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

Linghao Ren, Xinyue Li, Renjie Song, Yuning Wang, Meiyun Gui and Bo Tang ()
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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

Mathematics, 2025, vol. 13, issue 13, 1-37

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.

Keywords: urban transportation networks; multi-objective optimization; hierarchical modeling; machine learning; transportation planning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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