# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 3 Results
### Case Study: Real-Time Congestion Prediction and Police Deployment  
To validate the practical effectiveness of the proposed xLSTM-based congestion forecasting system, a case study was conducted using real-world traffic data from major intersections. The model's predictive outputs were compared against actual measurements across three critical traffic parameters: total vehicle volume, average vehicle speed, and lane occupancy rate. Figure 4 illustrates the comparison between the predicted and historical total traffic volume over a 2-day period. The xLSTM model successfully captures the periodic patterns and abrupt surges associated with peak-hour congestion events. Both weekday commuting peaks and weekend variations are effectively reflected, demonstrating the model's ability to adapt to diverse urban traffic rhythms. Figure 5 presents the model's prediction versus the ground truth for average vehicle speeds. Despite the higher volatility inherent in speed measurements, particularly \begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Model**} & \
\begin{tabular}{c} **Congestion** \\ **Accuracy (\%)** \\ \end{tabular} & \multirow{2}{*}{**F1-Score**} & \multirow{2}{*}{**MAE**} & \multirow{2}{*}{**RMSE**} &
\begin{tabular}{c} **Inference** \\ **Latency (ms)** \\ \end{tabular} \\ \hline LSTM & 81.4 & 0.824 & 0.142 & 0.188 & 67.9 \\ GRU & 82.1 & 0.832 & 0.137 & 0.182 & 54.7 \\ xGRU & 84.2 & 0.854 & 0.127 & 0.166 & 39.5 \\ ST-GCN & 85.7 & 0.864 & 0.125 & 0.161 & 123.6 \\ DCRNN & 86.2 & 0.869 & 0.122 & 0.157 & 141.2 \\ Informer & 83.9 & 0.841 & 0.132 & 0.171 & 228.4 \\ Autoformer & 85.1 & 0.858 & 0.128 & 0.164 & 195.6 \\ xLSTM & 87.3 & 0.882 & 0.116 & 0.149 & 41.2 \\ \hline \end{tabular}
\end{table} 
Table 1: Comparative performance of different models in short-term urban traffic congestion prediction. Figure 4: Comparison of predicted and actual total traffic volume over a 2-day period. The xLSTM model effectively captures peak and off-peak fluctuations. during congestion onset and dissipation, the predicted trends closely track the observed fluctuations. This indicates that the xLSTM model not only forecasts volume accumulation but also anticipates corresponding speed reductions--a critical indicator for early congestion detection. Figure 6 shows the lane occupancy rate predictions. Occupancy levels, often subject to sharp increases during bottleneck formation, are accurately predicted, including sudden congestion buildups and release phases. The model's ability to forecast these micro-level dynamics is essential for informing timely police intervention strategies. Overall, the results substantiate that the proposed xLSTM forecasting system achieves high fidelity in short-term congestion prediction across multiple traffic attributes, supporting its operational deployment for proactive patrol dispatch and traffic flow management. Notably, the observed shifts in traffic patterns after January 1 align with Taiwan's extended New Year holiday period (1-2 January). These temporal anomalies typically result in abrupt deviations in traffic flow, such as reduced vehicle volume during holidays and sharp rebounds afterward. Despite these irregularities, the xLSTM model effectively captured both the suppressed traffic conditions during holidays and the post-holiday surges, demonstrating its robustness under non-routine mobility patterns. Holiday and weekend intervals have been marked in Figures 4-6 for reference. To illustrate the practical utility of the xLSTM-based system in real-time congestion management, a forecasting scenario was analyzed on 16 May 2025. At 09:00, the observed traffic conditions indicated severe congestion, with an average speed of 28 km/h, an occupancy rate of 73%, and a total of 82 vehicles in the monitored segment (Figure 7). Figure 5: Predicted versus observed average vehicle speeds, highlighting the model’s ability to track congestion-induced speed variations in real time.