Assessing and improving multi-perspective redundancy at segment level in multimodal transportation networks

Transportation networks, as critical infrastructure systems, remain susceptible to disturbances from natural hazards and intentional attacks. Inherent network redundancy, as an important characteristic of resilience, can mitigate these adverse effects and improve sustainability of cities. However, comprehensive assessment approaches are needed given multiple dimensions of redundancy. This study aims to assess and improve the multi-perspective redundancy at segment level in multimodal transportation networks. By analyzing route, structure, and capacity redundancies, this study provides a holistic framework to evaluate redundancy at segment level. Additionally, an optimization strategy using genetic algorithms was developed to improve multi-perspective redundancy by targeting vulnerable network segments, prioritizing the addition of alternative links in low-redundancy areas. The presented assessment and improvement methods were applied to a multimodal transportation network composed of expressway and railway sub-networks in Beijing-Tianjin-Hebei-Shandong (BTHS) region of China. The findings reveal distinct contributions of road and rail networks to multi-perspective redundancy, which correlated with network density, spatial distribution and capacity allocation. The involvement of alternative transportation modes increased the redundancy across all metrics, emphasizing the advantages of integrated multimodal transportation networks. The proposed improvement approach effectively identified and strengthened city vulnerabilities, enhancing multi-perspective redundancy in the multimodal transportation network while balancing budget constraints. The proposed framework and improvement strategy offer valuable insights for infrastructure planning and provide decision-makers with targeted recommendations for strengthening city resilience and constructing sustainable cities through redundancy enhancement.