IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v13y2025i3p495-d1582169.html
   My bibliography  Save this article

An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks

Author

Listed:
  • Ganhua Wu

    (Business School, University of Shanghai for Science and Technology, Shanghai 200093, China)

Abstract

Optimizing transportation in both natural and engineered systems, particularly within complex network environments, has become a pivotal area of research. Traditional methods for mitigating congestion primarily focus on routing strategies that utilize first-in-first-out (FIFO) queueing disciplines to determine the processing order of packets in buffer queues. However, these approaches often fail to explore the benefits of incorporating priority mechanisms directly within the routing decision-making processes, leaving significant room for improvement in congestion management. This study introduces an innovative generalized priority queueing (GPQ) strategy, specifically designed as an enhancement to existing FIFO-based routing methods. It is important to note that GPQ is not a new queue scheduling algorithm (e.g., deficit round robin (DRR) or weighted fair queuing (WFQ)), which typically manage multiple queues in broader queue management scenarios. Instead, GPQ integrates a dynamic priority-based mechanism into the routing layer, allowing the routing function to adaptively prioritize packets within a single buffer queue based on network conditions and packet attributes. By focusing on the routing strategy itself, GPQ improves the process of selecting packets for forwarding, thereby optimizing congestion management across the network. The effectiveness of the GPQ strategy is evaluated through extensive simulations on single-layer, two-layer, and dynamic networks. The results demonstrate significant improvements in key performance metrics, such as network throughput and average packet delay, when compared to traditional FIFO-based routing methods. These findings underscore the versatility and robustness of the GPQ strategy, emphasizing its capability to enhance network efficiency across diverse topologies and configurations. By addressing the inherent limitations of FIFO-based routing strategies and proposing a generalized yet scalable enhancement, this study makes a notable contribution to network optimization. The GPQ strategy provides a practical and adaptable solution for improving transportation efficiency in complex networks, bridging the gap between conventional routing techniques and emerging demands for dynamic congestion management.

Suggested Citation

  • Ganhua Wu, 2025. "An Innovative Priority Queueing Strategy for Mitigating Traffic Congestion in Complex Networks," Mathematics, MDPI, vol. 13(3), pages 1-21, February.
  • Handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:495-:d:1582169
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/13/3/495/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/13/3/495/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shengyong Chen & Wei Huang & Carlo Cattani & Giuseppe Altieri, 2012. "Traffic Dynamics on Complex Networks: A Survey," Mathematical Problems in Engineering, Hindawi, vol. 2012, pages 1-23, September.
    2. Rongrong Yin & Xudan Song, 2023. "Mitigation strategy of cascading failures in urban traffic congestion based on complex networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 34(02), pages 1-20, February.
    3. Mishra, Ankit & Wen, Tao & Cheong, Kang Hao, 2024. "Efficient traffic management in networks with limited resources: The switching routing strategy," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    4. Tadić, Bosiljka & Thurner, Stefan, 2005. "Search and topology aspects in transport on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 346(1), pages 183-190.
    5. Du, Wen-Bo & Wu, Zhi-Xi & Cai, Kai-Quan, 2013. "Effective usage of shortest paths promotes transportation efficiency on scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3505-3512.
    6. Muhua Zheng & Zhongyuan Ruan & Ming Tang & Younghae Do & Zonghua Liu, 2016. "Influence of periodic traffic congestion on epidemic spreading," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 27(05), pages 1-14, May.
    7. Zoltán Toroczkai & Kevin E. Bassler, 2004. "Jamming is limited in scale-free systems," Nature, Nature, vol. 428(6984), pages 716-716, April.
    8. Zhang, Mengyao & Huang, Tao & Guo, Zhaoxia & He, Zhenggang, 2022. "Complex-network-based traffic network analysis and dynamics: A comprehensive review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    9. Tadić, Bosiljka & Thurner, Stefan, 2004. "Information super-diffusion on structured networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 332(C), pages 566-584.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhao, Jie & Wang, Zhen & Yu, Dengxiu & Cao, Jinde & Cheong, Kang Hao, 2024. "Swarm intelligence for protecting sensitive identities in complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 182(C).
    2. Gao, Cai & Yan, Chao & Zhang, Zili & Hu, Yong & Mahadevan, Sankaran & Deng, Yong, 2014. "An amoeboid algorithm for solving linear transportation problem," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 398(C), pages 179-186.
    3. Dávid Csercsik & Sándor Imre, 2017. "Cooperation and coalitional stability in decentralized wireless networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(4), pages 571-584, April.
    4. Chen, Jie & Wu, Chao-Yun & Li, Ming & Hu, Mao-Bin, 2019. "Hybrid traffic dynamics on coupled networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 516(C), pages 98-104.
    5. Samuel Ugwu & Pierre Miasnikof & Yuri Lawryshyn, 2023. "Distance Correlation Market Graph: The Case of S&P500 Stocks," Mathematics, MDPI, vol. 11(18), pages 1-13, September.
    6. Yang, Zhirou & Liu, Jing, 2018. "A memetic algorithm for determining the nodal attacks with minimum cost on complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1041-1053.
    7. Yuchen Pan & Shuai Ding & Wenjuan Fan & Jing Li & Shanlin Yang, 2015. "Trust-Enhanced Cloud Service Selection Model Based on QoS Analysis," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-19, November.
    8. Xiao, Guanping & Zheng, Zheng & Wang, Haoqin, 2017. "Evolution of Linux operating system network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 249-258.
    9. Zhang, Bowen & Xia, Yongxiang & Liang, Yuanyuan, 2023. "Effect of transfer costs on traffic dynamics of multimodal transportation networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
    10. Ma, Jinlong & Wang, Peng & An, Zishuo, 2023. "The influence of layered community network structure on traffic capacity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 626(C).
    11. Wu, Jian-Jun & Gao, Zi-You & Sun, Hui-jun, 2008. "Optimal traffic networks topology: A complex networks perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(4), pages 1025-1032.
    12. Xia, Yongxiang & Zhang, Wenping & Zhang, Xuejun, 2016. "The effect of capacity redundancy disparity on the robustness of interconnected networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 447(C), pages 561-568.
    13. Park, Ji Hwan & Chang, Woojin & Song, Jae Wook, 2020. "Link prediction in the Granger causality network of the global currency market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    14. Teqi Dai & Tiantian Ding & Qingfang Liu & Bingxin Liu, 2022. "Node Centrality Comparison between Bus Line and Passenger Flow Networks in Beijing," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    15. Song, Lili & Wu, Yingying & Wu, Moyu & Ma, Jie & Cao, Wei, 2023. "An integrated approach to model connectivity and identify modules for habitat networks," Ecological Modelling, Elsevier, vol. 483(C).
    16. Zhou, Zhen & Gu, Ziyuan & Qu, Xiaobo & Liu, Pan & Liu, Zhiyuan & Yu, Wenwu, 2024. "Urban mobility foundation model: A literature review and hierarchical perspective," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 192(C).
    17. Wang, Zhen & Yu, Chao & Cui, Guang-Hai & Li, Ya-Peng & Li, Ming-Chu, 2016. "Evolution of cooperation in spatial iterated Prisoner’s Dilemma games under localized extremal dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 566-575.
    18. Zhang, Wenping & Xia, Yongxiang & Ouyang, Bo & Jiang, Lurong, 2015. "Effect of network size on robustness of interconnected networks under targeted attack," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 435(C), pages 80-88.
    19. Zhongzhi Xu & Li Sun & Junjie Wang & Pu Wang, 2014. "The Loss of Efficiency Caused by Agents’ Uncoordinated Routing in Transport Networks," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-11, October.
    20. Xi Zhang & Zhili Zhou & Dong Cheng, 2017. "Efficient path routing strategy for flows with multiple priorities on scale-free networks," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:13:y:2025:i:3:p:495-:d:1582169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.