Volume 41, Issue 2, December 2023, Pages 598–605
Mory Richard BATIEBO1, Tiemoman KONE2, N’Guessan Behou Gérard3, and Serge Stephane AMAN4
1 Informatiques et Sciences du Numérique, Université Virtuelle de (UVCI), Abidjan, Côte d’Ivoi, Côte d’Ivoire
2 Department of Analysis, Decision and Information, Computer Science and Digital Science, Université virtuelle de côte d’Ivoire, Abidjan, Côte d’Ivoire
3 Virtual University of , Research and Digital Expertise Unit, Abidjan, Côte d’Ivoi, Côte d’Ivoire
4 Informatiques et Sciences du Numérique, Université Virtuelle de (UVCI), Abidjan, Côte d’Ivoi, Côte d’Ivoire
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This paper presents a novel approach to improving urban road traffic control using artificial intelligence (AI) for dynamic traffic light management. We begin by describing the current context of urban traffic management and the challenges facing traffic light infrastructures. We then explain how AI can be integrated into this context for more effective regulation. We have chosen to use a basic model based on convolutional neural networks (CNN) to model road traffic in real time. This model collects real-time data from traffic cameras and other sensors, pre-processes it and then analyses it to make intelligent decisions about traffic light control. By using historical data and adapting to changing conditions, our model has been able to reduce waiting times at intersections, minimise congestion and improve traffic flow. This research paves the way for more intelligent and adaptive traffic management in urban environments. The practical implications of our approach include more efficient urban mobility, reduced greenhouse gas emissions and improved road safety. Future prospects lie in the continued optimisation of the AI model and its integration with other intelligent transport systems, contributing to more sustainable and liveable cities.
Author Keywords: Urban Road Traffic Control, Convolutional Neural Networks (CNN), Dynamic Traffic Management, Waiting Time Optimisation, AI Model Adaptability, Intelligent Road Traffic.
Mory Richard BATIEBO1, Tiemoman KONE2, N’Guessan Behou Gérard3, and Serge Stephane AMAN4
1 Informatiques et Sciences du Numérique, Université Virtuelle de (UVCI), Abidjan, Côte d’Ivoi, Côte d’Ivoire
2 Department of Analysis, Decision and Information, Computer Science and Digital Science, Université virtuelle de côte d’Ivoire, Abidjan, Côte d’Ivoire
3 Virtual University of , Research and Digital Expertise Unit, Abidjan, Côte d’Ivoi, Côte d’Ivoire
4 Informatiques et Sciences du Numérique, Université Virtuelle de (UVCI), Abidjan, Côte d’Ivoi, Côte d’Ivoire
Original language: English
Copyright © 2023 ISSR Journals. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper presents a novel approach to improving urban road traffic control using artificial intelligence (AI) for dynamic traffic light management. We begin by describing the current context of urban traffic management and the challenges facing traffic light infrastructures. We then explain how AI can be integrated into this context for more effective regulation. We have chosen to use a basic model based on convolutional neural networks (CNN) to model road traffic in real time. This model collects real-time data from traffic cameras and other sensors, pre-processes it and then analyses it to make intelligent decisions about traffic light control. By using historical data and adapting to changing conditions, our model has been able to reduce waiting times at intersections, minimise congestion and improve traffic flow. This research paves the way for more intelligent and adaptive traffic management in urban environments. The practical implications of our approach include more efficient urban mobility, reduced greenhouse gas emissions and improved road safety. Future prospects lie in the continued optimisation of the AI model and its integration with other intelligent transport systems, contributing to more sustainable and liveable cities.
Author Keywords: Urban Road Traffic Control, Convolutional Neural Networks (CNN), Dynamic Traffic Management, Waiting Time Optimisation, AI Model Adaptability, Intelligent Road Traffic.
How to Cite this Article
Mory Richard BATIEBO, Tiemoman KONE, N’Guessan Behou Gérard, and Serge Stephane AMAN, “Optimising urban traffic management: A dynamic approach to traffic lights using artificial intelligence,” International Journal of Innovation and Applied Studies, vol. 41, no. 2, pp. 598–605, December 2023.
