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摘要
In order to investigate the relationship between horizontal and vertical alignment indicators and accidents of second-class highway, a risk index model based on the back propagation (BP) neural network is constructed to identify the risk change analysis of accident sections and the risk section interval. By collecting historical accident data, we calculate the roadway accident rate under each alignment indicator, and based on the road accident rate, we perform risk ratings of the eight horizontal and vertical alignment indexes. By comprehensively considering the impact of each alignment indicator, we obtain the weight coefficients of each alignment index based on the BP neural network model. By combining the weight coefficient of alignment indicators and risk ratings, we establish the risk index model, and further analyze the horizontal and vertical linear risk index changes within the accident prone section of second-class highway. The results show that when the second-class highway is a two-lane highway in both directions with a design speed of 60 km/h, combined with the accident rate of the bi-directional road section, it can be seen that the road longitudinal slope of 3% has a higher safety; The accident risk section has a certain regularity, i. e., the section within 100 m before the accident staking point is the risk section leading to an accident, and the section within 200 m before the accident staking point is the accident potential risk section. The results of the study can provide theoretical support for the design of second-class highway alignment and the study of road accident black spots, and then optimize the design of road alignment, accurately identify the accident black spots, reduce road traffic accidents, and improve the quality of road safety. ? 2023 Editorial Office of Journal of Shenzhen University.
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