Application analysis of highway traffic accident risk model based on geographically weighted negative binomial regression

Abstract

The traditional generalized linear model (GLM) can not effectively analyze discrete road traffic accidents when analyzing road traffic accidents with spatial dependence and heterogeneity. Therefore, a risk analysis method of highway traffic accidents based on geographically weighted negative binomial regression model (GWNBR) is proposed. Using geographical weighted regression (GWR) model and negative binomial regression (NB) model, this paper makes a comparative analysis of highway traffic accidents in Xi’an, including local spatial geographical weighted Poisson regression (GWPR) model and two geographical weighted negative binomial regression (GWNBRg and GWNBR) models. The corresponding model bandwidth is determined, and the performance of the model is compared based on the data of traffic environment, road characteristics, crowd characteristics and road alignment. The experimental results show that compared with the single NB model, the proposed model can effectively reduce the interference of the spatial nonstationarity of the data, and can effectively extract the risk factors affecting the accident. The coefficients of GWNBRg model and GWNBR model are positive, which are better than GLM in the mean and likelihood of the residuals. The spatial autocorrelation of the residuals is significantly reduced, and the significance level is 5%, which reduces the spatial heterogeneity of the data. The over dispersion parameter value of GWNBRg model shows a downward trend from southwest to northeast in space, which can effectively reflect the spatial relationship between traffic flow and accident rate, indicating that GWNBR model has a good effect in traffic accident risk analysis of super discrete highway. Therefore, the application of geographical weighted negative binomial regression to highway traffic accident risk prediction has a good application effect, and can effectively reduce the probability of accidents as safety prevention and early warning.