Bike-sharing systems for sustainable cities
Demand prediction using graph neural networks and spatial aggregation
Bibliographic Data
| ID | 21233745 |
|---|---|
| Authors | Igor Betkier (0000-0002-5437-7669, Military University of Technology in Warsaw, corresponding author), Mateusz Oszczypała (0000-0002-1194-6913, Military University of Technology in Warsaw), Wojciech Dawid (0000-0001-7964-5706, Military University of Technology in Warsaw) |
| Year | 2025 |
| Volume | 128 |
| Pages | 106479 |
| Publication date | 2025-06-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Sustainable Cities and Society (JOURNAL) |
| Journal identifiers | ISSN: 2210-6707 • E-ISSN: 2210-6715 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.scs.2025.106479 |
| OpenAlex | W4410635362 |
| Language | EN |
| Citations received | 6 |
| References cited | 58 |
Demand forecasting in bike-sharing systems is crucial for efficient urban bike fleet management and enhanced user satisfaction. This study offers a comprehensive analysis of Warsaw’s bike-sharing system over a cycling season, incorporating temporal, spatial, environmental, and weather-related data. By aggregating station-level information into centroid-based cells defined on a kilometer grid, the approach effectively captures complex spatiotemporal interactions. Various Graph Neural Network (GNN) architectures were evaluated, with a broad range of hyperparameters tested to identify the most accurate predictive models. These models were assessed by their ability to forecast the volume of bike trips between stations, yielding results adaptable to complex urban contexts. Findings underscore the significance of weather, local infrastructure distribution, and temporal patterns in shaping bike usage. The resulting predictive models enable strategic decisions in station planning, bike redistribution, and overall fleet management, thereby increasing availability and improving system performance. Achieving R2 scores above 0.91 and MAE below 0.37 highlights the high precision of these models. The introduction of an innovative spatial aggregation strategy, along with an extensive exploration of GNN architectures, marks a key contribution of this work. This methodology can be transferred to other major cities aiming to enhance their bike-sharing operations and foster sustainable urban mobility
Artificial neural network · Business · Geography · Graph · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation and Mobility Innovations · Urban Transport and Accessibility · Artificial Intelligence · Theoretical Computer Science
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Data-driven bike-sharing rebalancing for smart cities
Identifying and ranking high-demand areas in a bike-sharing system based on extreme value theory
Does street shade promote green travelling in summer? Evidence from bike-sharing in Shanghai
Adding bike simulation capacity to an activity–based travel demand model and testing policy scenarios
Theory-informed and interpretable graph learning for urban commuting flows
A review on bike-sharing
A spatiotemporal and graph-based analysis of dockless bike sharing patterns to understand urban flows over the last mile
A Comprehensive Survey on Graph Neural Networks
Understanding spatial-temporal travel demand of free-floating bike sharing connecting with metro stations
Revealing the driving factors and mobility patterns of bike-sharing commuting demands for integrated public transport systems
The association between travel demand of docked bike-sharing and the built environment
Impact of environment on bicycle travel demand—Assessment using bikeshare system data
Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks
Examining spatio-temporal mobility patterns of bike-sharing systems
Perceptually important points of mobility patterns to characterise bike sharing systems
Understanding the impacts of a public transit disruption on bicycle sharing mobility patterns
Spatiotemporal comparative analysis of scooter-share and bike-share usage patterns in Washington, D.C
Bike-share rebalancing strategies, patterns, and purpose
Examining spatiotemporal changing patterns of bike-sharing usage during Covid-19 pandemic
Spatio-temporal patterns of a Public Bicycle Sharing Program
Investigation on the effects of weather and calendar events on bike-sharing according to the trip patterns of bike rentals of stations
An empirical analysis of dockless bike-sharing utilization and its explanatory factors
Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China
Deciphering urban bike-sharing patterns
| Citations per year | 6 |
|---|---|
| Citation span | 2026 - 2026 (1) |
| Highly cited | No |