Artificial Intelligence in Hazardous Cargo Transportation - Snurnikov, Aleksandr
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The monograph explores the application of artificial intelligence to predict and mitigate risks in hazardous cargo transportation. It reviews AI-based accident prediction, telematics integration, and adaptive route optimization methods tailored for U.S. highways. Special attention is given to machine learning models for risk forecasting, reinforcement-learning route planners, and digital safety ecosystems aligned with U.S. DOT and FMCSA frameworks. The study demonstrates how AI transforms hazmat logistics from reactive compliance to proactive, data-driven safety management.

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Produktbeschreibung
The monograph explores the application of artificial intelligence to predict and mitigate risks in hazardous cargo transportation. It reviews AI-based accident prediction, telematics integration, and adaptive route optimization methods tailored for U.S. highways. Special attention is given to machine learning models for risk forecasting, reinforcement-learning route planners, and digital safety ecosystems aligned with U.S. DOT and FMCSA frameworks. The study demonstrates how AI transforms hazmat logistics from reactive compliance to proactive, data-driven safety management.
Autorenporträt
Aleksandr Snurnikov is an engineer and researcher specializing in transportation safety, intelligent infrastructure, and applied artificial intelligence. His work bridges industrial engineering with digital logistics, focusing on AI systems for road safety, risk analytics, and sustainable freight management.