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

2025-01-27

AI Summary

Existing methods for identifying functional communities in urban e-scooter mobility overlook the inherent overlap among communities, leading to structural distortion. This paper proposes the Geospatial Interaction Propagation (GIP) model, the first to formalize shared micromobility as a label propagation process with geographically decaying interaction weights. GIP integrates an enhanced Speaker–Listener Label Propagation Algorithm (SLPA) with an anomaly detection mechanism to enable spatially coherent and fully automated discovery of overlapping communities. Experiments on real-world e-bike trip data from Washington, D.C. demonstrate that GIP significantly improves modularity and computational efficiency while accurately uncovering multi-centered, nested functional urban structures. The approach advances the understanding of dynamic spatial organization in cities, establishing a novel paradigm for functional community detection in urban mobility networks.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy
• Web Mining and Content Analysis: Models for Web evolution

Abstract

Shared micro-mobility such as e-scooters has gained significant popularity in many cities. 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. 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.

Problem

• Urban Scooter Commute
• Community Detection
• Overlap Recognition

Innovation

• Geospatial Interaction Propagation (GIP) Model
• Anomaly Detection
• Intersecting Community Identification