## 6 Conclusions  
In this study, we aimed to address the fundamental issue of predicting shared bicycle demand in transportation system management. By summarizing various factors influencing shared bicycle demand in the spatial and temporal dimensions, we proposed an innovative LGDMGCN (Local-Global Dynamic Multigraph Convolutional Neural Network) model to more accurately predict shared bicycle demand. Experimental results demonstrate that the model performs exceptionally well in real-world scenarios, especially when considering complex factors such as the COVID-19 pandemic, dynamic changes in station availability, and spatiotemporal heterogeneity, achieving significant performance improvements. The innovations of this paper are primarily reflected in several aspects. Firstly, we designed a local-global spatiotemporal multigraph convolutional module, leveraging the spatial semantic information of stations to construct multiple graphs. Through multigraph convolution, it captures spatial features, while utilizing time-gated units captures temporal features. This structure allows the model to simultaneously focus on local and global spatiotemporal features, enabling a more comprehensive learning of multiscale correlation features in shared bicycle demand. Secondly, against the backdrop of the COVID-19 pandemic and dynamic changes in station availability, we introduced a dynamic multigraph generator and spatiotemporal dynamic multigraph convolutional module to adapt to changes in demand during pandemic periods and dynamic station availability. This effectively enhances the model's adaptability and predictive accuracy. Lastly, through the introduction of a temporal attention mechanism, we successfully captured global dependencies in time series, thereby enhancing the accuracy of multi-step predictions. Figure 11: Comparison of MAE for models across different periods. Figure 10: Relationship between daily confirmed COVID-19 cases and predicted demand for bike-sharing. During the experimental phase, we thoroughly validated the LGDMGCN model using real datasets and compared it with existing models. The results indicate that, compared to other models, the LGDMGCN model performs exceptionally well in predicting shared bicycle demand, particularly in complex real-world scenarios. This demonstrates the high practicality and performance of the proposed model in solving the shared bicycle demand prediction problem. However, predicting shared bicycle demand remains a complex and multidimensional problem. Future research could further deepen the consideration of other factors, such as traffic flow and social media information. And the multi-modal prediction with interactions between different modes of transportation explores the interrelationships among shared bicycles, buses, subways, taxis, and shared scooters. The model developed in this study provides shared bicycle operators with a more accurate and long-term demand prediction tool, offering valuable insights for efficient management and resource allocation in urban transportation systems. Funding acquisition, J.C.; methodology, J.C.; project administration, J.C.; software, R.H.; supervision, J.C.; validation, R.H.; visualization, R.H.; writing--original draft, J.C.; writing--review and editing, R.H. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 61104166. The data presented in this study are available on request from the corresponding author. The datasets used during the current study are available at [[https://divvy-tripdata.s3.amazonaws.com/index.html](https://divvy-tripdata.s3.amazonaws.com/index.html)]([https://divvy-tripdata.s3.amazonaws.com/index.html](https://divvy-tripdata.s3.amazonaws.com/index.html)), accessed on 16 February 2023. The authors would like to thank the reviewers for useful suggestions. The authors declare there are no conflicts of interest regarding the publication of this paper.