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Abstract |
Urban traffic congestion is a critical issue with significant implications for mobility and urban planning. In this study, we develop a real-time predictive alarm system capable of forecasting whether a street is likely to experience unusually high traffic within the next 30 min. The system classifies road segments into three alert levels based on traffic data updated every 10 min, providing timely information that can support decision-making in traffic management. The prediction model is built using deep learning techniques trained on a whole year of traffic data in the city of Valencia, and tested with the following year’s data. We evaluated different neural network architectures, including long short-term memory (LSTM) networks, an extended LSTM variant (xLSTM), and Graph Neural Networks (GNNs). Our results show that LSTM provides the best balance between accuracy and computational efficiency, making it the most suitable model for real-time deployment. In addition to traffic data, we incorporate meteorological variables such as wind speed, wind direction, and precipitation to explore their potential impact on traffic dynamics. Although the relationship between traffic and environmental conditions warrants further study, this work demonstrates the feasibility of using real-time predictions to improve urban mobility strategies. The proposed system offers a data-driven approach that can be integrated into broader traffic management frameworks to improve efficiency and responsiveness. |
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