A Data-Driven Framework for the Identification and Vulnerability Assessment of Critical Transfer Hubs in Multimodal Public Transportation Networks: A Case Study of New York City

Multimodal public transportation networks (MPTNs) play an important role in urban mobility by enabling seamless connections across different transportation modes. Within these networks, transfer hubs are especially crucial, as their disruptions can significantly reduce network efficiency and passenger convenience. This paper presents a data-driven framework to identify critical transfer hubs and assess their vulnerability in an MPTN, illustrated by a case study of New York City. Separate network models for bus, subway, rail, and ferry systems were constructed based on Space P and Space L representations, then combined into a unified MPTN by identifying transfer points through an automated data-processing method. These transfer points were clustered into hubs using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. Simulation results from targeted and random failure scenarios indicate that targeted failures at critical transfer hubs substantially reduce network connectivity and efficiency compared to random failures. The analysis identifies hubs such as Pelham Bay Park, Jamaica–179 Street, and 59 Street–Lexington Avenue as particularly vulnerable, highlighting their crucial role in maintaining overall network performance. The findings provide practical insights to improve the resilience and efficiency of MPTNs.