# Integrated Spatio-Temporal Graph Neural Network for Traffic Forecasting
## 4 Experimental Analysis
### Dataset Description  
To validate the efficacy of our proposed model, we conducted experiments on three publicly available real-world traffic datasets: PeMSD7 and PeMSD8, which are collected by the Caltrans Performance Measurement System (PeMS) [9]. These datasets are widely recognized in traffic forecasting research and have been used for performance benchmarking in previous studies like STGCN [33], ASTGCN [34], and LSGCN [35]. We followed the data partitioning strategy used in LSGCN to divide the datasets into training and testing sets. A summary of the dataset statistics is provided in Table 1.  
* PeMSD7: This dataset consists of traffic speed data collected from 228 sensors in District 7 of California, covering weekdays from May to June 2012, with a 5 min time interval. We used the first month of data for training and the remainder for validation and testing. * PeMSD8: This dataset contains traffic data from San Bernardino, recorded between July and August 2016, with 170 detectors across 8 roads, also at 5 min intervals. The initial fifty days are used for training, while the subsequent data serve for validation and testing. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Statistics** & **PeMSD7** & **PeMSD8** \\ \hline No. of nodes & 220 & 170 \\ \hline No. of edges & 832 & 295 \\ \hline Timestamp & 12,672 & 17,856 \\ \hline Time interval & 5 min & 5 min \\ \hline Daily range & 00:00–24:00 & 00:00–24:00 \\ \hline \hline \end{tabular}  
* PeMSD7: This dataset consists of traffic speed data collected from 228 sensors in District 7 of California, covering weekdays from May to June 2012, with a 5 min time interval. We used the first month of data for training and the remainder for validation and testing.
* PeMSD8: This dataset contains traffic data from San Bernardino, recorded between July and August 2016, with 170 detectors across 8 roads, also at 5 min intervals. The initial fifty days are used for training, while the subsequent data serve for validation and testing.  
\end{table} 
Table 1: Road traffic dataset description. Figure 3: Traffic prediction using ISTGCN. WaveNet, which has been competitive in handling time-series data, ISTGCN excels in reducing both MAE and MAPE, providing more accurate short-term predictions (15 and 30 min) by better modeling spatial relations. In terms of accuracy, ISTGCN also surpasses ASTGCN, which uses an attention mechanism for spatio-temporal data. The lower RMSE and MAE of ISTGCN suggest that its architecture avoids potential overfitting and provides more consistent performance across various time frames. Finally, compared to DCRNN, an earlier recurrent neural network-based approach, ISTGCN significantly reduces errors in all metrics, highlighting the limitations of recurrent models in capturing long-range dependencies and the advantages of convolutional approaches in this domain. Overall, the ISTGCN model demonstrates its superiority through lower error values across all metrics, highlighting its robustness in handling complex traffic data. The significant reduction in RMSE, MAE, and MAPE confirms that ISTGCN offers more precise traffic predictions, effectively modeling both the spatial and temporal aspects of the data, thus making it the most reliable model for traffic forecasting compared to its peers. Table 2 evaluates the performance of various spatio-temporal graph learning models--ISTGCN, USTGCN, LSGCN, Graph WaveNet, ASTGCN, and DCRNN--on traffic prediction tasks using two datasets, PeMSD7 and PeMSD8. The models predict traffic at three different future time intervals (15, 30, and 45 min) and are assessed using three widely recognized error metrics: root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These metrics help measure how accurately the models predict traffic conditions, with lower values indicating better performance.