Analyzing Critical Regions in Human Trajectories: A Context‐Aware Framework for Deep Learning‐Based Movement Prediction

Human mobility data are increasingly used in applications such as transportation analysis, traffic management, and urban planning, leading to the rapid growth of large‐scale trajectory datasets. A crucial aspect of analyzing human trajectories is the identification of critical points within these datasets. These critical points serve as effective representatives of entire trajectories, thereby reducing the computational demands associated with processing and analysis while maintaining the necessary level of accuracy. This study proposes a framework called Spatio‐temporal Semantic Contextual Attention‐based Encoder–Decoder Trajectory Prediction (STSC‐AED TrajecPred), designed to improve the identification and prediction of critical points in human trajectories. By utilizing fuzzy functions, the framework effectively integrates spatial, temporal, semantic, and contextual information to identify critical points that act as proxies for comprehensive datasets. The framework uses a transformer‐based encoder–decoder model together with spatial, temporal, semantic, and contextual embeddings to improve trajectory prediction. Evaluation results on two large‐scale datasets demonstrate that STSC‐AED TrajecPred successfully identifies critical points and reconstructs trajectory structures with an emphasis on enriched critical regions, leading to significant improvements in prediction accuracy while reducing computational overhead compared to existing approaches.