An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System

Abstract

Due to excessive car usage, pollution and traffic have increased. In urban cities in Saudi Arabia, such as Riyadh and Jeddah, drivers and air quality suffer from traffic congestion. Although the government has implemented numerous solutions to resolve this issue or reduce its effect on the environment and residents, it still exists and is getting worse. This paper proposes an intelligent, adaptive, practical, and feasible deep learning method for intelligent traffic control. It uses an Internet of Things (IoT) sensor, a camera, and a Convolutional Neural Network (CNN) tool to control traffic in real time. An image segmentation algorithm analyzes inputs from the cameras installed in designated areas. This study considered whether CNNs and IoT technologies could ensure smooth traffic flow in high-speed, high-congestion situations. The presented algorithm calculates traffic density and cars' speeds to determine which lane gets high priority first. A real case study has been conducted on MATLAB to verify and validate the results of this approach. This algorithm estimates the reduced average waiting time during the red light and the suggested time for the green and red lights. An assessment between some literature works and the presented algorithm is also provided. In contrast to traditional traffic management methods, this intelligent and adaptive algorithm reduces traffic congestion, automobile waiting times, and accidents.

Intelligent Algorithms for Air Pollution Corrosion Risk Assessment and Management of Automotive Materials

“…Finally, based on the evaluation results of the model, the risk of corrosion of automotive materials was managed and controlled. The specific details are as follows: the data collection and preprocessing module is responsible for collecting relevant data on the automotive usage environment, body and components, and preprocessing these data to provide a reliable data foundation for subsequent modules [15][16]. The feature extraction and selection module utilizes data mining technology to extract corrosion related features through data analysis and modeling, and screens these features to select the features that have the greatest impact on corrosion as model inputs [17][18].…”