Leveraging causal AI to uncover the dynamics in sustainable urban transport
A bike sharing time-series study
Bibliographic Data
| ID | 21231111 |
|---|---|
| Authors | Tamas Fekete (0000-0002-7513-2839, Constructor University, corresponding author), Girum Mengistu (Constructor University), Hendro Wicaksono (0000-0001-5518-3375, Constructor University) |
| Year | 2025 |
| Volume | 122 |
| Pages | 106240 |
| Publication date | 2025-03-01 |
| 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.2025.106240 |
| OpenAlex | W4408094772 |
| Language | EN |
| Citations received | 2 |
| References cited | 36 |
The importance of developing sustainable urban transportation systems to protect the environment is increasingly recognized worldwide, particularly within the European Union. In the era of digitalization, data-driven approaches are crucial for informed decision-making. This study introduces a methodology leveraging causal artificial intelligence (causal AI) to uncover cause-and-effect relationships in urban transport data. Unlike traditional methods relying on correlations, causal AI identifies the true drivers of transport dynamics. A case study using MOL Bubi bike-sharing data from Budapest demonstrates how the PCMCI (Peter and Clark Momentary Conditional Independence) algorithm revealed complex temporal dependencies within the data, with temperature emerging as the strongest causal factor positively influencing bike usage. Additionally, the reopening of the Chain Bridge led to a 10.7% increase in bike trips, as quantified by Causal Impact analysis. This case study can be extended to more complex scenarios with unpredictable outcomes. The insights gained provide policymakers with a deeper understanding, enabling them to design policies fostering sustainable urban mobility. These results showcase the potential of causal AI to guide policies that enhance sustainable urban mobility. • Causal AI reveals weather’s effect on bike usage in urban transportation. • Case study of MOL Bubi bike-sharing data from Budapest, Hungary. • Bridge reopening impacts bike-sharing, analyzed through event-based methods. • Granger causality and PCMCI uncover temporal links in bike-sharing patterns. • Results support policies for promoting sustainable urban transportation
Bike sharing · Economic geography · Geography · Sociology · Transport engineering · Computer Science · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization · Urban Transport and Accessibility · Geology
Causation, Prediction, and Search
Inferring causal impact using Bayesian structural time-series models
The Estimation of Causal Effects by Difference-in-Difference Methods
Impact of Everyday Weather on Individual Daily Travel Behaviours in Perspective
Machine Learning Approaches to Bike-Sharing Systems
Urban sustainability management
Modelling and assessing long-term urban transportation system resilience based on system dynamics
Unveiling causal dynamics and forecasting of urban carbon emissions in major emitting economies through multisource interaction
Exploring the causal relationship between urbanization and air pollution
Intelligent management of bike sharing in smart cities using machine learning and Internet of Things
Investigating the causal impacts relationship between economic flood damage and extreme precipitation indices based on ARDL-ECM framework
Causal impacts of the Covid-19 pandemic on daily ridership of public bicycle sharing in Seoul
Generating sparse origin–destination flows on shared mobility networks using probabilistic graph neural networks
The AI gambit
Effects of built environment and weather on bike sharing demand
| Citations per year | 2 |
|---|---|
| Citation span | 2025 - 2026 (2) |
| Highly cited | No |