Design of Intelligent Transportation System Based on Neural Network

Through indepth study of the system architecture, the basic principles and optimization methods of recurrent neural networks in the field of intelligent transportation, this paper constructs a framework of intelligent transportation management system integrating data collection, processing and predictive analysis. The system can not only perceive the city's traffic status in real time, but also accurately predict future traffic conditions based on the LSTM model. It effectively solves the gradient problem of traditional standard RNN methods on long sequence data and improves the stability of the model. In the future, it is hoped that relevant technical personnel will continue to optimize and improve the system architecture and algorithm model, and promote the development of intelligent traffic management system in a more intelligent and accurate direction.