Sundari, K.Senthil Thilak, A.S.2026-02-032025International Journal of Data Science and Analytics, 2025, 20, 3, pp. 1857-18702364415Xhttps://doi.org/10.1007/s41060-024-00575-0https://idr.nitk.ac.in/handle/123456789/20090Accurate prediction of vehicle trajectories is essential for safe and efficient navigation in urban environments, particularly with the increasing prevalence of autonomous vehicles and intelligent transportation systems. This paper introduces a deep learning-based approach for predicting vehicle trajectories on urban roads in real time. The method combines techniques from graph neural networks (GNNs) and long short-term memory (LSTM)-based models to capture intricate spatial and temporal dependencies among vehicles. Vehicles are represented as nodes in the proposed graph model, and graph attention mechanism is used to model the interactions between them. Additionally, LSTM modules encode motion patterns and temporal correlations, facilitating spatial and temporal information fusion to improve prediction accuracy. The effectiveness of the approach is demonstrated through extensive experimentation and evaluation in generating vehicle trajectories, surpassing baseline methods. The proposed method holds promise for real-time vehicle trajectory prediction, with the potential for applications in autonomous driving, traffic management, and intelligent transportation systems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.Autonomous vehiclesForecastingGraph neural networksGraph theoryIntelligent systemsIntelligent vehicle highway systemsLearning systemsReal time systemsTrajectoriesAutonomous VehiclesComplex road networksDeep learningGraph attention networkIntelligent transportation systemsLearning approachReal- timeTrajectory predictionVehicle trajectoriesLong short-term memory