###### Abstract  
Human mobility, encompassing the movement of individuals and/or groups across space and time, significantly impacts various aspects of society, with intra-urban mobility being a major research focus of scholars in diverse disciplines. Bike-sharing systems have become an alternatives in cities for achieving more sustainable transportation. Hence, bike-sharing-related data are considered an important data source to study intra-urban human mobility. To better understand human mobility in cities, it is essential to characterize the typical patterns involved in intra-urban human mobility. This paper mainly focuses on characterizing the temporal patterns of intra-urban human mobility on bike-sharing based on the trip information of the acquired bike-sharing data. To achieve this, on the one hand, we adopted an exploratory data analysis (EDA) method to describe the temporal patterns by performing exploratory analyses of bike-sharing trips. On the other hand, we used the continuous triangular model (CTM) to conduct multi-temporal-scale analysis of bike-sharing trips for further explorations of the temporal patterns where necessary. The data of bike-sharing trips in Shanghai, China, were adopted as the dataset for the case study. Generally, the study was conducted at two different levels: the trip level and the bike level. Specifically, at each level, the explorations were conducted from varying perspectives. According to the analyses, numerous meaningful temporal patterns were discovered, and several distinctive findings were acquired. The results of this study show the effectiveness of the EDA and CTM methods in characterizing temporal patterns of intra-urban human mobility, based on which potentially insightful information and suggestions can be provided to assist related actions. human mobility; intra-urban mobility; trajectory data; bike-sharing data; exploratory data analysis; the continuous triangular model +
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characterized by the regularities and periodicities that mark human lives. In urban areas, daily movements of people (e.g., residents) inevitably involve interactions with various urban components in the built environment, such as roads, commercial areas, and public facilities [5]. While the study of human mobility currently spans several disciplines, geography was arguably the first to analyze mobility data and develop theories to describe travel patterns [6]. In recent years, extensive geo-located datasets related to human movement have become available, enabling researchers to quantitatively study individual and collective mobility patterns and to generate models that capture and reproduce the spatio-temporal structures and regularities in human trajectories. Nowadays, a variety of urban human mobility data have been gathered and published, benefiting from the proliferation of location-aware techniques such as the global navigation satellite system (GNSS), Bluetooth, radio frequency identification (RFID), and Wi-Fi [7]. These data contain rich information about locations and can help address many urban challenges such as traffic congestion, air pollution problems, estimating migratory flows, and public health. Therefore, characterizing intra-urban human mobility patterns is crucial for understanding and optimizing the travel of human beings. Accompanied with the promulgation of the slogan \"low-carbon life, green travel\", the strategy of \"carbon neutrality\", and the consumption mode of \"sharing economy\" worldwide, environmentally friendly shared transit systems have been widely adopted by local citizens and advocated by governments in many countries. As a new form of urban transportation, bike-sharing has attracted many researchers to study human mobility due to its low cost for production and great convenience for uses [8]. Especially after the COVID-19 pandemic, bike-sharing has gradually become one of the most widespread and popular shared and sustainable mobility systems.