# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 5 Experiments
### Performance Comparison  
Table 5 presents the prediction results of whether a passenger interacted with the next station in the two datasets, compared with seven other methods. The results shown in the table are averages obtained after five independent training rounds. The best results are highlighted in bold, and the second-best results are marked with an underline. The DyGPP model achieved the highest accuracy and the best AUC score among all models. This indicates that dynamic graph models can efficiently handle passenger behavior prediction tasks using dynamic graph structures, and these structures can be \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Time gap for batch & 1000 s \\ Number of neighbors & 20 \\ Neighbor sampling strategy & Most recent neighbor \\ Negative sample strategy & Random \\ Time scaling factor & 0.000001 \\ Number of heads & 2 \\ Number of encoder model layers & 1 \\ Channel embedding dimension & 50 \\ Learning rate & 0.0001 \\ Max input sequence & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Parameters of the experiment. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Datasets** & \multicolumn{2}{c}{**BJSubway-40K**} & \multicolumn{2}{c}{**BJSubway-1M**} \\ \hline
**Metrics** & **AP** & **AUC** & **AP** & **AUC** \\ \hline TOP & 0.7880 \(\pm\) 0.0000 & 0.7831 \(\pm\) 0.0000 & 0.8501 \(\pm\) 0.0000 & 0.8204 \(\pm\) 0.0000 \\ Personal TOP & 0.7545 \(\pm\) 0.0000 & 0.7701 \(\pm\) 0.0000 & 0.7901 \(\pm\) 0.0000 & 0.8067 \(\pm\) 0.0000 \\ LSTM & 0.9026 \(\pm\) 0.0039 & 0.8752 \(\pm\) 0.0041 & 0.9395 \(\pm\) 0.0015 & 0.9336 \(\pm\) 0.0015 \\ GRU & 0.9119 \(\pm\) 0.0059 & 0.8860 \(\pm\) 0.0062 & 0.9418 \(\pm\) 0.0016 & 0.9357 \(\pm\) 0.0018 \\ TGN & 0.9390 \(\pm\) 0.0032 & 0.9215 \(\pm\) 0.0040 & 0.9430 \(\pm\) 0.0017 & 0.9365 \(\pm\) 0.0023 \\ GraphMixer & 0.8565 \(\pm\) 0.0078 & 0.8281 \(\pm\) 0.0079 & 0.7591 \(\pm\) 0.0015 & 0.7716 \(\pm\) 0.0018 \\ TGAT & 0.7094 \(\pm\) 0.0095 & 0.6410 \(\pm\) 0.0116 & 0.6563 \(\pm\) 0.0107 & 0.6420 \(\pm\) 0.0180 \\ DyGPP & **0.9699 \(\pm\) 0.0008** & **0.9591 \(\pm\) 0.0015** & **0.9697 \(\pm\) 0.0002** & **0.9647 \(\pm\) 0.0002** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: AP and AUC for passenger behavior prediction. effectively extended to other sequential models. Other key observations from the table are as follows:  
(i) Statistical methods (i.e., TOP and personal TOP), performed poorly in comparison. The statistical methods only considered the historical boarding and alighting counts of passengers, without analyzing specific times, and they did not account for the temporal sequence and potential changes in passenger travel. Compared to the personal TOP method, the TOP method performed better because most individuals had fewer than 100 interactions, making it difficult to effectively represent individual travel patterns given the significantly larger number of stations compared to the number of interactions. (ii) The sequential methods (i.e., LSTM and GRU) performed well in the prediction task. These methods predicted future passenger behavior by combining boarding and alighting patterns, fully learning the travel habits of passengers. However, the sequential methods did not consider the impact of station conditions on passenger travel choices and had insufficient predictive ability for travel behavior fluctuations caused by small-scale special circumstances. (iii) The dynamic graph methods considered the relationships between nodes, thereby accounting for the mutual influence between stations and passengers, which enhanced the prediction accuracy. The TGAT method used interaction occurrence times as an important basis for learning, but due to the limited amount of available data and the coexistence of long-term periodic patterns and short-term abrupt events in passenger travel behavior, TGAT's learning capability was insufficient.