FIR-Based Road Accident Blackspot Prediction and Safety Visualization Platform

Objective: To enable transport authorities to identify and monitor accident blackspots using predictive analytics on FIR data for targeted road safety interventions.

India faces a persistent challenge of road accidents, with many fatalities and injuries occurring at specific locations termed 'blackspots.' Transport and police departments often lack timely, actionable insights to proactively address these high-risk zones, as accident data is fragmented across FIR records, making on-ground interventions reactive rather than preventive. This aligns with the mandates of the Ministry of Road Transport & Highways and supports SDG 9 (Industry, Innovation, and Infrastructure).

The proposed solution is a digital platform that ingests anonymized First Information Report (FIR) data from police stations, applies predictive analytics and clustering algorithms to identify emerging and persistent accident blackspots, and presents the findings on an interactive dashboard. The system will allow authorities to visualize hotspots, analyze contributing factors, and prioritize safety measures.

Key features include automated FIR data extraction, geocoding of accident locations using OpenStreetMap, machine learning models for blackspot prediction, and a user-friendly dashboard for real-time monitoring. The prototype will provide actionable insights, generate alerts for new blackspots, and support data export for policy planning.

Measurable impact includes faster identification of dangerous locations, data-driven deployment of road-safety measures, and potential reduction in accident rates over time. The platform can scale to multiple districts or states by integrating additional data sources such as traffic volume, road conditions, and weather, fostering a culture of evidence-based road safety management.