Smartphones and Integrated Control System for Hazardous Materials Transportation

Drivers’ behaviors are directly influenced by human beings and have different reactions. Artificial intelligence is a powerful tool to learn and predict the traffic effects according to drivers’ behavior and make predictions more effective to support hazardous material traffic management. This paper presents a proposal using deep learning, simulation, and performance analysis of road systems with improvement in hazardous materials transportation control. The analysis compares the reduction of accident detection time with their consequences such as damages caused by traffic jams, damages to human health, and environmental damages. The reduction in detection time is provided by the use of smartphones and an integrated control system for tracking, management, monitoring, and control of hazardous materials transportation.