Unified Framework for Real-time Multi-factor Risk-aware Route Optimization Using Machine Learning
Year: 2025
Type: article
Abstract: In the era of smart transportation and increasing road traffic, traveler safety is a critical priority. Traditional navigation systems often minimize distance or time, overlooking contextual safety factors. This paper presents a unified, machine learning-based framework for multi-factor risk assessment and safe route optimization. The system integrates static parameters (vehicle type, user demographics, lighting, road type, group size) and dynamic parameters (real-time weather, traffic, surface condition, time). It gathers data from Google Maps, OpenStreetMap, OpenWeatherMap, and TomTom, processes it through a modular pipeline, and assigns risk values to route segments. Gradient Boosting, Random Forest, and Decision Tree models predict cumulative risk scores to recommend the safest route. A case study between Maragathapuram and Parvathipuram in Tamil Nadu validates the framework. The experimental results show Gradient Boosting provides the highest prediction accuracy. The system provides real-time, personalized, and data-driven travel recommendations, enhancing intelligent transportation with a focus on safety.
Author: Thilagavathi T
Language: English
Topic: Traffic and Road Safety
Subfield: Safety, Risk, Reliability and Quality
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414928889
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