Exploring year-to-year spatiotemporal changes in cycling patterns for bike-sharing system in the pre-, during and post-pandemic periods

The bike-sharing system plays an important role during the COVID-19 pandemic due to its benefit of reducing the risk of infection. Previous studies have employed bike trip datasets to analyze the impact of COVID-19 on spatiotemporal cycling mobility, which was limited to a short pandemic period. An understanding of the longterm impact of the pandemic on human travel patterns is urgently needed. This paper presents a framework for investigating the long-term spatiotemporal change in cycling patterns in the pre-, during and post-pandemic periods. A spatiotemporal clustering algorithm is combined with spatiotemporal visualization, network analysis, and important location identification to explore latent mobility patterns. The strength of the proposed framework is that it can model a unified representation of cycling networks across different years, and provide insightful spatiotemporal patterns that allow easy interpretation. The experiments are conducted on five years of data collected from the 'Citibike' system in New York City. The results demonstrate the efficiency of the proposed approach in exploring the evolutionary cycling patterns of how people respond to the COVID-19 pandemic and are beneficial for various stakeholders.