Study on traffic share in historical districts based on BP neural network
In order to improve the methods of traffic planning in historical and cultural blocks,the characteristics of travelers,travel features,traffic characteristics,and vehicle attributes are used as the input data of the Back-Propagation(BP)neural network to establish a travel mode choice model based on the traditional Bayesian Regularization algorithm.Through empirical analysis of six historical and cultural blocks in Zhuhai city,the study found that the accuracy of the predicted values after sample training based on the traditional BP neural network is high,and the overall average error of the traffic share rate comparison in various regions is about 1.02％,which basically matches the actual measurement values from field surveys.The research results show that the traffic prediction model based on the traditional BP neural network is effective in historical blocks and can provide certain theoretical support for the traffic planning of historical blocks.
万方数据
维普
