# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 1 Introduction  
Urban traffic congestion has become one of the most pressing challenges in contemporary metropolitan governance, affecting not only commuter efficiency but also the operational readiness of emergency services. In densely populated urban centers such as Taichung, the ability to accurately forecast traffic bottlenecks in real time has grown increasingly essential for enabling proactive police deployment and ensuring timely incident response [1, 2]. Although recent advances in deep learning have considerably enhanced the accuracy of short-term traffic predictions, the practical integration of these predictive capabilities into real-world emergency response systems remains limited [3, 4]. Traditional police dispatch mechanisms continue to rely on reactive protocols, typically initiated only after congestion or incidents have already occurred. This reactive paradigm is inadequate in the face of increasingly complex and dynamic urban traffic conditions, driven by surging vehicle ownership, asymmetric commuting flows, and nonlinear mobility behaviors [5]. To address these limitations, there is a critical need for predictive, data-driven systems capable of supporting real-time operational decisions in urban traffic governance. Accurate traffic flow forecasting plays a foundational role in smart city infrastructure, enabling responsive interventions such as signal optimization, route reallocation, and strategic resource deployment. Among various machine learning models, Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks have shown notable success in capturing temporal dependencies in traffic data [6; 7]. However, the unique urban morphology of Taiwanese cities, the high heterogeneity of vehicle types, and irregular daily commuting patterns pose significant challenges to standard forecasting models. In light of these challenges, this study proposes an extended LSTM (xLSTM)-based traffic prediction framework specifically tailored to the complexities of Taiwan's urban road networks. By leveraging real-world vehicle detector (VD) sensor data and integrating forecast results into traffic governance protocols, this research contributes a scalable and validated model for intelligent, anticipatory urban traffic management [6; 8; 9]. According to the International Transport Forum, global urban vehicle ownership has surged by more than 30% over the past decade, resulting in longer commute times, increased greenhouse gas emissions, and substantial economic inefficiencies. Cities such as New York, London, and Beijing report annual commuter delays exceeding 80 h per capita [10; 11]. Taiwan mirrors these global trends. Major cities like Taichung and Kaohsiung now rank among the most congested worldwide, with average travel times exceeding 22 min for a 10 km journey and commuters losing between 54 to 58 h per year to congestion [12; 13]. Notably, these challenges persisted even during the COVID-19 pandemic, underscoring the structural nature of urban mobility inefficiencies. Taiwan's unique congestion dynamics are fueled by exceptionally high vehicle ownership--over 14 million motorcycles and 8 million cars--combined with low public transit adoption and decentralized urban planning. This has resulted in disproportionate pressure on arterial road networks and limited the effectiveness of conventional infrastructure expansion strategies [14; 15]. Tackling these issues requires more than physical road construction; it necessitates the deployment of intelligent, adaptive systems that can predict congestion in real time and support dynamic traffic interventions. This study addresses that need by introducing an xLSTM-based forecasting framework that integrates real-time vehicle detector (VD) data with advanced predictive modeling, specifically optimized for operational police dispatch applications. In recent years, a substantial body of research has been dedicated to improving traffic forecasting capabilities through advanced deep learning techniques, graph-based representations, and hybrid architectures.