Modeling shared e-micromobility as a label propagation process for detecting overlapping communities

Abstract

Shared micro-mobility such as electric scooters (e-scooters) has gained significant popularity in many cities. While many studies have analyzed the spatiotemporal patterns of shared micro-mobility using individual-level trip data, the spatial structure of e-scooter mobility networks and their socio-economic implications remain underexplored. Examining these mobility networks through the lens of network science — such as analyzing their community structures — can provide valuable insights for urban policy and planning. For example, allocating e-scooters at the overlapping locations of two communities may improve the operational efficiency of e-scooter distribution. However, existing methods for detecting community structures in mobility networks often overlook potential overlaps between communities. In this study, we conceptualize shared micro-mobility in urban spaces as a process of information exchange, where locations are connected through e-scooters, facilitating the interaction and propagation of community affiliations. As a result, similar locations are assigned the same label. Based on this concept, we developed a Geospatial Interaction Propagation model (GIP) by designing a Speaker-Listener Label Propagation Algorithm (SLPA) that accounts for geographic distance decay, incorporating anomaly detection to ensure the derived community structures reflect meaningful spatial patterns.We applied this model to detect overlapping communities within the e-scooter system in Washington, D.C. The results demonstrate that our algorithm outperforms existing model of overlapping community detection in both efficiency and modularity. Additionally, we discovered significant social segregation within the overlapping communities: areas belong to multiple communities tend to be wealthier with shorter commute times. Our results provide a potential explanation for the community structure in human mobility networks and may offer insights for urban planning and policymaking aimed at creating a more equitable and accessible mobility system.