Paper Contents
Abstract
Traffic collisions rank among the primary causes of fatalities and economic damage across the globe. While Machine Learning (ML) models exhibit potential in forecasting accident risks, their opaque nature undermines confidence among drivers, law enforcement, and policymakers. Explainable Artificial Intelligence (XAI) addresses this issue by rendering predictions more transparent and comprehensible. This paper examines the principles of explainable ML, reviews related studies, outlines a framework for converting predictions into preventive measures, and analyzes outcomes with illustrative examples. Approaches such as instantaneous driver notifications, dynamic traffic signals, and integration within smart cities are emphasized as methods to avert accidents before they happen.
Copyright
Copyright © 2025 Dr. C. Venish Raja. This is an open access article distributed under the Creative Commons Attribution License.
