{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T05:02:36Z","timestamp":1790226156027,"version":"4.1.0"},"reference-count":26,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Event detection on real-time traffic data is crucial for transportation systems, enhancing safety in autonomous systems and the resilience of urban mobility infrastructure. However, the complex, heterogeneous traffic stream data and spatial-temporal patterns pose a significant challenge to existing methods, which often fail to capture higher-order interactions among nodes and edges and semantic context. This paper proposes a generative, self-attention model layered with two stacked content-based layers based on a framework that incrementally handles the process of traffic stream networks. Experimental analysis on benchmark event -detection dataset demonstrates the effectiveness of the proposed methodology, attaining peak performance values of 0.80 in Normalized mutual information(NMI), 0.73 IN Adjusted Mutual Information (AMI), and 0.72 in Adjusted Rand Index(ARI) across the incremental block of heterogeneous data, the proposed method consistently outperforms state-of-the-art baselines.<\/jats:p>","DOI":"10.3390\/s26195999","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:32:12Z","timestamp":1790083932000},"page":"5999","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Generative Self-Attention for Adaptive Real-Time Traffic Event Detection in Next-Generation Intelligent Transportation Systems"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8397-3681","authenticated-orcid":false,"given":"Sakshi","family":"Verma","sequence":"first","affiliation":[{"name":"Department of IT, Indian Institute of Information Technology, Lucknow 226002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7736-8303","authenticated-orcid":false,"given":"Sushil Kumar","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Department of IT, Indian Institute of Information Technology, Lucknow 226002, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5438-579X","authenticated-orcid":false,"given":"Vishal Krishna","family":"Singh","sequence":"additional","affiliation":[{"name":"School of Computer Science and Electronic Engineering, Colchester Campus, University of Essex, Colchester CO4 3SQ, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4571-1888","authenticated-orcid":false,"given":"Rajkumar Singh","family":"Rathore","sequence":"additional","affiliation":[{"name":"Department of Computer Science, School of Technologies, Cardiff Metropolitan University, Cardiff CF5 2YB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0953-5047","authenticated-orcid":false,"given":"Weiwei","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9421","DOI":"10.1109\/TITS.2025.3563631","article-title":"Correlated Channeled Spatio Temporal Graph Attention Network Model for Traffic Prediction","volume":"26","author":"Singh","year":"2025","journal-title":"IEEE Trans. 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