Traffic accident severity prediction method applied to regional road network

Abstract

The invention discloses a traffic accident severity prediction method applied to a regional road network. The method comprises the following steps: 1, acquiring and preprocessing regional road networktraffic accident data; 2, based on the regional road network traffic accident data, establishing a potential category analysis model; 3, respectively establishing a CART decision tree model for eachsub-category according to a potential category analysis result; and 4, respectively establishing an accident severity model (considering independent variables and interaction items) based on binary logistic regression for each subcategory, and taking intersection points of sensitivity and specificity curves as model prediction classification thresholds. According to the method, the adverse effectof accident data heterogeneity on an analysis result can be reduced, the problems that a traditional traffic accident severity prediction model ignores interaction items and the comprehensive prediction effect of unbalanced data is poor are solved, and the prediction precision and goodness of fit of the accident severity model are improved.

Description

technical field [0001] The invention relates to a traffic accident severity prediction method applied to a regional road network, and belongs to the technical field of road traffic safety analysis. Background technique [0002] According to the global road safety status report, road traffic accidents are the eighth leading cause of death in the world, causing more than 1.35 million deaths every year. Road traffic safety has gradually become a major focus of attention worldwide. Relying on the analysis of traffic accident data to determine the factors affecting the severity of accidents and propose countermeasures to reduce the risk of fatal accidents is one of the most practical measures to improve traffic safety. However, road traffic accidents are complex events involving various drivers' reactions to the external environment, as well as the interaction between vehicles, road conditions, traffic factors and environmental factors, and there may be unobserved factors affecti...

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiencies of the prior art, the present invention proposes a method for predicting the severity of traffic accidents applied to regional road networks, in order to reduce the adverse effects of accident data heterogeneity on analysis results and identify the interaction terms of independent variables And adjust the classification threshold of the prediction model, so as to overcome the problem of the traditional traffic accident severity prediction model ignoring the interaction term and the poor comprehensive prediction effect of unbalanced data, and improve the prediction accuracy and goodness of fit of the accident severity model

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