Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman
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 cha...
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| Format: | Article |
| Language: | English |
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University of Human Development
2025-06-01
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| Series: | UHD Journal of Science and Technology |
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| Online Access: | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1458 |
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| author | Qeethara Al-Shayea Huthaifa Aljawazneh |
| author_facet | Qeethara Al-Shayea Huthaifa Aljawazneh |
| author_sort | Qeethara Al-Shayea |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-58e4a403afe04d96bcde5ba7a0a966c8 |
| institution | Kabale University |
| issn | 2521-4209 2521-4217 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Human Development |
| record_format | Article |
| series | UHD Journal of Science and Technology |
| spelling | doaj-art-58e4a403afe04d96bcde5ba7a0a966c82025-08-24T18:44:50ZengUniversity of Human DevelopmentUHD Journal of Science and Technology2521-42092521-42172025-06-019116918010.21928/uhdjst.v9n1y2025.pp169-1801591Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of AmmanQeethara Al-Shayea0Huthaifa Aljawazneh1Department of Business Intelligence, Faculty of Business, Al-Zaytoonah University of Jordan, Amman, JordanDepartment of Business Intelligence, Faculty of Business, Al-Zaytoonah University of Jordan, Amman, JordanTraffic 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.https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1458traffic congestionmachine learningsimulationaimsun software |
| spellingShingle | Qeethara Al-Shayea Huthaifa Aljawazneh Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman UHD Journal of Science and Technology traffic congestion machine learning simulation aimsun software |
| title | Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman |
| title_full | Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman |
| title_fullStr | Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman |
| title_full_unstemmed | Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman |
| title_short | Evaluating the Effectiveness of Traffic Metering Strategies in Reducing Congestion: A Case Study of Amman |
| title_sort | evaluating the effectiveness of traffic metering strategies in reducing congestion a case study of amman |
| topic | traffic congestion machine learning simulation aimsun software |
| url | https://journals.uhd.edu.iq/index.php/uhdjst/article/view/1458 |
| work_keys_str_mv | AT qeetharaalshayea evaluatingtheeffectivenessoftrafficmeteringstrategiesinreducingcongestionacasestudyofamman AT huthaifaaljawazneh evaluatingtheeffectivenessoftrafficmeteringstrategiesinreducingcongestionacasestudyofamman |