## 2 Related Works  
### Analysis and Prediction Methods for the BSS  
Early scholars employed traditional statistical and regression methods to analyze and predict bike-sharing traffic. The widely used method for time series applications like bike sharing is the Autoregressive Integrated Moving Average model (ARIMA). [PERSON] et al. proposed a seasonal ARIMA model based on ARIMA and weather data to analyze bike-sharing numbers in Vancouver (2017). [PERSON] et al. developed an application system capable of estimating the availability of bicycles at each station, employing an enhanced ARIMA model to forecast the number of shared bicycles at each station. [PERSON] et al. introduced a multi-factor regression model based on ARIMA to predict bike-sharing traffic (2018). However, traditional statistical and regression methods require data to undergo a sliding operation to obtain stationary sequences before conducting linear regression. Such methods rely on manual feature engineering by professionals and are not suitable for handling complex nonlinear relationships. With the advancement of computer processing power and the widespread use of terminal devices generating vast amounts of passive data ([PERSON] et al., 2018), the era of big data has arrived. Scholars began to explore machine learning and deep learning methods for bike-sharing predictions. Common machine-learning models for bike-sharing system predictions include Random Forest, Gradient Boosting Tree, and Bayesian networks. [PERSON] et al. proposed a random forest model to predict the number of shared bicycles that considers meteorological data, capable of simultaneously handling categorical and numerical variables without requiring normalization and possessing interpretability [10]. [PERSON] et al. introduced a Gradient Boosting Regression Tree (GBRT) model for bike-sharing predictions [11]. [PERSON] et al. used Bayesian networks to predict bike availability within 10-20 min [12]. However, machine learning prediction methods fell short of fully modeling the complex spatiotemporal relationships in bike-sharing time series. In recent years, driven by the development of deep learning, many deep learning models have been applied to bike-sharing system predictions. Simultaneously, bike-sharing has experienced rapid development in many cities worldwide, easing transportation for people but also posing challenges to urban traffic management. Managers and scholars have focused on researching more accurate bike-sharing traffic prediction methods to optimize management, leading to numerous relevant studies. Research on bike-sharing traffic prediction based on deep learning models generally falls into three categories: region-based, cluster-based, and station-based. Region-based studies often involve dividing the area to be predicted into a grid and forecasting the traffic for each grid separately. [PERSON] et al. divided the area into a 5 \(\times\) 5 grid, utilized LSTM to capture temporal features, and employed Conv-LSTM to capture spatiotemporal features, demonstrating good predictive performance during morning peak hours [13]. However, direct grid division may sever some complete functional areas, rendering the prediction results unsuitable for management applications. Some scholars adopted clustering methods based on different rules to irregularly divide the city into areas or clusters for prediction [14]. [PERSON] et al. used the DBSCAN algorithm [15] to cluster bike parking points, achieving meaningful area division [16]. [PERSON] and [PERSON] employed the Louvain heuristic clustering algorithm [17] to hierarchically cluster bike-sharing stations, conducting community-level demand predictions based on clustering results [18]. While cluster-based methods tend to overlook some fine-grained spatial features [2], station-based predictions are beneficial for the management of docked bike-sharing systems. These predictions can be directly used for operational decision-making and user experience improvement. [PERSON] et al.