Home / Current Issue / Paper 1724301
Evaluation of Fuzzy Logic-Based Adaptive Traffic Light Control System for Intelligent Highways
Subject area: Science,Engineering and Technology · Area of research: Fuzzy Logic
Abstract
This study investigates the implementation of a fuzzy logic traffic control system to enhance urban traffic management. The primary purpose is to address the challenges of congestion and inefficiency associated with traditional fixed-time controllers. The study identifies key problems, such as inadequate response to fluctuating traffic conditions and long waiting times at intersections. To achieve the objectives, a comprehensive methodology was developed, incorporating real-time traffic data to inform fuzzy logic decision-making. The procedures involved analysing vehicle counts, queue lengths, and waiting times to optimise signal timings dynamically. Results indicated that the fuzzy logic system achieved a vehicle throughput of 10 to 30 vehicles, significantly higher than the fixed-time controller's throughput of 8 to 16 vehicles. Additionally, the fuzzy logic controller demonstrated a remarkable clearance rate of 99% to 100%, compared to the fixed-time controller's performance. The study's findings advocate for the adoption of fuzzy logic in traffic management policies, emphasising the need for integrating advanced technologies such as IoT and machine learning for smarter urban environments. By providing a framework for dynamic traffic control, this research contributes valuable insights into optimising traffic flow and reducing congestion, ultimately promoting more efficient and sustainable urban transportation systems.
Keywords
evaluation; adaptive; intelligent highway; traffic light
References
[1] Ai-min S. Discussion on countermeasures and intelligent operation mode of division construction for the expressway passenger and freight transport system. China Journal of Highway and Transport. 2020;33(5):1–7.
[2] Boukerche A, Tao Y, Sun P. Artificial intelligence-based vehicular traffic flow prediction methods for supporting intelligent transportation systems. Computer Networks. 2020;182:107484.
[3] Bui DT, Hoang ND, Nhu VH. A swarm intelligence-based machine learning approach for predicting soil shear strength for road construction: A case study at Trung Luong National Expressway Project (Vietnam). Engineering with Computers. 2019;35(3):955–965.
[4] Chen IMA. IMG/M v.5.0: An integrated data management and comparative analysis system for microbial genomes and microbiomes. Nucleic Acids Research. 2019;47(D1):D666–D677.
[5] Cheng Z, Wang W, Lu J, Xing X. Classifying the traffic state of urban expressways: A machine-learning approach. Transportation Research Part A: Policy and Practice. 2020;137:411–428.
[6] Cheng Z, Yuan J, Yu B, Lu J, Zhao Y. Crash risks evaluation of urban expressways: A case study in Shanghai. IEEE Transactions on Intelligent Transportation Systems. 2022;23(9):15329–15339.
[7] Cui Y, Lei D. Design of highway intelligent transportation system based on the internet of things and artificial intelligence. IEEE Access. 2023.
[8] Ding Y, Jin M, Li S, Feng D. Smart logistics based on the Internet of Things technology: An overview. International Journal of Logistics Research and Applications. 2021;24(4):323–345.
[9] Enems Project. Design and construction of automatic traffic control system [blog post]. 2017 May 8. https://enemsproject.wordpress.com/
[10] Jan B, Farman H, Khan M, Talha M, Din IU. Designing a smart transportation system: An Internet of Things and big data approach. IEEE Wireless Communications. 2019;26(4):73–79.
[11] Traffic light control in vehicular network system using fuzzy logic. European Journal of Computer Science and Information Technology.
[12] Jiang D. The construction of smart city information system based on the Internet of Things and cloud computing. Computer Communications. 2020;150:158–166.
How to cite this paper
@article{1724301,
author = {Azanor Great Sylvester, P. Elechi, B. L Bakare},
title = {Evaluation of Fuzzy Logic-Based Adaptive Traffic Light Control System for Intelligent Highways},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {3488-3498},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724301.pdf},
abstract = {This study investigates the implementation of a fuzzy logic traffic control system to enhance urban traffic management. The primary purpose is to address the challenges of congestion and inefficiency associated with traditional fixed-time controllers. The study identifies key problems, such as inadequate response to fluctuating traffic conditions and long waiting times at intersections. To achieve the objectives, a comprehensive methodology was developed, incorporating real-time traffic data to inform fuzzy logic decision-making. The procedures involved analysing vehicle counts, queue lengths, and waiting times to optimise signal timings dynamically. Results indicated that the fuzzy logic system achieved a vehicle throughput of 10 to 30 vehicles, significantly higher than the fixed-time controller's throughput of 8 to 16 vehicles. Additionally, the fuzzy logic controller demonstrated a remarkable clearance rate of 99% to 100%, compared to the fixed-time controller's performance. The study's findings advocate for the adoption of fuzzy logic in traffic management policies, emphasising the need for integrating advanced technologies such as IoT and machine learning for smarter urban environments. By providing a framework for dynamic traffic control, this research contributes valuable insights into optimising traffic flow and reducing congestion, ultimately promoting more efficient and sustainable urban transportation systems.},
keywords = {evaluation; adaptive; intelligent highway; traffic light},
month = {October},
}