## 5 Experiment
### Experimental Results and Analysis
#### 5.4.1 Comparative Experiment  
In the comparative experiment, the following models are selected as baseline methods:  
* LSTM (Long Short-Term Memory): An improved RNN model commonly used for time series prediction [45]. * STGCN (Spatiotemporal Graph Convolutional Network): A spatiotemporal prediction model with a \"gated TCN + GCN+ gated TCN\" structure of spatiotemporal graph convolution modules. This model innovatively employs a combination of Graph Convolution and Gated Causal Convolution, eliminating the reliance on RNN for prediction. It is particularly effective in modeling relationships between stations based on the road network structure and has shown good prediction results in road speed prediction problems [40]. * DGCRN (Dynamic Graph Convolutional Recurrent Network): This model is based on an encoder-decoder structure, considering the impact of dynamics on traffic prediction, and defines the computation of dynamic graph convolution. It uses a hyper-network to dynamically generate an adjacency matrix at each step of the RNN, allowing the model to adaptively capture changing spatial relationships. The generated dynamic matrix is merged with the original road network matrix to provide a richer spatial representation [31]. * AttConvLSTM (Attention Convolutional LSTM): An encoder-decoder framework, utilizing convolutional and ConvLSTM units, is employed to identify complex features, capturing spatiotemporal characteristics and the impact of pick-up and drop-off interactions on citywide passenger demand. The model embeds an attention mechanism to depict the influence of underlying citywide travel patterns [46]. * STSeq2 Seq (Spatiotemporal Sequence-to-Sequence): A model that captures dynamic relationships between time series data points by modeling non-local spatial correlationsbetween traffic sections. It utilizes an encoder-decoder architecture where the encoder processes the input sequence and the decoder generates the output sequence. It also introduces an attention mechanism in the decoder to model dependencies between different time steps [38]. Using historical demand data from the past 12 time intervals to predict the future multi-step demand for shared bicycles at the station level, the LGDMGCN model was compared with other baseline models using the Chicago shared bicycle dataset. Experimental results are presented in Table 2:  
Comprehensive comparative experimental results indicate that various time series forecasting models exhibit certain differences in performance across different prediction time steps, as evaluated by the MAE and RMSE metrics. Specifically, it can be observed that:  
1. The LSTM model, which solely considers temporal relationships between time series, lacks spatial correlations, resulting in overall inferior performance compared to models that incorporate spatiotemporal relationships. 2. The AttConvLSTM model, by combining Convolutional and LSTM layers and incorporating an attention mechanism, demonstrates the effectiveness of modeling spatiotemporal dependencies and attention mechanisms for bike-sharing station prediction compared to LSTM. 3. The STGCN model, which constructs stations into graphs and utilizes graph convolution for spatial modeling, performs well in capturing the spatial structure of bike-sharing stations. By employing TCN modules for temporal modeling, it achieves good performance in 4 h predictions. However, as STGCN is a single-step prediction model, its performance decreases significantly in multi-step predictions. 4. Both the STSeq2 Seq and DGCRN models capture the spatiotemporal characteristics of bike-sharing station demand through sequence-to-sequence model structures, exhibiting good performance in multi-step predictions. With increasing prediction steps, their performance remains relatively stable. Additionally, both models consider spatiotemporal dynamics.