# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 5 Experiments
### Baselines  
In our experiments, we compared our model with seven other commonly used models. These models encompass a variety of techniques and implementation methods, including statistical methods (i.e., TOP and Personal TOP), sequence model methods (i.e., LSTM and GRU), graph convolutional methods (i.e., TGAT), memory network models (i.e., TGN), and MLP-based methods (i.e., GraphMixer).  
**TOP.** The TOP method [33] is a statistical method based on the travel patterns of all users. First, the training data are fed into the model for statistical analysis. Then, for each  
Figure 4: Distribution of Interaction Frequencies Between Passengers and Stations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Passenger** & **Station** & **Interaction** & **Interaction Per Passenger** \\ \hline BJSubway-40K & 459 & 392 & 44,852 & 97 \\ BJSubway-1M & 7491 & 392 & 958,076 & 127 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistics of all datasets.  
station, two sequences are generated: one for all alighting stations when the station is the boarding station, and one for all boarding stations when the station is the alighting station. After completing the statistics, the corresponding station is determined based on the passenger's last travel status, and the station with the highest occurrence frequency in the corresponding sequence is selected as the predicted station.  
**Personal TOP.** The personal TOP [33] method is similar to the TOP method but generates unique sequences for each specific passenger for boarding and alighting stations. During testing, for passengers with existing sequence records, the predicted station is directly generated based on their unique sequences. For passengers without sequence records, the predicted station is determined based on the overall sequence.  
**LSTM.** LSTM [34] is a commonly used method for processing sequential data. Compared to traditional RNN models, it introduces input gates, forget gates, output gates, and a cell state, which help prevent the problems of vanishing or exploding gradients. This allows LSTM to better handle long-term dependencies in sequences.  
**GRU.** GRU [35] can be seen as a variant of LSTM. It uses a reset gate to determine how to combine new input information with previous memory, and an update gate to define the amount of previous memory to retain for the current time step. GRU is capable of preserving information from long sequences, without clearing it over time or removing it if it is unrelated to the prediction.  
**TGAT.** TGAT [23] employs a self-attention mechanism to generate corresponding representations by aggregating the temporal topological features of neighbors for each passenger node and station node. Additionally, it uses a time encoder to capture temporal characteristics.  
**TGN.** TGN [18] maintains an updated memory node for each passenger node and station node and updates its memory when the node is observed. This is achieved through interaction with a message function, a message aggregator, and a memory updater. This model generates temporal embeddings for both passenger nodes and station nodes.  
**GraphMixer** GraphMixer [32] demonstrates that a fixed-time encoding function outperforms the trainable version. It incorporates this fixed function into a link encoder based on MLP-Mixer to learn from temporal links. A node encoder using neighbor mean-pooling is implemented to summarize node features.