Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman

• Published in: UHD Journal of Science and Technology Vol. 9; no. 1; pp. 169 - 180
• Main Authors: Al-Shayea, Qeethara, Aljawazneh, Huthaifa
• Format: Journal Article
• Language: English
• Published: University of Human Development 12.06.2025
• Subjects: aimsun software; machine learning; simulation; traffic congestion
• ISSN: 2521-4209; 2521-4217
• DOI: 10.21928/uhdjst.v9n1y2025.pp169-180

Traffic congestion is a significant issue in urban road networks, particularly in Amman, where peak hours cause major delays for commuters. Developing an advanced traffic management system is essential to helping residents save time, reduce congestion, and alleviate traffic jams. To address this challenge, we have implemented a simulation model powered by machine learning techniques to effectively and accurately manage traffic flow on Amman's streets. This innovative system leverages real-world data from the Jordanian capital to dynamically optimize traffic control. By automating traffic management processes, the model aims to reduce congestion while easing the workload of traffic personnel. This approach promises to enhance urban mobility and contribute to building a smarter and more efficient traffic management infrastructure in Amman, ensuring a better quality of life for its residents. After implementing the metering strategy, the traffic flow became more balanced, with less congestion and smoother transitions between intersections. The metering points effectively regulated the entry of vehicles into the circles, preventing congestion buildup and improving overall traffic efficiency.