# xLSTM-Based Urban Traffic Flow Prediction for Intelligent Transportation Governance
## 5 Conclusions  
This study presents an xLSTM-based real-time traffic flow forecasting system designed to enhance urban traffic governance and proactive police deployment. By leveraging real-world vehicle detector and CCTV data, the system demonstrates superior predictive accuracy, low inference latency, and practical applicability in dynamic and heterogeneous urban environments.  
The empirical results validate that the xLSTM architecture, featuring matrix-based memory cells and exponential gating mechanisms, effectively captures complex spatio-temporal traffic patterns. The proposed framework achieves an average congestion prediction accuracy exceeding 87%, providing sufficient lead time for tactical patrol unit de ployment and incident management. Unlike computationally intensive Transformer-based or graph neural network models, xLSTM balances forecasting precision with computational efficiency, making it highly suitable for edge deployment scenarios such as dispatch control centers.  
In practical terms, this research advances the integration of AI-driven forecasting technologies into smart city infrastructure, promoting more responsive, data-driven urban traffic management and public safety operations. The real-time forecasting capabilities demonstrated in this study offer tangible benefits for congestion mitigation, emergency response optimization, and infrastructure planning.  
Conceptualization, [PERSON]-I.H. and [PERSON]-[PERSON]; methodology, [PERSON]-I.H. and [PERSON]-[PERSON]; software, [PERSON]-I.H. and [PERSON]-[PERSON]; validation, [PERSON]-I.H., [PERSON]-[PERSON] and [PERSON]-[PERSON]; formal analysis, C.-I.H.; investigation, [PERSON] and [PERSON]; resources, [PERSON]; data curation, [PERSON] and [PERSON]; writing--original draft preparation, [PERSON]-[PERSON]; writing--review and editing, [PERSON] and [PERSON]; visualization, [PERSON] and [PERSON]; supervision, [PERSON] and [PERSON]; project administration, [PERSON]-[PERSON]; funding acquisition, [PERSON] and [PERSON]; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Science and Technology Council (NSTC), grant number NSTC 113-2221-E-492-016. The APC was funded by the National Science and Technology Council (NSTC).  
Not applicable.  
The data supporting the findings of this study are available through a public prototype at [[http://TCPS.nchc.org.tw](http://TCPS.nchc.org.tw)]([http://TCPS.nchc.org.tw](http://TCPS.nchc.org.tw)) (accessed on 28 May 2025), which demonstrates real-time forecast results. Further datasets can be made available from the corresponding author upon reasonable request.  
We extend our appreciation to the National Center for High-performance Computing (NCHC) for providing the data and model marketplace platforms that were crucial for achieving data and model reuse in this project. We also wish to thank the Taichung City Government Transportation Bureau for providing open database data and the Hsinchu City Police Department for their guidance, both of which greatly contributed to the success of this study. A public prototype is available at [[http://TCPS.nchc.org.tw](http://TCPS.nchc.org.tw)]([http://TCPS.nchc.org.tw](http://TCPS.nchc.org.tw)) (accessed on 28 May 2025) for real-time forecast demonstration.  
Author [PERSON] was employed by the National Center for High-performance Computing (NCHC). The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.