Roadside accidents occur when a vehicle leaves the travel line, crosses an edge line or a centre line, collides with trees, guardrails, utility poles, and other natural or man-made objects located on roadsides, or overturns or falls into deep ditches or rivers
In terms of roadway characteristics, a wider shoulder has been found to decrease the occurrence of ROR accidents on horizontal curves [3, 5], but the increase of the shoulder width is associated with an increasing vehicle operating speed [6]. e frequency of ROR accidents will increase if vehicles travel in a narrower lane because the requirement for sharing the roadway with other vehicles increases the chance of conflicts, whereas driveway density has little impact on ROR accidents [3, 5]
We employed chi-squared automatic interaction detection (CHAID) decision tree analysis to identify significant risk factors resulting in the occurrence of roadside accidents, explored the impact of different combinations of risk factors on roadside accidents, and used path analysis to determine the importance of these significant risk factors by investigating their direct and indirect effects on roadside accident occurrence
Summary
Roadside accidents occur when a vehicle leaves the travel line, crosses an edge line or a centre line, collides with trees, guardrails, utility poles, and other natural or man-made objects located on roadsides, or overturns or falls into deep ditches or rivers. In terms of human factors, the National Highway Traffic Safety Administration (NHTSA) suggested that driver distraction, fatigue, driver’s degree of familiarity with the roadway, blood alcohol presence, age, and gender were the most significant factors contributing to roadside accidents [18], and 30% of these accidents occurred due to driver inattention [8,9,10] All of these factors have a direct or indirect effect on changes in vehicle speeds, and the risk of accidents increases, followed by an increase in vehicle speeds. We chose highway geometric design indexes (horizontal curve radius, hard shoulder width, longitudinal slope, superelevation slope, and width value of the curve), pavement condition (adhesion coefficient), and traffic characteristics (vehicle speed and vehicle type) as input variables, and vehicle final states as the output variable. Length (m) Width (m) Height (m) Wheelbase (m) Weight (kg) Height of centre of gravity (m) Distance of height of centre of gravity from front axle (m) Tyre pattern ABS ESP
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