Modeling shared e-micromobility as a label propagation process for detecting overlapping communities
Datos Bibliográficos
| ID | 7150581 |
|---|---|
| Autores | Ping Luo (0000-0003-1126-0329, Massachusetts Institute of Technology), Peng Luo (0000-0002-3680-8509), Chengyu Song (0000-0003-3650-6794, University of Alabama), Hao Li (0000-0001-5192-5458, National University of Singapore), Di Zhu (0000-0002-3237-6032), Zhu Di (0009-0000-3549-4731, University of Minnesota), Songhua Hu (0000-0002-0731-3080, Massachusetts Institute of Technology), Fábio Duarte (0000-0003-0909-5379, Massachusetts Institute of Technology, autor de correspondencia) |
| Año | 2025 |
| Volumen | 122 |
| Páginas | 102336 |
| Fecha de publicación | 2025-12-01 |
| Peer Reviewed | Sí |
| Open Access | Sí |
| Tipo | ARTICLE |
| Revista | Computers Environment and Urban Systems (JOURNAL) |
| Identificadores de la revista | ISSN: 0198-9715 • E-ISSN: 1873-7587 |
| Editorial | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.compenvurbsys.2025.102336 |
| OpenAlex | W4414150780 |
| Idioma | EN |
| Referencias citadas | 40 |
Community structure · Geographic information system · Geospatial analysis · Mobility model · Popularity · Spatial network · Through-the-lens metering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility
Applied Logistic Regression
OpenStreetMap
From local explanations to global understanding with explainable AI for trees
Modularity and community structure in networks
Understanding spatiotemporal trip purposes of urban micro-mobility from the lens of dockless e-scooter sharing
A spatiotemporal dynamic analyses approach for dockless bike-share system
Extracting spatial effects from machine learning model using local interpretation method
E-bikes and urban transportation
Spatial analysis of shared e-scooter trips
Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems
Delineating borders of urban activity zones with free-floating bike sharing spatial interaction network
Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C
Examining spatiotemporal changing patterns of bike-sharing usage during Covid-19 pandemic
Complementary or Competing? Studying the Relationship between E-Scooter Sharing and Bikesharing in Austin, Texas
Understanding Place Characteristics in Geographic Contexts through Graph Convolutional Neural Networks
| Velocidad de citación | historical |
|---|---|
| Altamente citado | No |