Understanding the vulnerability of multimodal public transportation networks

Urban multimodal transportation networks are crucial for social and economic development but remain highly vulnerable to natural hazards. Existing research has primarily focused on single-mode systems, overlooking intermodal interdependencies that shape passenger flow redistribution and can trigger cascading failures. This study proposes a model framework for assessing the vulnerability of multimodal public transportation networks by combining geospatial data with network science-based methods. A bidirectional subway–tram network is constructed for Amsterdam to validate the feasibility, where disruption scenarios are simulated combining percolation analysis and connectivity loss metrics. The results show that intermodal transfer links substantially improve network redundancy and that neglecting them in simulations leads to an overestimation of connectivity losses. Critical links and stations without sufficient alternatives are identified as key vulnerabilities under disruptions. Percolation analysis further captures degradation processes from minor to large-scale failures, offering a more comprehensive perspective than single-event simulations. Overall, the study demonstrates the feasibility of geospatial-based multi-layer network analysis as a decision-support tool for enhancing the resilience of urban multimodal public transportation systems. Moreover, this framework can be extended to incorporate additional modes and passenger demand data in future applications.