Leveraging Probabilistic Risk Assessment and Machine Learning for Safety and Cost Optimization in HAZMAT Transportation

Abstract

Abstract Transportation systems play a pivotal role in modern society, but they are not without inherent risks and inefficiencies. This paper explores the integration of Probabilistic Risk Assessment (PRA) and Machine Learning (ML) techniques to enhance safety and cost optimization in hazardous materials (HAZMAT) transportation. Traditional PRA methods, while robust, are limited by the quality and quantity of data available for analysis. ML techniques can address these limitations by analyzing large datasets, identifying patterns, and making accurate predictions. The integration of ML techniques into PRA can enhance data analysis, prediction capabilities, routing decisions, resource allocation, and decision-making processes in HAZMAT transportation. This paper presents a comprehensive literature review on PRA and ML within the transportation industry, discusses the potential benefits of integrating these approaches, examines the challenges associated with transportation accident data, and suggests areas for further research and improvements in HAZMAT transportation safety analysis.