## 1 Introduction  
Within the urban transportation framework, the Bike-Sharing System (BSS) functions as the essential \"capillary\" transport network, primarily addressing the challenge of the \"last mile\" commute from subway or bus stations to the final destination. The BSS has witnessed rapid growth and expansion since its inception, owing to its inherent convenience, environmental friendliness, and capacity to alleviate pressure on public transportation systems to a certain extent. However, due to indiscriminate deployment and managerial oversights, issues such as excessive proliferation, haphazard parking, uneven distribution, as well as imbalanced supply have emerged within the BSS. Addressing these issues requires operators to comprehend users' cycling demands, enabling proactive planning for scheduling and deployment. Bike-sharing demand prediction is a fundamental problem in managing transportation systems. However, achieving accurate and long-term demand forecasts is challenging for operators due to the multifaceted factors influencing bike-sharing demand. We categorize numerous factors influencing bike-sharing demand into four main classes across spatial and temporal dimensions:  
1. Spatial Correlation Factors: Spatial correlation refers to the association between the demand for bike-sharing and spatial locations at the same time. On one hand, the demand for bike-sharing varies depending on the different Points of Interest (POIs) near bike-sharing stations. For example, during the city's evening peak hours, the demand for bike-sharing near office buildings is higher compared to residential areas. On the other hand, there exists mutual influence in demand between bike-sharing stations, and this influence is correlated with the distance between stations. The increase in demand at station A, as depicted in Figure 1, leads to a corresponding rise in demand at neighboring station B, whereas the demand at more distant station C remains relatively unchanged. 2. Spatial Heterogeneity Factors: Spatial heterogeneity refers to sudden, irregular spatial factors influencing bike-sharing demand. For instance, the addition or closure of stations (referred to collectively as dynamic changes in stations) impacts the demand at surrounding stations. As illustrated in Figure 2, the blue dots represent regular stations, yellow dots represent newly added stations, and purple dots represent stations that have disappeared. 3. Temporal Correlation Factors: Temporal correlation refers to fluctuations in demand at the same station over time. For instance, people's travel needs differ on weekdays and weekends, leading to varying demand at the same station at different times. Additionally, since the outbreak of the COVID-19 pandemic, people's travel patterns have been significantly affected, resulting in fluctuations in bike-sharing demand following the pattern of each pandemic wave, as shown in Figure 3.
4. Temporal Heterogeneity Factors: Temporal heterogeneity factors refer to the influence of various unforeseen elements at different points in time on the demand for the same station, including holidays and adverse weather conditions. As illustrated in Figure 4, on days affected by rainy or snowy weather, the demand decreases. Existing research has predominantly utilized deep learning models to learn the spatiotemporal correlation features of bike-sharing demand. [PERSON] et al. divided the bike-sharing demand time series into hourly, daily, and weekly levels, merging them into the model to capture different temporal dependencies [1]. [PERSON] et al. proposed a GC-LSTM model with a spatiotemporal attention matrix, utilizing attention mechanisms to learn spatial correlations [2]. [PERSON] and [PERSON] introduced a data-driven graph convolutional model (GCNN-DDGF) to learn spatial correlations between bike-sharing stations [3]. [PERSON] et al. proposed the FGST model, leveraging graph generation and spatiotemporal embedding to model spatiotemporal dependencies [4].