On the Resilience Evaluation of Urban Multimodal Transportation Network Considering Dynamic Travel Demand

Abstract

The urban multimodal transportation network is an essential urban infrastructure for daily mobility, while it is vulnerable to severe disturbances. Existing research often evaluates multimodal network resilience and criticality from either a structural or operational perspective, overlooking its multidimensional characterization. Most studies incorporating dynamic demand are conducted at daily or hourly intervals, neglecting finer temporal granularity that better captures network resilience and criticality. To address these gaps, this study proposes a comprehensive resilience evaluation method for multimodal transportation networks by integrating network structure and function. Node criticality is identified using a novel demand growth rate indicator. Various disturbance scenarios, including random and deliberate disturbances, are constructed to simulate sudden events, considering the impacts of the disturbance scale and intensity of nodes or edges. Moreover, an affected demand redistribution model is developed by combining graph convolutional network (GCN) and the Logit model, considering travel time, distance, transfer numbers, and path complexity. The proposed methods are applied to the multimodal transportation network in Tianjin, China, using transit smart card transaction data. Results reveal multimodal networks exhibit better resistance from a structural perspective, while the subway network achieves higher efficiency when the disturbance scale is less than 0.2. A threshold effect emerges between disturbance scale and residual passenger capacity. Node disturbances cause an average of 21% higher performance losses than edge disturbances. This method quantifies resilience and identifies the critical nodes considering minute-level dynamic travel demand, dynamic demand between nodes, and travel behaviors. These insights support decision-makers in generating more effective response strategies.