# Interpretability Study of Gradient Information in Individual Travel Prediction
## 4 Experiment
### Feature Impact Analysis  
#### 4.4.1 Analysis of Single-Feature Mask Results  
We designed a single-feature masking experiment to investigate the impact of individual features on the model's prediction performance. In the experiment, we randomly selected 1000 samples from the test set that were correctly predicted by the model. These samples covered different travel patterns and feature combinations, ensuring the representativeness and reliability of the experimental results. By ranking the gradient information, we were able to determine the influence of each sample feature on the model's prediction. Then, we masked each feature position in the input data to simulate the absence of that feature in the prediction process. The results obtained are shown in Table 4.  
Figure 6 illustrates the trend of changes in the model's PA value when single features are masked in descending order. When the feature at the maximum gradient position was masked, the model's JS divergence increased significantly, and the PA value also dropped substantially, with the model performance experiencing a sharp decline (by about one-third). This indicates that this feature point may be the most critical for prediction. Specifically, when this feature was masked, the model's prediction accuracy decreased  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Sequence Length** & **JS** & **PA** \\ \hline
20 & 0.0863 & 0.292 \\
40 & 0.1050 & 0.361 \\
60 & 0.0929 & 0.342 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Changes in metrics after mask maxima for different sequence lengths.  
significantly, and the output probability distribution also changed markedly. This further confirms the central role of the maximum gradient feature in the model's decision-making process. In addition to the maximum gradient feature, only the top few features had a noticeable impact on the prediction. These features may be the most recent travel features in the historical records, which provide important contextual information for the model, helping it better understand and predict future travel behavior. Masking these features also led to a significant downward trend in the model's prediction accuracy, although the impact was slightly less than that of the maximum gradient feature.  
For single features with smaller gradient values, masking these features had a relatively minor impact on the model, with a fluctuating downward trend. This suggests that as the gradient decreases, the feature's impact on the model indeed diminishes, but not in a linear fashion. Specifically, if a single feature from a distant historical record does not contain key information for prediction or travel patterns, masking it would not cause a sharp decline in the model's accuracy. This is because these features contribute little to the model's prediction, and the model can rely on other unmasked features to maintain prediction performance. Masking distant historical features would not disrupt the model's pattern information, as these features have a minor impact on the model's prediction, and the model can rely on other unmasked features to maintain prediction performance.