# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 4 Discussion  
This study demonstrates the feasibility and effectiveness of integrating an xLSTM-based forecasting model into real-time urban traffic governance systems. Through empirical  
Figure 8: Forecasted traffic condition at 16:20, predicting congestion relief with increased speed and reduced vehicle density.  
Figure 7: Observed traffic congestion at 15:50, with low speed, high occupancy, and vehicle accumulation.  
evaluation using real-world vehicle detector (VD) and CCTV data, the proposed framework achieves high predictive accuracy (over 87% in congestion classification) and low inference latency (approximately 41 ms per instance). These outcomes substantiate the system's operational viability for proactive police deployment and dynamic traffic management.  
Compared to conventional LSTM, GRU, and more recent Transformer-based or graph neural network (GNN) models, the xLSTM architecture offers a distinctive balance between predictive precision and computational efficiency. While Transformer and GNN approaches often achieve strong global feature modeling, they impose substantial computational demands, which can hinder real-time edge deployment in resource-constrained environments such as police dispatch centers. By contrast, xLSTM's lightweight yet expressive design, enhanced by matrix-based memory cells and exponential gating, supports scalable forecasting without sacrificing responsiveness. Inspired by GRU's parameter efficiency, the xLSTM model removes the redundant projection matrices typically present in multi-layer LSTM configurations. As a result, the total parameter count is reduced by approximately 28.1%, from 1.52 million in standard three-layer LSTM to 1.09 million in the xLSTM configuration used in this study. This compact architecture helps to reduce the memory footprint and accelerates both training and inference.  
The empirical findings further indicate that the xLSTM model effectively adapts to the complexities of Taiwan's urban traffic environments, including highly variable road geometries, significant modal splits (e.g., high motorcycle densities), and irregular congestion patterns. The ability to predict both congestion buildup and dissipation provides a critical lead time advantage for law enforcement agencies, allowing for optimized patrol deployment and traffic signal interventions based on reliable short-term forecasts.  
Nevertheless, several limitations warrant consideration. The model's performance exhibits slight degradation under extreme conditions, such as during heavy rainfall events, public holidays, or sudden road closures, where traffic patterns deviate substantially from historical norms. Furthermore, variations in sensor quality and data synchronization across different administrative regions may introduce additional noise into the forecasting pipeline. Future research should explore the integration of auxiliary information, such as real-time weather reports, accident notifications, and floating car data, to further enhance model robustness [31].  
Looking forward, the proposed system holds substantial potential for broader smart city applications. Beyond supporting proactive police dispatch, xLSTM-based traffic forecasts could inform adaptive traffic signal control, dynamic rerouting recommendations for drivers, and urban infrastructure planning initiatives. Expanding the system's coverage to encompass regional or national transportation networks, in collaboration with governmental agencies, represents a promising avenue for future development. Additionally, incorporating online learning mechanisms could enable continuous adaptation to evolving traffic behaviors, further strengthening the system's resilience and long-term effectiveness.