Machine learning-enabled real-time risk prediction and mitigation in drone-based hazmat delivery

Hazardous material (hazmat) transportation presents considerable public safety and regulatory challenges, especially in dense urban environments. While drones are gaining traction as a viable solution for last-mile hazmat delivery, the literature has yet to present a comprehensive framework that integrates real-time risk mitigation with multi-stakeholder consequence assessment. This study addresses this critical gap by introducing a novel drone-based hazmat transportation framework built on two core innovations: the Accident Prediction and Mitigation System (APRiMS) and a multi-dimensional risk prediction suite. APRiMS functions as an autonomous decision-support engine, continuously processing real-time flight, shipment, and environmental data to estimate accident likelihood and initiate mitigation measures. In parallel, the framework incorporates four machine learning models that predict the potential impacts of drone-related incidents on the public, businesses, and customers. These components operate within a closed-loop architecture, wherein risk predictions dynamically inform APRiMS decisions, enabling real-time, consequence-aware operational responses. The proposed framework offers a scalable and intelligent deployment of drones for high-risk logistics.