A Study on Multifactorial Prediction Model for the Road Traffic Accidents

Disharmony of various factors in road system is one of important reasons that cause traffic accidents. In order to study how traffic accidents are influenced by various road factors, it is required to transform traditional linear prediction models into non-linear prediction ones. Considering above-mentioned problems, multifactorial model for road traffic accident prediction based on Grey-Radial Basis Function (RBF) neural networks is put forward by combining virtues of Grey theory that aim at handling "poor" information and weaken undulation of data sequence together with the superior capability of neural networks of adapting non-linear information. Finally, the results of a case study demonstrate that the combined model can effectually overcome the shortcoming that current prediction models only consider influence of one factor on the traffic accidents. The proposed models can also improve efficiency of system modeling and precision of the prediction.