Artificial Intelligence and Machine Learning in Risk Management for the Road Transport of Dangerous Goods in Brazil: fundamentals, application, and compliance

• DOI: https://doi.org/10.22533/at.ed.5157262631035

• Palavras-chave: artificial intelligence; machine learning; dangerous goods; risk management; road transport; telematics; IoT; CVaR; risk equity

• Keywords: artificial intelligence; machine learning; dangerous goods; risk management; road transport; telematics; IoT; CVaR; risk equity

• Abstract:

  This article presents a complete and operational framework for the application of Artificial Intelligence and Machine Learning in risk management in the road transport of dangerous goods in Brazil, with a focus on reducing low-frequency, high-consequence losses, maintaining the level of logistics service, and regulatory compliance. The work is aligned with Decree No. 96,044 of May 18, 1988, which approves the Regulations for the Road Transport of Dangerous Goods, as well as the standards of the Brazilian Association of Technical Standards ABNT NBR 7500, referring to the identification for the land transport of dangerous goods, and ABNT NBR 9735, which provides for the set of equipment for emergencies in the land transport of dangerous goods. In addition, state Technical Instructions are considered, with emphasis on Technical Instruction 32 of 2025 of the Fire Department of the Military Police of the State of São Paulo, which establishes parameters for prevention and response in buildings and areas at risk involving dangerous goods.
  This article seeks to combine technical evidence from multimodal predictive models—such as Gated Recurrent Unit recurrent networks integrated with deep neural networks with multimodal incorporation—with risk-oriented route optimization methods, including the use of Conditional Value-at-Risk, known internationally as Conditional Value-at-Risk, and the concept of risk equity among affected communities. The approach is complemented by telemetry and the Internet of Things with sensors certified for explosive atmospheres, and by international guidelines for electronic documentation in emergencies established within the United Nations Economic Commission for Europe. 
  As an applied demonstration, we present the BR-116 corridor, known as Régis Bittencourt, for Class Three flammable liquid cargoes – Flammable Liquids, with results indicating an expected risk reduction of between 20% and 30% and a reduction in the Conditional Value at Risk to 95% between 35% and 40%, while preserving the delivery window and regulatory compliance.