Traffic Flow Prediction Model Based on Temporal Convolutional Network

Traffic flow prediction aims to predict the future state of the road network based on historical data and other information in the traffic network, which helps users to take effective countermeasures in advance, and is one of the important methods to solve traffic problems. However, the task of traffic flow prediction is extremely challenging due to the complex non-linear spatial and temporal correlation of traffic flow data. To explore the complex correlation of traffic data on traffic road networks and predict traffic flow accurately and timely, this paper adds the gating mechanism into the temporal convolutional network TCN, constructing a gated TCN model to investigate the temporal correlation of traffic data and finally tests the performance of the proposed model on a real traffic speed dataset. The results show that the model has the best prediction accuracy and can well capture the sudden changes of traffic sudden changes in speed and stable prediction results.