Authors : Mikhailov Aleksandr Vladimirovich

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/tbrkwdrr

Scribd : https://tinyurl.com/br4nyyz5

DOI : https://doi.org/10.38124/ijisrt/25nov1326

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This article examines the integration of artificial intelligence into freight-transport safety through the system architecture proposed by engineer Alexander Snurnikov. His work combines multimodal sensing, predictive analytics, and adaptive routing to reduce accident probability in long-haul trucking and hazardous-cargo logistics. The study outlines a model in which vehicle-mounted sensors, driver-state monitoring, and real-time routing algorithms jointly contribute to a continuous risk-mitigation cycle. The article also highlights infrastructure-level elements, including energy-efficient highway design and data-driven traffic regulation, positioning them as complementary components of systemic safety. The findings demonstrate that AI-driven decision support can reduce response times, increase routing stability, and enhance human- factor awareness, especially under dynamic environmental conditions. This approach corresponds with current safety objectives of the U.S. Department of Transportation and supports the migration from reactive to predictive logistics governance. The analysis concludes that integrated AI architectures, when combined with human-supervised operational control, offer a viable pathway toward reducing systemic risk in freight operations.

Keywords : Artificial Intelligence; Freight Safety; Driver State Monitoring; Hazardous Cargo Routing; Predictive Analytics; Transportation Systems; Risk Mitigation; Logistics Optimisation; Intelligent Infrastructure.

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