| ABSTRACT |
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. |
