Chakrabarty, AmitabhaRaza, ReazChowdhury, Abrar SyedTanvir2026-01-212026-01-2120252025-10ID 22101632ID 22101571ID 22101337http://hdl.handle.net/10361/27468Cataloged from PDF version of thesis.Includes bibliographical references (pages 50-52).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2025.As urban traffic systems grow increasingly complex, traditional methods for managing congestion and accidents are proving insufficient. This research proposes a lane-level traffic anomaly detection framework that integrates Graph Attention Networks (GAT) with temporal fusion encoding to capture spatio-temporal dependencies within vehicle-road collaborative systems. A self-supervised learning mechanism enables effective anomaly detection with minimal labeled data while an attention-based module prioritizes critical neighbour regions to enhance robustness and computational efficiency. Through comprehensive experimentation, the proposed framework demonstrates strong anomaly detection performance with an F1-score of 0.84, significantly outperforming established base models such as LSTM (0.74), Autoencoder (0.65), DBSCAN (0.46) etc. This enables more adaptive, efficient and intelligent traffic management while optimizing overall network flow.60 pagesenBRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.Graph attention networksTemporal fusionAnomaly detectionLane-level traffic anomalySelf-supervised learningGraph neural networksUnsupervised learningTraffic dataReal-time informationTraffic managementNeural networks (Computer science).Electronic data processing--Distributed processing.Vehicular ad hoc networks (Computer networks).Vehicle-infrastructure integration.Intelligent Transportation Systems.Traffic monitoring--Real-time data processing.Thesis