Deep learning and mobile control system for hazardous materials transportation

Abstract

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.