Traffic Flow Prediction Model Based on the Fusion of Timedomain Convolutional Network and Long- and Short-term Memory Network

This paper proposes a traffic flow prediction model based on the fusion of time-domain convolutional network (TCN) and long-short-term memory network (LSTM), and verifies its effectiveness through multi-dimensional experiments. To address the complexity of urban traffic flow prediction, the study constructs a TCN-LSTM hybrid model to fuse temporal feature extraction and long and short-term dependency capturing capabilities, and performs prediction validation on three types of traffic flow datasets, namely, cars, bicycles and trucks, respectively. The experimental results show that: in car traffic prediction, the training loss value of the model decreases significantly from the initial 0.8 to less than 0.1, and the R of the training set and the test set reaches 0.73 and 0.75, respectively, which reflects good convergence and generalisation ability; bicycle traffic prediction shows that the R of the training set reaches as high as 0.84 but the R of the test set decreases to 0.31, which shows that there is a certain degree of overfitting phenomenon; truck Truck traffic prediction achieves a balanced performance of R 0.73 for the training set and R 0.50 for the test set, which verifies the model's ability to capture heavy vehicle traffic patterns robustly. By comparing the performance differences between TCN, LSTM and their hybrid models through ablation experiments, it is found that TCN-LSTM is superior in key indicators: the mean absolute error (MAE) is 0.11 lower than that of the pure TCN model, the mean squared error (MSE) is in between that of TCN and LSTM, and the relative prediction deviation (RPD) reaches the highest value of the three at 2.15, which is a good proof that the hybrid model combines both multi-scale capturing ability of time-series features and the advantage of accurate modelling of nonlinear relationships.