Study of Roadside Accident Prediction of a Multibridge and Multitunnel Section on an Expressway in a Mountainous Area

In order to ensure the roadside traffic safety of the multi-bridge and multi-tunnel section on the expressway in mountainous areas and optimize the roadside traffic facilities, and reduce the road traffic accidents caused by the over-frequency changes of the driving environment among the bridge group and tunnel group and their intervals, combining with the driving rules, the prediction model of the numbers of roadside accidents, passenger and freight vehicle accidents in multi-bridge and multi-tunnel section on the expressway in mountainous areas is established by using statistics, machine learning and other related theories. In order to analyze the influence of driving environment of expressway on drivers' visual, psychological and operational characteristics, 10 prediction indicators are selected from the aspects of road alignment, traffic structure, traffic environment and weather condition. The action mechanism between roadside accidents and 10 predictive indicators is explained by employing Spearman correlation analysis. The roadside accident prediction model based on BPNN, GA-BPNN and PSO-BPNN are established. MAE, RMSE and MAPE are used as the model evaluation indicators to select the optimal model. The roadside accident data including rollover, side collision and collision with fixed objects are verified by examples by using the accident patterns of Chongqing-Hunan Expressway in the past 5 years. The result shows that (1) the roadside accidents of the roadside accidents of multi-bridge and multi-tunnel section on expressway in mountainous area are comprehensively affected by 10 prediction indicators, which are positively correlated with the length of road section, the proportion of curved roads and the proportion of bridges, and the influence of length of road section is the greatest; (2) compared with BPNN and GA-BPNN prediction models, the errors of MAE, RMSE, and MAPE of PSO-BPNN are reduced by 18. 5%, 17. 65%, and 24. 16% on average, and the model prediction error is smaller and the accuracy is higher; (3) the accurate prediction of the number of roadside accidents and the number of passenger and freight vehicle accidents can provide effective decision-making support for the optimization design of roadside facilities.