From nodes to hubs: A scalable methodology for identifying and classifying multimodal mobility hubs in the Milan metropolitan area

Multimodal Mobility Hubs have gained increasing attention as a sustainable approach to promoting environmentally friendly transportation in cities. By co-locating multiple transport services, mobility hubs offer efficient multimodal transfers and address issues such as car dependency, congestion, and unequal access to mobility. Building upon these advantages, this paper introduces a reproducible classification method that evaluates both the transport supply (Node) and the physical and functional characteristics of the surrounding context (Place). The methodology readapts the ABC location policy and the Node–Place classification model to systematically identify existing nodes that can serve as multimodal mobility hubs, combining them with comprehensive indicators derived from the mobility hubs literature. Through this approach, the paper illustrates how open and standardized datasets are used to (i) cluster and score transport nodes based on their multimodal offerings, (ii) analyze land use and urban services in their catchment areas, and (iii) compare current conditions with planned transport and land-use transformations. Additionally, this paper introduces a cross-scale approach to support the localization of potential multimodal mobility hubs, their classification, and insights into their future performance. The proposed framework is tested in Milan’s metropolitan area, where it highlights opportunities to enhance multimodality and, alternatively, provides deeper insights from applying transformations in land use and transport supply. Findings show that this approach is scalable and replicable across diverse urban contexts. Ultimately, the paper contributes to evidencebased policy by offering a tool to guide urban and transport planners in locating, selecting, and upgrading mobility hubs, facilitating more sustainable and inclusive mobility networks.