GeoAI-Based Road Hazard Prediction and Alert System

This paper presents a GeoAI-based road hazard prediction and alert system designed to support proactive driver decision-making. The proposed framework integrates live location and speed information, weather conditions, road-network attributes, historical accident data, and crowd-sourced hazard reports into a rule-based multi-factor road-risk assessment pipeline. Historical accident hotspots are related to the driver's current location using Haversine distance weighting. The resulting risk score is decomposed into contributing factors and used to generate hands-free voice alerts, safer-route suggestions, and location-aware emergency-assistance information. The prototype integrates OpenStreetMap, OSRM, Open-Meteo, Leaflet/React-Leaflet, and the Web Speech API. The current implementation is an academic prototype using a deterministic rule-based risk engine rather than a trained machine-learning model. Evaluation to date is functional and manual, and formal statistical validation is identified as future work.