Research and Application of Traffic Flow Prediction in Chengdu City Based on Neural Network Algorithms

Abstract

As urban traffic systems increase in complexity, accurately predicting traffic flow has become a critical task in traffic management and planning. This paper employs neural network technologies, specifically Graph Convolutional Networks (GCN) and Long Short-Term Memory networks (LSTM), to develop a traffic flow prediction model for Chengdu that incorporates spatio-temporal characteristics. Through empirical analysis, the model demonstrates high accuracy and good generalization capability, effectively predicting and analyzing traffic flow at different times and regions. Additionally, this study explores the application of the prediction model in traffic law regulation and policy formulation, providing data support and scientific basis for advancing smarter traffic systems.