The rise of shared e-bike systems presents a promising solution for sustainable urban mobility, yet their operational efficiency is often hampered by unpredictable user demands. This challenge directly impacts the achievement of SDG 11 by creating service inconsistencies that can deter users. To address this, we propose a data-driven methodology for optimizing resource allocation in shared e-bike systems. Based on large-scale trip data from Ningbo, China, our analysis reveals significant spatiotemporal demand regularities at a fine-grained, cell-based level, including pronounced commuting peaks and clear spatial heterogeneity between high- and low-demand zones. Building upon these findings, we implement a SARIMAX model to generate accurate, hourly, day-ahead demand forecasts that incorporate key contextual information. Our results indicate that the SARIMAX model provides substantial improvements in predictive accuracy while offering superior interpretability and practical computational efficiency. The resulting forecasts enable data-informed decision-making for critical operations such as fleet rebalancing, battery swapping, and parking zone management. This study provides a robust and routine transparent tool for shared mobility operators, demonstrating how industrial engineering principles and statistical modeling can directly enhance the sustainability and user experience of urban transportation systems.
Ma et al. (Tue,) studied this question.