Multiplex Centrality-Driven Analysis for Enhancing UrbanTransport Resilience and Infrastructure Planning

Abstract

Urban and national transportation systems are inherently multiplex, comprising interconnected transport modes whose resilience is essential for maintaining mobility during disruptions. This study introduces a weighted multiplex centrality framework for identifying influential nodes in multimodal transport networks. The proposed metric integrates intralayer degree and eigenvector centralities with density-based layer weights and correlation-based interlayer coupling, enabling the evaluation of node importance based on both within-layer connectivity and cross-layer structural influence. To strengthen the methodological validation, the framework was evaluated using four synthetic multiplex transport network configurations: Erdős–Rényi random, Barabási–Albert scale-free, Watts–Strogatz small-world, and transport-inspired heterogeneous networks. The results demonstrate that the proposed metric produces stable and interpretable network degradation patterns across diverse topological conditions and performs competitively with average degree and average eigenvector centrality measures in sequential node-removal experiments. The framework was further validated using a real-world Sri Lankan transport network modeled as a strict multiplex system comprising 542 physical transport locations distributed across bus, railway, and domestic flight layers. The proposed method identified Colombo as the most influential node, followed by major transport hubs, including Polgahawela Junction, Ragama Junction, Maho Junction, Peradeniya Junction, and Maradana. Transfer-aware node-removal analysis further demonstrated that these highly ranked nodes make substantial contributions to network cohesion and navigability. Overall, the proposed framework provides a flexible, interpretable, and mathematically grounded tool for assessing transport network resilience, prioritizing critical infrastructure, supporting emergency preparedness, and informing strategic investment decisions.