Integrating AI and GIS for real-time traffic accident prediction and emergency response: A case study on high-risk urban areas

Abstract

Traffic accidents pose a significant risk factor in urban areas, affecting those on the road, public mobility and the proper functioning of transport infrastructures. This paper presents an innovative framework based on artificial intelligence (AI) and geographic information system (GIS) that aims to predict traffic accidents and optimise emergency responses. The proposed framework focuses on high-risk areas in large cities where it predicts the occurrence of accidents based on historical data combined with traffic densities, weather conditions and proximity to intersections. The AI model trained based on these variables predicts accident zones, while the GIS interface provides spatially accurate visualisations. Early results show that emergency teams respond 20 percent faster to predicted high-risk zones, thereby improving urban traffic safety and efficiency. The study illustrates the practical potential of the combination of AI and GIS for improving transport infrastructures, particularly in accident prediction and emergency responses optimisation. The proposed framework will be tested in other cities, aiming at scaling the framework by integrating more variables related to urban mobility and further extending the application domains in city transport management.