###### Abstract
The use of bikes positively impacts the environment, as they produce minimal air and noise pollution compared to motor vehicles [9; 10]. Bikes can substitute cars on short-distance trips and have the potential of solving the \"last mile\" problem in urban traffic [11; 12; 13]. In addition, as a form of physical activity, cycling brings remarkable benefits to people's health [14]. Given the above-mentioned advantages of bike-sharing, the trajectory data from bike-sharing systems have become an ideal source for analyzing intra-urban human mobility characteristics and identifying typical intra-urban human mobility patterns. A comprehensive understanding of the spatio-temporal characteristics and patterns of bike-sharing usage is crucial for developing effective bike management and scheduling strategies. Such an understanding can provide a scientific basis for these strategies, thereby enhancing the development and service of bike-sharing systems. The low cost and convenience of bike-sharing offer an alternative transport mode for short-distance trips, influencing people's travel behaviors and mobility [15; 16; 17]. Promoting bike-sharing services can help mitigate typical urban issues such as traffic congestion and automobile exhaust pollution. Therefore, studying bike-sharing has significant implications for understanding human behaviors and lifestyles as well as assessing city development. Recently, bike-sharing has become a popular research topic, with bike-sharing systems being classified into two categories: docked bike-sharing and dockless bike-sharing. Dockless bike-sharing offers an advantage over docked bike-sharing by providing stationless rental services, allowing users to rent bikes via a GNSS-based smartphone app without needing to use fixed docking stations [3; 18; 19]. Consequently, dockless bike-sharing trajectory data are a valuable data source for many studies. Typical research topics related to the spatio-temporal analysis of dockless bike-sharing data include understanding the usage and patterns of shared bikes [18; 20; 21; 22; 23; 24; 25], identifying urban functional regions [26; 27; 28; 29; 30], evaluating spatial efficiency [31; 32; 33], optimizing bike resource allocation [34; 35; 36; 37], planning bike lanes [38; 39; 40; 41], and predicting bike usage based on various factors [37; 42; 43; 44]. To facilitate the understanding of the formation mechanism of public bicycle networks, it is essential to characterize intra-urban human mobility patterns. This can be achieved through statistical analyses of human behavior characteristics in space and time, focusingon both the origin and destination of trips. For bike-sharing companies to provide better services and manage large volumes of shared bikes effectively, specific statistical analyses of individual bikes are crucial. Additionally, understanding the evolving mechanism of public bicycle network structures requires focusing on the behavior characteristics of cyclists (i.e., bike users). By summarizing existing studies, we found a lack of focused research on characterizing intra-urban human mobility patterns, particularly concerning the temporal aspects of trip information. Therefore, in this paper, we propose to characterize the temporal patterns of intra-urban human mobility through trip analysis to summarize typical intra-urban human mobility patterns regarding the temporal aspects. A trip is considered the intra-urban mobility of a bike user during a specific time period. The study was conducted at two different levels: the trip level and the bike level. At each level, corresponding analyses were conducted at varying temporal scales. The methods used for the analysis mainly include exploratory data analysis (EDA) and the continuous triangular model (CTM). Our aim was to conduct a detailed analysis of bike-sharing trips to characterize typical temporal patterns of intra-urban human mobilities.