Artificial Intelligence in Hazardous Cargo Transportation: Risk Prediction and Route Optimization for U.S. Highways

The highway transportation of hazardous materials poses unique safety and security challenges that demand innovative risk management and routing strategies. Artificial intelligence (AI) techniques are increasingly being applied to predict accident risk and optimize routing for hazardous cargo. This article reviews AI‐based risk prediction systems and AI‐driven route optimization methods specifically in the context of U.S. highway hazmat transport. Discuss accident‐prediction models leveraging historical crash data, real‐time sensor and telematics integration, and predictive maintenance insights. On the routing side, we examine reinforcement‐learning and optimization frameworks that incorporate safety constraints, time‐varying traffic conditions, and regulatory requirements. Real‐world examples from U.S. Department of Transportation initiatives and industry platforms (e.g. Convoy, KeepTruckin/Project44) are integrated to illustrate current practices. Key risk factors (e.g. population density, material hazard class, driver conditions) are summarized in a structured risk table. We also outline a conceptual AI system architecture for hazmat routing, highlighting data sources and decision‐support components. The review concludes that AI can significantly enhance proactive safety management and efficient routing of hazardous shipments, supporting federal safety goals. Future work should address data integration challenges, model validation, and regulatory acceptance of AI systems.