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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\u00a0min. The system classifies road segments into three alert levels based on traffic data updated every 10\u00a0min, 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\u2019s 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.<\/jats:p>","DOI":"10.1007\/s00521-025-11316-0","type":"journal-article","created":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T04:41:38Z","timestamp":1748666498000},"page":"15837-15854","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Deep learning for urban air quality: a traffic-based prediction and alert system for Valencia"],"prefix":"10.1007","volume":"37","author":[{"given":"Miguel G.","family":"Folgado","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8864-2507","authenticated-orcid":false,"given":"Ver\u00f3nica","family":"Sanz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Johannes","family":"Hirn","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Edgar","family":"Lorenzo-S\u00e1ez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Javier","family":"Urchueguia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"11316_CR1","unstructured":"Azdad Z, Stoll B, M\u00fcller J (2022) The development trends of low and zero-emission zones in Europe"},{"key":"11316_CR2","unstructured":"European Environmental Agency (2019) Transport"},{"key":"11316_CR3","unstructured":"Covenant of Mayors (2023) European commissions covenant of mayors. 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