title: Analysis of work zone rear-end crash risk for different vehicle-following patterns.
abstract: This study evaluates rear-end crash risk associated with work zone operations for four different vehicle-following patterns: car-car, car-truck, truck-car and truck-truck. The deceleration rate to avoid the crash (DRAC) is adopted to measure work zone rear-end crash risk. Results show that the car-truck following pattern has the largest rear-end crash risk, followed by truck-truck, truck-car and car-car patterns. This implies that it is more likely for a car which is following a truck to be involved in a rear-end crash accident. The statistical test results further confirm that rear-end crash risk is statistically different between any two of the four patterns. We therefore develop a rear-end crash risk model for each vehicle-following pattern in order to examine the relationship between rear-end crash risk and its influencing factors, including lane position, the heavy vehicle percentage, lane traffic flow and work intensity which can be characterized by the number of lane reductions, the number of workers and the amount of equipment at the work zone site. The model results show that, for each pattern, there will be a greater rear-end crash risk in the following situations: (i) heavy work intensity; (ii) the lane adjacent to work zone; (iii) a higher proportion of heavy vehicles and (iv) greater traffic flow. However, the effects of these factors on rear-end crash risk are found to vary according to the vehicle-following patterns. Compared with the car-car pattern, lane position has less effect on rear-end crash risk in the car-truck pattern. The effect of work intensity on rear-end crash risk is also reduced in the truck-car pattern.
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China. Electronic address: jxweng@bjtu.edu.cn.
authors.forename: Jinxian
authors.initials: J
authors.lastname: Weng
authors.affiliation: Department of Civil and Environmental Engineering, National University of Singapore, 117576 Singapore, Singapore.
authors.forename: Qiang
authors.initials: Q
authors.lastname: Meng
authors.affiliation: MOE Key Laboratory for Urban Transportation Complex Systems Theory and Technology, Beijing Jiaotong University, Beijing 100044, China.
authors.forename: Xuedong
authors.initials: X
authors.lastname: Yan
mesh_terms: D000063:Accidents, Traffic / Q000706:statistics & numerical data*; D001334:Automobile Driving / Q000706:statistics & numerical data*; D001336:Automobiles / Q000706:statistics & numerical data; D006801:Humans; D008962:Models, Theoretical; D018986:Motor Vehicles / Q000706:statistics & numerical data; D012307:Risk Factors; D017132:Workplace / Q000706:statistics & numerical data
keywords: Rear-end crash; Vehicle-following pattern; Work intensity; Work zone