Relationship between Highway Geometric Characteristics and Accident Risk: A Multilayer Perceptron Model (MLP) Approach

Abstract

The traffic safety of mountain highway has always been one of the taking point. This study aims to collect road design data in large-scale research and analyzes the accident risk of highway geometric alignment. Accordingly, a method based on satellite maps and clustering algorithms is proposed to calculate the geometric alignment of the highway plane and its longitudinal section. The reliability of the method was verified on Nanfu highway in Chongqing, China. The planar and longitudinal sectional geometries of the four highways in Chongqing were obtained by the above method, and the corresponding 36,439 traffic accidents which occurred from 2010 to 2016 were used as the research objects. The accident risk of the highway geometry was analyzed based on the SHAP and MLP theories. The results show that the fitting and prediction abilities of the MLP model are better than those of the negative binomial model, and its correlation coefficient is improved by 33.2%. In addition, compared with the negative binomial model, the MLP model can estimate more accurately and flexibly the complex nonlinear relationship between the independent and the dependent variables.