Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.