Cited by

• 2021The effects of built environment spatial variation on bike-sharing usage: A case study of Suzhou, China116 cited
• 2021How did micro-mobility change in response to COVID-19 pandemic? A case study based on spatial-temporal-semantic analytics115 cited
• 2021Inferring the trip purposes and uncovering spatio-temporal activity patterns from dockless shared bike dataset in Shenzhen, China96 cited
• 2021Examining factors associated with bike-and-ride (BnR) activities around metro stations in large-scale dockless bikesharing systems94 cited
• 2021Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations63 cited
• 2021Green travel mobility of dockless bike-sharing based on trip data in big cities: A spatial network analysis57 cited
• 2022Unravel the spatio-temporal patterns and their nonlinear relationship with correlates of dockless shared bikes near metro stations49 cited
• 2022Examining the varying influences of built environment on bike-sharing commuting: Empirical evidence from Shanghai48 cited
• 2023A multi-activity view of intra-urban travel networks: A case study of Beijing40 cited
• 2022Dynamic Intra-Cell Repositioning in Free-Floating Bike-Sharing Systems Using Approximate Dynamic Programming37 cited
• 2023A Safe Infrastructure for Micromobility: The Current State of Knowledge34 cited
• 2022A hybrid big-data-based and tolerance-based method to estimate environmental benefits of electric bike sharing34 cited
• 2024The association between travel demand of docked bike-sharing and the built environment: Evidence from seven US cities29 cited
• 2024Exploring the relationship between built environment and spatiotemporal heterogeneity of dockless bike-sharing usage: A case study of Shenzhen, China25 cited
• 2024Measuring exposure and contribution of different types of activity travels to traffic congestion using GPS trajectory data24 cited
• 2022Identifying trip purpose from a dockless bike-sharing system in Manchester24 cited