Artificial intelligence is a powerful tool to learn and predict traffic effects according to drivers' behavior and make effective predictions to support traffic management team. This paper presents a proposal using reinforcement deep learning, simulation, and performance analysis of road systems with improvement in hazardous materials transportation control. The analysis reports the reduction of accident detection time and damages caused by traffic jams. The use of smartphone sensors, artificial intelligence, and an integrated control system for tracking, management, monitoring, and control of hazardous materials transportation allows for the reduction in detection time.
Abstract. The Blue Path (CA-e) is an document created and transmitted electronically that contains all necessary data to allow the sanitary authority to analyze the requirement of exportation that will result on the sanitary clearance of the animal protein for exportation. Actually it is in use in Brazil. However, even though the data exists electronically, when it is send to the importer country some must be sent on paper, because the bilateral agreements do not allow the exchange of electronic documents. The present work proposals the extension of the CA-e, as an element of integration and traceability, that will allow the automation of the communication between private and public systems not only at Brazil, the country of the origin of the animal protein exported, but also between and at the country of destiny. Based on the number presented by the adoption of the CA-e at Brazil, it is fair to conclude that this communication machine/machine would bring great economy and optimization for the supply chain.
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