An Intelligent Adaptive Dynamic Algorithm for a Smart Traffic System Ahmed Alsheikhy 1,* , Yahia Said 1 and Tawfeeq Shawly 2 1 Electrical Engineering Department, College of Engineering, Northern Border University, Arar, Saudi Arabia 2 Electrical Engineering Department, Faculty of Engineering at Rabigh, King Abdulaziz University, Jeddah, Saudi Arabia *Corresponding Author: Ahmed Alsheikhy. Email: aalsheikhy@nbu.edu.sa Received: 09 August 2022; Accepted: 11 November 2022 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 numer- ous solutions to resolve this issue or reduce its effect on the environment and resi- dents, it still exists and is getting worse. This paper proposes an intelligent, adaptive, practical, and feasible deep learning method for intelligent traffic con- trol. 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 dur- ing the red light and the suggested time for the green and red lights. An assess- ment 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. Keywords: Smart traffic control; artificial intelligence; traffic congestion; IoT; CNN; smart roads 1 Introduction When the number of vehicles on the road outnumbers the capacity of that road, there is traffic congestion [1]. In big cities in Saudi Arabia, such as Riyadh and Jeddah, traffic congestion accelerates the number of accidents, increases the number of injured people, and affects the economy’ s growth. Traffic congestion is a severe and dangerous hazard to urban life [1,2]. In 2019, the world’ s population was 7.7 billion [3]. The United States of America lost nearly 6 billion hours from 2000 to 2010 due to traffic congestion [3]. According to the current growth of the population, traffic jams will continue to be one of the most serious difficulties governments face. These jams result in high consumption of fuels which leads to air pollution [3–6]. Having a feasible, reliable transportation system infrastructure would also reduce traffic congestion This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Computer Systems Science & Engineering DOI: 10.32604/csse.2023.035135 Article ech T Press Science