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.

This study proposes a multi-dimensional urban transportation network optimization framework to address structural failure risks caused by aging infrastructure and regional connectivity efficiency bottlenecks, establishing a hierarchical progressive resilience enhancement system. Using the Baltimore road network as a case study, we first develop a road network traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies to inform decision-making regarding the reconstruction of the collapsed Francis Scott Key Bridge. Subsequently, we develop a dynamic adaptive public transit network optimization model that employs an entropy weight method-TOPSIS multi-objective decision framework coupled with an improved adaptive simulated annealing algorithm, achieving coordinated optimization of suburban-urban dual-layer networks. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 45,000 to 32,100) and ensured global public transit network topological connectivity (22.4% average path redundancy improvement). Innovatively proposing a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning, our community unit resilience enhancement strategy achieves 30.4%–44.6% traffic load variance reduction (from (1.68--2.74)×107 to (1.12--1.52)×107) in typical regions with minimal change costs. Notably, we conducted comprehensive hyperparameter experiments to investigate algorithm robustness and scenario-specific parameter configurations. The results demonstrate consistent optimization trends across different parameter combinations, indicating excellent model stability. Specifically, we identified two optimal operational modes: (1) For time-sensitive scenarios like emergency response, an aggressive configuration combining rapid cooling (cooling rate=0.90) with multi-node coordination (node change num=5) achieves 85% of maximum improvement within 50 epochs; (2) For long-term planning, a conservative strategy using slow cooling (cooling rate=0.99) with extended training epochs yields superior global optima, showing 12.7% better final solution quality than rapid-cooling alternatives.The research reveals that this framework forms a multi-objective optimization paradigm encompassing structural resilience, functional connectivity, and cost control through the coupling effect of dynamic optimization algorithms and hierarchical modeling, providing theoretical and methodological support for sustainable smart city transportation system renewal.

Chaotic systems serve as fundamental pseudo-random sequence generators in encryption algorithms and play a vital role in communication security. However, most current research still focus on the classical Logistic chaotic map, making it vulnerable to targeted attacks. To address this issue, this paper proposes a general construction method for a class of cubic chaotic maps over the real number field and proves the existence of chaos based on the robust chaos criterion for S-unimodal maps. Furthermore, by integrating the proposed cubic chaotic map with the infinite folding map, a new one-dimensional discrete chaotic map is developed. Dynamical analysis demonstrates that, compared with the infinite folding map and the Logistic map, the newly constructed map exhibits stronger chaotic behavior and more stable complexity, showing superior potential for practical applications in secure communications.