Recent developments in artificial intelligence have brought about significant changes to intelligent transportation systems. Hierarchical Graph Neural Networks (HGNNs) have emerged as a novel approach to road safety research and driver violation prediction in the last several years. Though they excel at capturing temporal and spatial information, traditional deep learning models such as CNNs and LSTM networks frequently fail to capture the intricate interplay between drivers, vehicles, road surroundings, and other variables. A kind of structure learning system called Graph Neural Networks (GNNs) can be employed to illustrate the interconnections between various traffic networks. To display the evolution of relationships over time, Graph Neural Networks make use of nodes and edges. By arranging data hierarchically across many levels of abstraction, HGNNs enhance GNNs' capabilities. Together, studies of behavior at the micro level and predictions of traffic patterns at the macro level are made easier by this. Hierarchical models outperform their flat-structured counterparts in terms of accuracy, interpretability, and flexibility through the use of attention processes, uncertainty estimations, and multi-scale fusion mechanisms. In order to create hybrid learning systems, current HGNN frameworks include attention-based and probabilistic processes into CNNs, LSTMs, and GNNs. The significance of explainable, federated, and edge-deployable designs has been magnified by recent advancements. While enhancing processing performance and safeguarding data privacy, these designs facilitate real-time analysis. Adaptive alarm systems, sophisticated traffic control solutions, and rapid violation detection are all made feasible by these technologies.
