Data-Driven Road Safety: A Machine Learning Framework Utilizing Open Traffic Data

Abstract

Road traffic accidents continue to be a problem across the world and according to statistics cause high mortality and economic losses. This research work conceptualizes an idea that will use open traffic data and machine learning models to forecast accidents on roads in order to promote road safety. Based on the presented literature review, the framework incorporates a step-by-step procedure to analyze risk factors for targeted safety interventions, including data pre-processing and feature selection, application of a chosen model for high-risk zones identification, and improving the result by altering related factors. The findings show the applicability of open data and predictive analysis in traffic safety matters, with special emphasis on temporal, spatial, and environmental features. Resources allocation, urban traffic control, and monitoring are cases used to illustrate the framework's applicability. Although this is a conceptual model, the challenges, such as data quality, data privacy issues, and practical issues with implementation, are also included in the framework, along with suggestions for future research, such as the use of stream data and improved modeling techniques. This investigation contributes to the literature as a robust theoretical model from which practical solutions for road traffic safety interventions can be derived to reduce and ultimately eliminate traffic accidents and fatalities worldwide.