Spatiotemporal Patterns of Electric Bike Sharing Demand and Their Nonlinear Relationships with the Built Environment: A Case Study in Yangzhou, China

Abstract Electric bike sharing (EBS), a convenient and efficient mobility solution, has become an integral part of urban transportation systems in many cities. To promote the sustainable development of EBS systems, it is vital to understand the spatiotemporal patterns of EBS demand and their relationships with influencing factors. Using 1 month of EBS trip data from Yangzhou, China, this study applied a nonnegative matrix factorization (NMF) model, combined with the space-time cube model and emerging hot spot analysis, to identify spatiotemporal patterns of EBS demand. The results show that EBS demand exhibits four distinct temporal patterns: (1) morning peak, (2) daytime, (3) evening peak, and (4) nighttime, each with unique spatial hot spot distributions. Subsequently, the study employed Light Gradient Boosting Machine (LightGBM) models to explore the complex relationships between EBS demand and the built environment within each of these patterns, with a pooled model as a benchmark. The analysis reveals that the relative importance of variables varies significantly across patterns. For instance, the importance of the percentage of residential district peaks in the morning peak pattern, while the percentage of commercial district and educational institutions are more prominent in the daytime, evening peak, and nighttime patterns. Moreover, these relationships exhibit pronounced nonlinearity. For example, the positive correlation between population density and EBS demand levels off at 2,000 people / km 2 for the morning peak pattern, but not until 5,000 people / km 2 for other patterns. These findings can guide operators in formulating refined operational strategies and assist policymakers in developing targeted interventions.