Crash risk score analysis for fleet management

Abstract

Methods, systems, and computer programs are presented to provide risk insights based on crash risk scores. The described solution addresses the challenge of reducing vehicle crashes and prioritizing safety interventions by utilizing a behavioral monitoring system (BMS) that calculates crash risk scores at the trip, driver, and organizational levels. The system employs machine learning models trained on historical data, incorporating features such as trip characteristics, driver history, and organizational safety practices. Risk scores are aggregated to provide actionable insights, enabling fleet managers to identify high-risk drivers, optimize routes, and implement targeted coaching or training. The solution improves safety performance, reduces crashes, and enhances resource allocation efficiency. The calculated risk scores and insights are presented via user interfaces, offering visualizations and recommendations for safety interventions.