GRBF: A Neural Network Model for Evaluating the Impact of Community Opening on the Surrounding Road Traffic

Abstract

With the rapid development of economy and the urbanization of population continue to increase, government management department promotes opening community in order to improve traffic conditions. In view of the shortcomings of the existing methods, this paper puts forward the GRBF neural network model. First of all, this paper uses analytic hierarchy process to establish a multi-index evaluation system. Secondly, a generalized radial basis function (GRBF) neural network based on geometric knowledge is proposed to evaluate the impact of community opening on surrounding road traffic by using neighborhood heuristic algorithm and recursive least square algorithm. Evaluation indicators and data were derived from surveys of different social classes in the community. Therefore, the analysis results can prove the impact of community opening on road traffic, and three representative communities are selected as examples to explain the advantages of the model in this paper. The experimental results show that GRBF model is better than other similar methods in evaluating the impact of community opening on road traffic, and is more operable. The research results can provide more valuable reference for the government to improve the traffic environment.