## 1 Introduction
Some studies have also incorporated temporal heterogeneity factors such as weather and holidays for more accurate predictions [2; 5]. However, certain factors remain underexplored, leading to suboptimal model performance in real-world applications. Firstly, most existing multi-step forecasting models are limited to capturing local spatiotemporal correlations, focusing on nearby temporal and spatial features while ignoring globally similar spatiotemporal patterns. Secondly, the significant impact of the COVID-19 pandemic on travel patterns has not been well-modeled by existing models. Thirdly, for docked bike-sharing systems, the dynamic changes in stations, i.e., station additions and closures, cannot be predicted based on historical demand alone. Yet, these changes significantly affect long-term demand forecasting, and there is currently a lack of research addressing this influential factor. To address the aforementioned challenges, this paper proposes a Local-Global Dynamic Multi-Graph Convolutional Neural Network (LGDMGCN) model based on the multi-source data-driven approach. This model, operating in the context of the impact of COVID-19 and dynamic changes in stations, not only captures local-global multiscale spatiotemporal correlations but also incorporates diverse information to capture irregular spatiotemporal heterogeneities and dynamics, leading to more accurate multi-step predictions. Specifically, the LGDMGCN model employs a dynamic multi-graph generator to leverage various spatial semantic features and the temporal dynamic semantic information  
Figure 4: The impact of rainy or snowy weather on the demand for shared bicycles. Figure 3: The relationship between daily demand for shared bicycles and daily confirmed COVID-19 cases. of stations to create dynamic multi-graphs. Subsequently, based on spatial semantics, these dynamic multi-graphs are separately fed into local or global spatiotemporal multi-graph convolution modules to learn spatiotemporal features on different scales of bike-sharing demand. Further, a temporal attention mechanism is employed to capture the global dependencies of multi-step time. Finally, the output sequence is concatenated with multi-source temporal semantic features such as COVID-19 pandemic data, holidays, and weather, and input into a Gated Recurrent Unit (GRU) for prediction. The contributions of this paper are summarized as follows:  
1. In the proposed LGDMGCN model, a local-global spatiotemporal multi-graph convolution module is designed to learn multiscale spatiotemporal correlation features. It fully utilizes the spatial semantic information of stations to construct multiple graphs and employs multi-graph convolution to capture spatial features. Time gate units are used to capture temporal features, and the local-global structure incorporates features both around individual stations and across all stations globally. Additionally, a time attention mechanism is applied to focus on the long-term dependencies in the time series, capturing global relationships. 2. In the context of the COVID-19 pandemic, the model integrates multi-source data, including confirmed COVID-19 cases, holidays, and weather conditions, to learn the heterogeneity features of time. Considering the actual scenario of dynamic station changes, a dynamic multi-graph generator is employed to create dynamic multi-graphs. Subsequently, a spatiotemporal dynamic multi-graph convolution module is used to learn spatiotemporal correlation features with spatial heterogeneity. 3. The proposed prediction algorithm demonstrated its applicability to real-world scenarios by effectively integrating external factors such as holidays, weather, and the COVID-19 pandemic. Experimental results on real datasets showed that the LGDMGCN model exhibited superior performance in multi-step prediction, considering temporal and spatial heterogeneity factors.