Machine Learning Approaches to Bike-Sharing Systems
A Systematic Literature Review
Bibliographic Data
| ID | 22032977 |
|---|---|
| Authors | Vitória Albuquerque (0000-0001-9684-968X, Universidade Nova de Lisboa, corresponding author), Miguel Sales Dias (0000-0002-1445-2695, Iscte – Instituto Universitário de Lisboa), Fernando Bação (0000-0002-0834-0275, Universidade Nova de Lisboa) |
| Year | 2021 |
| Volume | 10 |
| Issue | 2 |
| Pages | 62 |
| Publication date | 2021-02-02 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi10020062 |
| OpenAlex | W3128136982 |
| Language | EN |
| Citations received | 10 |
| References cited | 39 |
Cities are moving towards new mobility strategies to tackle smart cities’ challenges such as carbon emission reduction, urban transport multimodality and mitigation of pandemic hazards, emphasising on the implementation of shared modes, such as bike-sharing systems. This paper poses a research question and introduces a corresponding systematic literature review, focusing on machine learning techniques’ contributions applied to bike-sharing systems to improve cities’ mobility. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) method was adopted to identify specific factors that influence bike-sharing systems, resulting in an analysis of 35 papers published between 2015 and 2019, creating an outline for future research. By means of systematic literature review and bibliometric analysis, machine learning algorithms were identified in two groups: classification and prediction
Bike sharing · Data science · Machine learning · MEDLINE · Multimodality · Political science · Systematic review · Transport engineering · World Wide Web · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility · Artificial Intelligence
Exploring the Multiscale Relationship between the Built Environment and the Metro-Oriented Dockless Bike-Sharing Usage
Exploring the determinants of bike-sharing member trips with explainable machine learning
Long-term bike-sharing demand forecasting and interpretability analysis considering Urban Heat Island (UHI)
Criteria and methods in nuclear power plants siting
Economic Optimization of Bike-Sharing Systems via Nonlinear Threshold Effects
Conflating point of interest (POI) data
Leveraging causal AI to uncover the dynamics in sustainable urban transport
Predicting street-level distribution of bike-sharing traffic volume in metro station areas using integrated generative adversarial networks
Urban accessibility and digital transformation
Assessing resilience of urban bike-sharing systems
The promises of big data and small data for travel behavior (aka human mobility) analysis
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
Comparing cities’ cycling patterns using online shared bicycle maps
The benefits of publishing systematic quantitative literature reviews for PhD candidates and other early-career researchers
The urban sustainable development goal
Software survey
| Citations per year | 2.5 |
|---|---|
| Citation span | 2022 - 2026 (5) |
| Highly cited | No |