## 6 Discussion
### Discussion: RNNs vs. Other Prediction Models
Additionally, these models do not inherently incorporate mechanisms for analyzing spatial relationships and require complex data preprocessing or feature engineering to include this information indirectly. Considering the aspect of model interpretability, traditional machine learning models have relatively simple algorithms and fewer parameters, making their decision processes easy to understand. The outputs of these models are easily interpretable, aiding in the analysis and validation of prediction results' reasonableness. In contrast, RNNs are less intuitive compared to traditional machine learning models. As shown in Table 4, the experimental results using the AFC dataset reveal that the prediction performance of the ARIMA model, representing statistical learning models, and the SVR model, representing traditional machine learning, do not have an advantage over RNNs. Finally, we discuss TCN models, which have shown excellent performance in time series forecasting compared to RNNs and their variants and other deep learning models. Earlier, we outlined the fundamental characteristics of these two types of models. Given that TCN's development occurs later than RNNs, we now discuss the specific features of TCNs in the context of traffic prediction compared to RNNs. Firstly, TCN's advantage lies in its parallel processing capability. In contrast to RNNs, where predicting the next time step requires waiting for the computation of the previous step to complete, TCNs can process the entire sequence in parallel. This makes TCNs more efficient in handling long-time series traffic data, enabling quick responses to changes in traffic flow and making it suitable for real-time or near-real-time traffic prediction systems. TCNs can also flexibly adjust their receptive field by tuning the number of dilated convolution layers, dilation factors, or filter sizes, allowing them to adapt to different lengths of historical data. Different traffic scenarios require varying lengths of historical data for effective prediction. Regarding gradient stability, TCNs avoid the problem of exploding or vanishing gradients compared to RNNs. This is because TCN's backpropagation is independent of the time direction. This characteristic enhances the stability of model training, which is particularly crucial for traffic data scenarios that often involve sudden events and non-linear changes. Additionally, when handling long input sequences, TCNs are more efficient than RNNs and their variants, like LSTM and GRUs. This efficiency stems from TCN's shared filters across layers, where the complexity of backpropagation is mainly determined by the network depth, making it feasible to handle large-scale traffic data in resource-constrained environments. In terms of inputting traffic data, TCNs and RNNs can handle input sequences of arbitrary lengths, allowing flexibility in dealing with different situations in traffic data. However, TCNs have some notable drawbacks. For instance, during the testing phase, TCNs require storing sufficient historical data to generate predictions. In contrast, RNNs only need to maintain hidden states and receive the current input to create predictions. Consequently, TCNs may require more memory during testing. Moreover, when transitioning from an application scenario with lower historical data requirements to one with greater historical data demands, TCNs may need to increase their receptive field size to adapt to the new requirements. This may entail significant adjustments to the model architecture, increasing the complexity of model tuning.