Deep learning and mobile control system for hazardous materials transportation

2021 ◽  
Author(s):  
Luiz A. Reis ◽  
Sergio L. Pereira ◽  
Eduardo M. Dias ◽  
Maria L. R. P. D. Scoton
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Hazardous Materials ◽  
Hazardous Materials Transportation

Smartphones and Integrated Control System for Hazardous Materials Transportation

2021 ◽  
Author(s):  
LUIZ ANTONIO REIS ◽  
Sergio Luiz Pereira ◽  
Eduardo Mario Dias ◽  
Maria Lídia Rebello Pinho Dias Scoton
Keyword(s):  
Control System ◽  
Traffic Management ◽  
Integrated Control ◽  
Hazardous Materials ◽  
Detection Time ◽  
Human Beings ◽  
Monitoring And Control ◽  
Hazardous Materials Transportation ◽  
And Performance ◽  
Integrated Control System

Abstract 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.

scholarly journals A Deep Learning based Explainable Control System for Reconfigurable Networks of Edge Devices

2021 ◽  
pp. 1-1
Author(s):  
Priyanthi Dassanayake ◽  
Ashiq Anjum ◽  
Ali Kashif Bashir ◽  
Joseph Bacon ◽  
Rabia Saleem ◽  
...  
Keyword(s):  
Deep Learning ◽  
Control System ◽  
Reconfigurable Networks

Hazardous Materials Transportation by Road: Fuzzy-Theory-Based Routing Design

2013 ◽  
Vol 397-400 ◽  
pp. 696-699
Author(s):  
Peng Fei Li ◽  
Mao Xiang Lang
Keyword(s):  
Risk Analysis ◽  
Fuzzy Theory ◽  
Hazardous Materials ◽  
Minimum Risk ◽  
Hazardous Materials Transportation ◽  
Shortest Route ◽  
Routing Design ◽  
Fuzzy Risk Analysis

Firstly, the consequence of the accident was divided into several ranks. Then we can get the risk fund by the fuzzy risk analysis. Secondly, the stochastic number of every route was produced by the computer, and then the risk of every section can be got. Thirdly, the shortest route theory can be used to get the minimum risk routes. The rationality of the model and the feasibility of the algorithm are proved by the computation and analysis of the example.

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