With the acceleration of urbanization,the traffic congestion problem of urban arterial roads is becoming more and more serious.Deep learning technology has become an important tool for studying traffic situation due to its advantages in processing complex data.This paper discusses the application of deep learning models driven by multi-source data(such as traffic flow,weather,social activities,etc.)in the study of urban arterial road traffic situation.Through the analysis and experimental verification of existing methods,this paper shows how deep learning can improve the accuracy of traffic prediction and optimize traffic management strategies,so as to alleviate the pressure of urban traffic.
维普
万方数据