## 1 Introduction  
As an economical, convenient, and eco-friendly travel mode, bike-sharing systems have grown dramatically worldwide during the last decade [1]. The systems can help relieve air pollution and traffic congestion, and bring health benefits by involving more physical activities [2]. A bike-sharing system is an access/egress mode service for public transport. It supports multimodal transport connections and increases the reachable areas of public transit [3]. By March 2021, 2012 bike-sharing programs have been put in operation and 300 others are under construction around the world [4]. Bike-sharing systems can be categorized into the following two types: a docked bike-sharing system and dockless bike-sharing system [5]. The docked bike-sharing system hasstationary docking stations for users to rent and return bikes. For the dockless bike-sharing system, riders can use mobile phone applications (APPs) to rent and return bikes in the designated physical or electric-fencing areas [6; 7]. These two bike-sharing systems help promote urban mobility. However, because user demand distribution is asymmetric from a spatial and temporal perspective, shared bike distribution is unbalanced. In order to better satisfy user demand and assist operators in scheduling the optimal routes and rebalancing the timetable, it is necessary to accurately predict the short-run bike-sharing demand in advance to improve the efficacy of bike-sharing systems [8]. Users could better plan travel strategies and change the origins/destinations in advance, which will eventually help balance the systems without rebalancing the shared bikes. Furthermore, short-term predictive models could also be applied to design a dynamic redistribution strategy and better balance the shared bikes distribution between saturated and non-saturated stations/regions [9]. Previous studies mainly focus on the predictions of shared-bike usage at the city level [10], station cluster level [11], and station/grid level [10]. Although the bike-sharing usage aggregating at the city level and cluster level does simplify the problem, it neglects the characteristics of and interactions among stations and fails to extract spatial effects under fine-grained content. Therefore, the generated prediction models are unsatisfactorily reliable [10; 12]. Therefore, accurate station-level demand prediction for docked bike-sharing and the TAZ/grid-level demand prediction for dockless bike-sharing will be better for operators of bike-sharing systems to rebalance the imbalanced supply of shared bikes and recommend stations to passengers [13]. Classical spatial regression includes spatial effects in the demand prediction process; however, it can be limited by assumptions, accuracy, and computer power. The Spatial-Temporal Graph Attentional Long Short-Term Memory (STGA-LSTM) neural network model proposed in this study incorporates spatial and temporal usage patterns to predict the short-run demand for docked shared bikes. Moreover, the attention mechanism is useful to improve the accuracy and interpretability of a deep-learning neural network [14]. As far as we know, it is one of the first efforts to integrate the GC-LSTM model with the attention mechanism for the prediction of short-term bike-sharing demand, and this method can be generalized for the demand prediction of both docked and dockless bike-sharing systems. To facilitate the training and testing of the proposed model, this paper uses smart card data of docked bike-sharing provided by the Nanjing public bicycle company, points of interest (POIs) data obtained by calling the Amap API [15], road network data downloaded from OpenStreetMap [16], and historical hourly weather data extracted from Weather Underground [17]. By comparing the proposed model with the leading prediction methods, it is shown that the proposed method is effective in predicting the short-run demand for shared bikes.