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Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling

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  • Linghao Ren

    (School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China)

  • Xinyue Li

    (School of Economics and Management, Shandong Jiaotong University, Jinan 250353, China)

  • Renjie Song

    (School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China)

  • Yuning Wang

    (School of Artificial Intelligence, Guilin University of Electronic Technology, Guilin 541004, China)

  • Meiyun Gui

    (School of Business, Guilin University of Electronic Technology, Guilin 541004, China)

  • Bo Tang

    (School of Mathematics and Computing Science, Guilin University of Electronic Technology, Guilin 541004, China)

Abstract

This study proposes a multi-dimensional urban transportation network optimization framework (MTNO-RQDC) to address structural failure risks from aging infrastructure and regional connectivity bottlenecks. Through dual-dataset validation using both the Baltimore road network and PeMS07 traffic flow data, we first develop a traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies for guiding reconstruction planning for the collapsed Francis Scott Key Bridge. Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. The framework introduces three key innovations: (1) a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning; (2) fault-tolerant network topology optimization demonstrated through 1000-epoch Monte Carlo failure simulations; (3) cross-dataset transferability validation showing 15.7% performance variance between Baltimore and PeMS07 environments. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 42,760 to 32,100), 22.4% improvement in public transit path redundancy, and 30.4–44.6% decrease in regional traffic load variance with minimal costs. Hyperparameter analysis reveals two optimal operational modes: rapid cooling (rate = 0.90) achieves 85% improvement within 50 epochs for emergency response, while slow cooling (rate = 0.99) yields 12.7% superior solutions for long-term planning. The framework establishes a new multi-objective paradigm balancing structural resilience, functional connectivity, and computational robustness for sustainable smart city transportation systems.

Suggested Citation

  • Linghao Ren & Xinyue Li & Renjie Song & Yuning Wang & Meiyun Gui & Bo Tang, 2025. "Multiphase Transport Network Optimization: Mathematical Framework Integrating Resilience Quantification and Dynamic Algorithm Coupling," Mathematics, MDPI, vol. 13(13), pages 1-37, June.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:13:p:2061-:d:1684343
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    References listed on IDEAS

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    1. Di, Zhen & Yang, Lixing & Qi, Jianguo & Gao, Ziyou, 2018. "Transportation network design for maximizing flow-based accessibility," Transportation Research Part B: Methodological, Elsevier, vol. 110(C), pages 209-238.
    2. Guo-Ling Jia & Rong-Guo Ma & Zhi-Hua Hu, 2019. "Urban Transit Network Properties Evaluation and Optimization Based on Complex Network Theory," Sustainability, MDPI, vol. 11(7), pages 1-16, April.
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