# Dynamic Graph Convolutional Crowd Flow Prediction Model Based on Residual Network Structure
## 4 Experiments and Analysis
### Baseline Methods and Comparative Experiments  
To assess the practical performance of Res-DGCN, we compared it with traditional time series forecasting methods as well as some excellent graph convolutional networks. * HA [4]: This model refers to the Historical Average model, which predicts based on the historical average values. * ARIMA [26]: This model refers to the AutoRegressive Integrated Moving Average model, which is a forecasting model based on time series analysis. * STGCN [13]: This model is a neural network model based on spatio-temporal graph convolutions. It solely relies on historical inflow and outflow data for prediction and does not take into account OD flow and POI data. * DCRNN [27]: This model is based on a combination of recurrent neural networks and diffusion convolutional neural networks. Similar to STGCN, it only utilizes historical crowd flow data for prediction. * STGNN [28]: This model is another type of network model based on spatio-temporal graph convolutions. Unlike STGCN with gated convolutions, STGNN utilizes GRU and Transformer to capture short-term and global temporal dependencies for prediction. * MVGCN [10]: This model is a crowd flow prediction model based on multi-view graph convolutional neural networks. Unlike traditional single-view graph convolutional neural networks, MVGCN utilizes multiple views of spatio-temporal data to improve prediction accuracy. * 3D-DGCN [11]: This model is a crowd flow prediction model based on three-dimensional graph convolutional neural networks. It serves as the baseline model for the proposed model in this paper. The results of the comparative experiments in this paper are presented in Tables 2 and 3. In these tables, \(\Delta\) represents the percentage improvement in performance compared to the 3D-DGCN model. Table 2 shows that Res-DGCN performs better than the baseline methods and obtains the best results on both datasets. Traditional time series analysis methods exhibit lower predictive accuracy as they can only handle simple time series data and are unable to capture complex patterns and non-linear trends in time series, making it challenging to deal with the complex variations and noise interference in real-world applications. The deep learning-based methods, however, exhibit substantial advancements as they are all founded on graph neural networks for crowd flow prediction. What they have in common is that they all use graph neural networks to model spatial relationships and temporal relationships. In terms of the computational complexity of the model, Res-DGCN has a deeper network structure compared to 3D-DCGN, so the training time of the model becomes longer, but there is a greater improvement in prediction accuracy. Compared with the remaining baseline models, Res-DGCN has a greater advantage in terms of the number of parameters, which indicates that the model is feasible to be deployed in practical program applications. The comparison between our model's predicted values and the historical ground truth data is shown in Figure 6. It is clear from the graph that our model is sensitive to changes in the historical data. Although the predicted curve lags behind the true values to some extent, it effectively fits the true value curve in a timely manner. The predicted results of our model are closer to the historical ground truth values compared to the 3D-DGCN model. During the time points of 12:00 and 16:00, the real data exhibit peak periods corresponding to commuting hours, but the predicted curve of the 3D-DGCN model fails to capture this characteristic and does not show significant upward and downward trends. In contrast, the performance of Res-DGCN is better. This is because the proposed conditional convolutional module in this paper can effectively learn the temporal correlations among neighboring time steps. It can dynamically generate convolutional kernels based on the input conditions, enabling the model to adaptively learn feature representations under different input conditions.