Assessing resilience of urban bike-sharing systems
Weather and land-use influences on demand dynamics
Bibliographic Data
| ID | 19218893 |
|---|---|
| Authors | Xinghang Zhu (0000-0001-8907-2550, McGill University), Xudong Wang (0000-0002-0261-6736, University of Waterloo), Lijun Sun (0009-0001-6484-438X, McGill University), Luis Miranda-Moreno (0000-0003-3322-1814, McGill University, corresponding author) |
| Year | 2026 |
| Volume | 176 |
| Pages | 107152-107152 |
| Publication date | 2026-05-26 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Cities (JOURNAL) |
| Journal identifiers | ISSN: 0264-2751 • E-ISSN: 1873-6084 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.cities.2026.107152 |
| OpenAlex | W7162409671 |
| Language | EN |
| References cited | 34 |
Demand resilience of urban bike-sharing stations varies by land-use factors. • XGBoost-SHAP predicts demand and quantifies land-use impacts under extreme weather. • Higher street density and downtown proximity enhance demand stability. • Recreational and commercial POIs significantly affect demand resilience in Summer. • Connectivity to public transit significantly stabilizes demand in winter weather
Dynamics (music) · Psychological resilience · Resilience (materials science) · System dynamics · Urban resilience · Transportation and Mobility Innovations · Transportation Planning and Optimization · Urban Transport and Accessibility
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A review on bike-sharing
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Machine Learning Approaches to Bike-Sharing Systems
Extracting spatial effects from machine learning model using local interpretation method
Dockless bike-sharing as a feeder mode of metro commute? The role of the feeder-related built environment
Effects of built environment and weather on bike sharing demand
Unlocking Trends
Headwind or tailwind? The evolution of bike-sharing and ride-hailing demand during the Covid-19 pandemic
How does weather affect bikeshare use? A comparative analysis of forty cities across climate zones
Modeling bike-sharing demand using a regression model with spatially varying coefficients
How does extreme temperature affect shared travel? Evidence from bike-sharing order flow in China
Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China
The effects of built environment spatial variation on bike-sharing usage
Exploring the relationship between built environment and spatiotemporal heterogeneity of dockless bike-sharing usage
Understanding bike-sharing mobility patterns in response to the Covid-19 pandemic
An environmental benefit analysis of bike sharing in New York City
Effects of meteorology on bike-sharing
| Highly cited | No |
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