Road Accident Risk Intelligence and Forecasting (RARIF) Framework using Machine Learning

An ongoing global public health and economic crisis is represented by traffic accidents. Conventional accident analysis, which mostly uses static statistical models, has a hard time adjusting to complex and dynamic urban settings. In order to integrate and analyze diverse data sources, this paper presents a new and comprehensive Road Accident Risk Intelligence and Forecasting (RARIF) Framework that makes use of cutting-edge machine learning (ML). Real-time traffic density, weather, vehicle telematics, road infrastructure features, and accident history are all combined in the framework. The RARIF framework provides high-fidelity prediction of accident-prone zones and spatiotemporal risk forecasting by using a hybrid modeling approach that makes use of reliable algorithms like Random Forest, XGBoost, and Deep Neural Networks (DNN).The incorporation of Explainable AI (XAI) techniques, which offer interpretability and transparency in model-based decision-making, is a crucial part of this framework. The empirical findings of the study show how well the framework predicts outcomes and how it can facilitate proactive, data-driven interventions. The framework's significant implications for public safety, intelligent transportation systems, and smart city planning are covered in the paper's conclusion.