Design of Intelligent Traffic Management System Based on LSTM Neural Network
With the acceleration of urbanization,traffic congestion has become increasingly severe,and traditional traffic management systems have struggled to meet the demands of modern traffic management.To enhance traffic efficiency,reduce congestion,and optimize travel experiences,this paper designs an intelligent traffic management system based on Long Short-Term Memory(LSTM)neural networks.This system aims to achieve precise prediction and intelligent regulation of traffic conditions through deep learning and big data analytics.The paper first constructs the overall framework of the intelligent traffic management system and then provides a detailed analysis of the design of each functional layer.In the business layer,the selection and optimization strategies of the LSTM neural network model are thoroughly discussed,including adjustments to the network structure and optimization of the loss function,to ensure that the model can accurately capture the temporal characteristics and nonlinear relationships in traffic flow data.Furthermore,experiments demonstrate that the system exhibits excellent stability and prediction accuracy,providing better support for intelligent traffic management.
Long Short-Term Memory(LSTM)neural networkintelligent traffic management systemtraffic flow prediction
万方数据
维普
