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October 9, 2025International Journal of Apllied MathematicsOpen Access

Unified Framework for Real-Time Multi-Factor Risk-Aware Route Optimization Using Machine Learning

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The framework predicts risk scores for routes, enhancing traveler safety.
Gradient Boosting achieved the highest prediction accuracy in the provided case study.
The system integrates both static and dynamic parameters for comprehensive risk assessment.
Real-time travel recommendations are based on data processed from multiple mapping and weather services.

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Implication

This framework employs machine learning to enhance route optimization and safety in transportation, integrating real-time parameters with risk assessment.

Cite This Study

Thangaraj Thilagavathi (2025) studied this question.

synapsesocial.com/papers/68e7d631bd66d359be6267f7 https://doi.org/10.12732/ijam.v38i5s.317

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Implication

This framework employs machine learning to enhance route optimization and safety in transportation, integrating real-time parameters with risk assessment.

Unified Framework for Real-Time Multi-Factor Risk-Aware Route Optimization Using Machine Learning

Key Points

Discussion

Implication

Cite This Study

Unified Framework for Real-Time Multi-Factor Risk-Aware Route Optimization Using Machine Learning

Key Points

Discussion

Implication

Cite This Study

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