Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy
As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for eme...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/6997 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849429139968229376 |
|---|---|
| author | Atef Gharbi Mohamed Ayari Nadhir Ben Halima Akil Elkamel Zeineb Klai |
| author_facet | Atef Gharbi Mohamed Ayari Nadhir Ben Halima Akil Elkamel Zeineb Klai |
| author_sort | Atef Gharbi |
| collection | DOAJ |
| description | As urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution. |
| format | Article |
| id | doaj-art-2e2f467ad1ed4cb897535454820921ce |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2e2f467ad1ed4cb897535454820921ce2025-08-20T03:28:28ZengMDPI AGApplied Sciences2076-34172025-06-011513699710.3390/app15136997Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg StrategyAtef Gharbi0Mohamed Ayari1Nadhir Ben Halima2Akil Elkamel3Zeineb Klai4Department of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi ArabiaDepartment of Information Technology, Community College of Qatar, Doha 7344, QatarDepartment of Information Systems, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi ArabiaDepartment of Computer Sciences, Faculty of Computing and Information Technology, Northern Border University, Rafha 91911, Saudi ArabiaAs urban traffic density and congestion increase, effective urban traffic management becomes increasingly challenging, negatively impacting travel times and the overall efficiency of transportation systems. In this paper, a hierarchical Stackelberg model is presented to address both priority for emergency vehicles (EVs) and fairness for other vehicles. This model involves the Traffic Management Center (TMC) as the top-level authority, with emergency vehicles as the first-level leaders and regular vehicles (RVs) as the second-level followers. The multilevel decision-making structure enables real-time adjustments to prioritize critical traffic and ensure equitable treatment for regular traffic. Simulations were conducted under various traffic scenarios, including normal conditions, emergency vehicle priority, and peak traffic congestion. According to the results, the hierarchical Stackelberg model outperforms traditional models in terms of reducing average travel time, waiting time, and congestion. The model also incorporates fairness metrics such as Gini coefficients and skewness to ensure that regular vehicles are not disproportionately affected by emergency vehicle priority. According to these findings, the hierarchical Stackelberg model improves both traffic efficiency and fairness in complex urban environments, positioning it as a promising solution.https://www.mdpi.com/2076-3417/15/13/6997hierarchical Stackelberg modelfairnessmulti-agent systemsOptimizing Autonomous Traffic Management |
| spellingShingle | Atef Gharbi Mohamed Ayari Nadhir Ben Halima Akil Elkamel Zeineb Klai Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy Applied Sciences hierarchical Stackelberg model fairness multi-agent systems Optimizing Autonomous Traffic Management |
| title | Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy |
| title_full | Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy |
| title_fullStr | Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy |
| title_full_unstemmed | Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy |
| title_short | Fairness Criteria in Multi-Agent Systems: Optimizing Autonomous Traffic Management Through the Hierarchical Stackelberg Strategy |
| title_sort | fairness criteria in multi agent systems optimizing autonomous traffic management through the hierarchical stackelberg strategy |
| topic | hierarchical Stackelberg model fairness multi-agent systems Optimizing Autonomous Traffic Management |
| url | https://www.mdpi.com/2076-3417/15/13/6997 |
| work_keys_str_mv | AT atefgharbi fairnesscriteriainmultiagentsystemsoptimizingautonomoustrafficmanagementthroughthehierarchicalstackelbergstrategy AT mohamedayari fairnesscriteriainmultiagentsystemsoptimizingautonomoustrafficmanagementthroughthehierarchicalstackelbergstrategy AT nadhirbenhalima fairnesscriteriainmultiagentsystemsoptimizingautonomoustrafficmanagementthroughthehierarchicalstackelbergstrategy AT akilelkamel fairnesscriteriainmultiagentsystemsoptimizingautonomoustrafficmanagementthroughthehierarchicalstackelbergstrategy AT zeinebklai fairnesscriteriainmultiagentsystemsoptimizingautonomoustrafficmanagementthroughthehierarchicalstackelbergstrategy |