Use of Predictive Road Safety Analytics: Integrating Internal and External Driving and Journey Management Data with Power BI

Abstract

Driving remains one of the highest-risk activities in oil and gas operations, particularly across expansive geographies with varied infrastructure, security profiles, and driver populations. Traditional reactive safety approaches such as post-incident reviews and stand-alone IVMS reports are insufficient to identify emerging trends and prevent high-potential vehicle incidents. This paper introduces an integrated, data-driven Power BI dashboard developed to analyze and forecast driving risk across nine countries in the MENA and Pakistan regions.

The dashboard unifies internal and external driving data sources including IVMS, AI camera violations, journey management plans (JMP), behavioural safety observations (Radar), fatigue tracking, driver utilization hours, and driving real-time user feedback ("Rate My Driving"). The tool visualizes eight critical dimensions of road safety: driving exposure, driving violations, RAG performance, passenger driver evaluations, behavior-based safety, road incidents, fatigue management, and journey management compliance. The dashboard converts raw driving data into actionable safety intelligence, enabling HSE and operations teams to proactively retrain drivers, mitigate high-risk routes, and close compliance gaps.

Piloted across diverse terrains, this solution resulted in measurable improvements: increased journey planning adherence, reductions in over-speed and distraction events, and a cultural shift toward transparent safety ownership. This paper details the architecture, results, and lessons learned from deploying this dashboard and highlights its potential to drive a new standard in predictive road safety across Oilfield operations.