Review of Time Series Traffic Forecasting Methods

Traffic forecasting is an important part of the smart transportation system, and accurate traffic forecasting is crucial for urban traffic scheduling and public travel planning. The traffic forecasting problem is greatly affected by the time dimension, and it is of great significance to investigate and summarize the related methods of time series traffic forecasting. Aiming at the problem of time series traffic forecasting, this paper focuses on the existing time series traffic forecasting models based on deep learning, and studies and analyzes the application fields and structural characteristics of different forecasting models. Finally, the current mainstream traffic prediction datasets are introduced, and the main challenges and solutions in the current traffic prediction field are discussed, which provides a reference for solving the problem of intelligent traffic prediction.