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

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 d...

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Vydané v:Mathematics (Basel) Ročník 13; číslo 13; s. 2061
Hlavní autori: Ren, Linghao, Li, Xinyue, Song, Renjie, Wang, Yuning, Gui, Meiyun, Tang, Bo
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.07.2025
Predmet:
Algorithms
Analysis
Buses
Clustering
Communications traffic
Datasets
Design
Dijkstra's algorithm
Efficiency
Emergency response
Fault tolerance
hierarchical modeling
Infrastructure
Infrastructure (Economics)
Linear programming
Local transit
machine learning
Monte Carlo simulation
multi-objective optimization
Network management systems
Network topologies
Optimization
Optimization models
Public transportation
Redundancy
Resilience
Roads & highways
Robustness (mathematics)
Simulated annealing
Simulation methods
Simulation models
Structural failure
Tolls
Topology optimization
Traffic flow
Transportation networks
Transportation planning
Transportation systems
Urban transportation
urban transportation networks
Maryland
United States > US
Baltimore Maryland
ISSN:2227-7390, 2227-7390
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Popis
Shrnutí: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.
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ISSN:2227-7390
2227-7390
DOI:10.3390/math13132061