Intersection Safety Modeling Using Semantic Scene Graph and Graph Neural Network

Traffic intersections are critical zones where vehicle and pedestrian interactions significantly impact road safety. This study presents a novel graph-based approach to model and analyze intersection traffic dynamics, leveraging Graph Neural Networks (GNNs) for risk assessment. By representing traffic participants and road infrastructure as a structured graph, we capture spatial-temporal relationships that influence crash likelihood. Using real-world intersection video data, we construct semantic scene graphs to encode actor interactions and road topology, enabling a data-driven understanding of risk factors. Two GNN models, TransformerConv and GINEConv, are employed to assess safety risks, where TransformerConv captures dynamic interactions through adaptive attention weighting, and GINEConv models structured dependencies within the intersection network. Our findings demonstrate that this framework can effectively classify high-risk scenarios, threat assessment of each actors (node), characterize their interaction (edge), and provide near real time safety analysis with 79.8% accuracy. This provides a scalable method for proactive intersection safety monitoring.