## 2 Literature Review  
Short-run bike-sharing prediction assists system operators in daily operations and improves the system reliability and accessibility [18]. During the past decades, how to predict the short-term demand of bike-sharing has been widely studied. The methods for bike-sharing prediction can be divided into parametric methods and non-parametric methods. The parametric methods usually regard bike-sharing demand prediction as a time-series issue from multi-source and heterogeneous data [19]. Auto-Regressive Moving Average (ARMA) model-based models were adopted to predict the short-term bike-sharing demand. [PERSON] et al. [20] applied an ARMA model to predict available shared bikes for stations with a some minutes/hours horizon window ahead. [PERSON] et al. [21] proposed a modified Auto-Regressive Integrated Moving Average (ARIMA) model to forecast the number of available shared bikes for each station considering temporal factors of and interactions among stations. [PERSON] et al. [22] applied a Seasonal Auto-Regressive Integrated Moving Average (SARIMA) model to analyze bike-sharing demand using hourly bike usage and the corresponding weather data. They found that weather-related factors, especially temperature and rain, significantly impacted bike-sharing usage. Previous ARMA-based models can use temporal information to predict the bike-sharing demand. However, these models cannot well extract the heterogeneous correlations among stations [3]. The non-parametric methods include machine learning approaches and deep learning approaches. Using machine learning methods, several researchers worked to establish nonlinear prediction model based on massive bike-sharing historical trip data. A few studies adopted Random Forest [23], Bayesian network [24], Gradient Boosted Tree [25], and Artificial Neural Networks (ANN) [26] to predict the bike-sharing demand at each station at any given time period in the future, without including the spatial or temporal correlations between stations. In addition, recent studies used deep learning methods to predict the short-term station-level bike-sharing demand. [PERSON] and [PERSON] [27] applied Long Short-Term Memory (LSTM) neural networks and Gated Recurrent Unit (GRU) to predict the short-term availability of shared bikes at docking stations using historical data within one month. [PERSON] et al. [28] proposed the Recurrent Neural Network (RNN) to predict both rental and return demand for every station in the system using time, weather, and station data. [PERSON] et al. [29] adopted the LSTM model to predict the short-term bike-sharing usage by considering the correlation between bike-sharing users and public transport passengers. To extract spatial-temporal dependencies, some studies combined both network structures and put forward a Convolutional LSTM network (Conv-LSTM). For example, [PERSON] et al. [12] combined irregular CNN and LSTM units and used historical passenger flows and external factors such as weather, traffic control, and social activities to explore the characteristic of spatial-temporal traffic flows and predict hourly road traffic flows. [PERSON] et al. [30] proposed a Conv-LSTM model and predicted the dockless bike-sharing systems distribution within a short-run period by considering spatial-temporal variables. [PERSON] et al. [31] constructed a Multi-Block Hybrid model by involving CNN and GRU to implement short-run dockless bike-sharing prediction. Furthermore, Graph Neural Network (GCN) could detect the complex heterogeneous spatio-temporal effects of bike-sharing ridership, by treating the bike station as the vertices. [PERSON] et al. [32] developed a Graph Neural Network (GCN) prediction model and estimated the bike-sharing demand in different hours of a day for each station by considering the temporal patterns, spatial characteristics, and global variables (weather and weekday /weekend). [PERSON] [33] applied spatio-temporal GCN (ST-GCN) to predict shared bike flows across all stations in the next hour. [PERSON] et al.