# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 1 Introduction
These approaches aim to better capture the spatio-temporal dynamics of urban mobility, particularly in the context of real-time traffic management and predictive analytics. Deep learning models have demonstrated considerable success in mobility prediction. [PERSON] et al. proposed DeepMove, an attentional recurrent network that predicts individual mobility trajectories by capturing temporal dependencies in human behavior patterns. Similarly, [PERSON] et al. introduced T-GCN, which combines graph convolutional networks with gated recurrent units to model both spatial road network topology and temporal traffic variations [2]. In the realm of ensemble and hybrid systems, [PERSON] et al. developed a deep learning ensemble model that integrates multiple forecasting components to improve robustness and accuracy under varying traffic conditions. [PERSON] et al. proposed an explainable hybrid AI system for emergency dispatch that blends rule-based logic with deep learning to support real-time decisions [16]. Graph-based spatio-temporal learning has become a central theme in traffic modeling. [PERSON] et al. presented STSGCN [10], a synchronous graph convolutional network that fuses temporal and spatial features for congestion prediction, while [PERSON] et al. utilized a multi-attention mechanism to adaptively learn the graph structure of traffic networks. These approaches are particularly effective in complex, interconnected road systems. To support decentralized and privacy-aware computation, federated learning frameworks have also been proposed. [PERSON] et al. surveyed recent advances in collaborative traffic learning across smart cities, emphasizing security and communication efficiency. Meanwhile, vision-based traffic modeling has also gained traction. [PERSON] et al. used deep residual networks to model citywide crowd flows in high-density urban settings [17], and [PERSON] et al. employed CNNs to treat traffic data as spatial images, enabling accurate speed prediction across large networks [18]. [PERSON] et al. [19] were among the first to formalize a spatio-temporal graph convolutional framework for traffic forecasting, laying the groundwork for subsequent GCN-based innovations. Several studies have focused on specific architectural enhancements. [PERSON] et al. implemented a spatio-temporal deep learning model for ride demand forecasting [20], while [PERSON] et al. explored reinforcement learning strategies for adaptive signal control [21]. Attention-based models [22], multi-source data fusion [23], and lightweight CNNs for edge devices [24] have been proposed to balance complexity and efficiency. Transformer-based innovations have also been adapted. [PERSON] et al. proposed FEDformer, a frequency-decomposed transformer for long-term traffic series forecasting [25]. [PERSON] et al. developed a spatio-temporal self-attention network tailored to urban flow prediction [26], and [PERSON] et al. combined multi-modal inputs such as video, sensor, and weather data into a deep fusion model for congestion prediction [27]. Collectively, these works underscore the growing sophistication of traffic forecasting research and provide a rich methodological foundation for our proposed xLSTM-based system. Unlike prior models, xLSTM balances predictive power with operational deployability in real-time police dispatch systems, addressing both spatio-temporal complexity and edge inference constraints. The xLSTM architecture incorporates innovations' memory cells and exponential gating mechanisms, enabling the model to learn complex spatial-temporal dependencies in traffic flow and to forecast congestion 15 to 30 min ahead of time. These predictive insights are directly linked to decision-support mechanisms for police operations, facilitating the proactive deployment of patrol units to high-risk intersections before congestion emerges. The contributions of this research lie at the intersection of artificial intelligence, urban traffic management, and public safety.