Leveraging causal AI to uncover the dynamics in sustainable urban transport: A bike sharing time-series study

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.