Accident; analysis and prevention
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In this study, we attempted to explain drivers' crash risk variation in car-following for crash avoidance considering the effects of drivers' visual perception, vehicle type, and horizontal curves, with a structural equations model. The model was built by incorporating drivers' speed risk perception and distance risk perception as latent variables. A series of on-road experiments was conducted on the curved segments of a freeway in China to collect naturalistic driving data to approximate the model. ⋯ In addition, the difference between the effect of speed risk perception and distance perception on crash risk variation was discussed considering the direct and indirect origins of risk in driving. The findings suggests that the incorporation of visual perceptual, vehicular, and roadway factors and its relevant speed risk perception and distance risk perception can better explain the crash risk in car-following. This study also emphasized the possibility and the need of applying the line markings as a visual intervention to prevent the drivers from rear-end crashes on curves, which may provide new insights and be a new solution for roadway safety.