## 5 Conclusions  
Human mobility is an important research focus in many disciplines, including geographical information science. This paper mainly concentrates on intra-urban human mobility. Given that a large proportion of intra-urban trips are relatively short, bike-sharing data serve as an excellent way to characterize these mobility patterns. Accordingly, this study aimed to characterize the temporal patterns of intra-urban human mobility by analyzing bike-sharing trips using either an exploratory data analysis (EDA) or the continuous triangular model (CTM). A case study was conducted using the bike-sharing trip data in Shanghai, China. The study explored temporal patterns at two levels: the trip level and the bike level. At each level, we examined and characterized the temporal patterns of intra-urban human mobility across various scales, such as per hour, per day, per week, and considering both origin and destination.  
The study identified several key temporal patterns in intra-urban human mobility through bike-sharing systems. Most trips are short, with durations typically under 50 min and peaking between 6 and 8 min. The analysis revealed distinct daily patterns, such as shorter trips during morning peaks and longer trips in the evening, with a tendency for trips to be longer during non-working hours, early mornings, and late evenings, when public transport options are limited. Usage patterns showed that the majority of bikes are utilized fewer than five times within the study period. Additionally, periodic behaviors in bike usage were observed, reflecting the rhythms of daily and weekly life. These findings suggest that bike-sharing systems are primarily used for short, frequent trips and that user behavior varies significantly depending on the time of day and availability of alternative transportation. These findings can provide valuable suggestions and insights to governmental departments and bike-sharing companies, assisting in traffic management, policy-making, and strategy adjustments to improve environmental conditions and public health.  
Given the importance of bike-sharing systems under the national strategy of \"carbon neutrality\" in China, the analysis of bike-sharing data is becoming increasingly important. While this paper offers a detailed analysis of trip numbers and trip durations from various perspectives, it primarily focuses on the temporal dimension. Due to space limitations and specific research objectives, a detailed analysis of trips on the spatial dimension (such as trip distances and trip coverages) was not included in this study. This will be the focus of our future work, where we anticipate uncovering novel findings.  
Conceptualization, P.Z. and M.L.; data curation, J.X.; formal analysis, P.Z., M.L. and J.X.; funding acquisition, P.Z.; investigation, P.Z.; methodology, P.Z., M.L. and J.X.; software, Z.Z. and R.C.; validation, P.Z.; visualization, R.C. and Z.Z.; writing--original draft, P.Z.; writing--review and editing, P.Z., M.L. and J.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by grants from the Introduction Program of High-Level Innovation and Entrepreneurship Talents in Jiangsu Province (Grant No. CZ032 SC20025), the NUPTSF (Grant No. NY219140), and the Smart Health Big Data Analysis and Location Services Engineering Lab of Jiangsu Province (Grant No. SHEL221004).  
Not applicable.  
The original data presented in the study are openly available in FigShare at [[https://doi.org/10.6084/m9.figshare.25975015.v1](https://doi.org/10.6084/m9.figshare.25975015.v1)]([https://doi.org/10.6084/m9.figshare.25975015.v1](https://doi.org/10.6084/m9.figshare.25975015.v1)).  
The authors would like to thank the anonymous reviewers for their valuable comments and suggestions to improve the manuscript.  
The authors declare no competing interests.