# Interpretability Study of Gradient Information in Individual Travel Prediction
## 5 Interpreting Predictions Using Contribution Maps  
To further illustrate the interpretability of gradient information in individual travel prediction tasks, in this subsection, we will briefly explain how to reconceptualize the contribution map and use it for travel prediction tasks. By visualizing the gradient information, we demonstrate how the gradient information explains the model's decision-making behavior from input to output. Figures 1-4 display the model's decision-making process for different travel patterns within the correctly observed subset. Figure 8 shows the gradient information under the home-to-work travel pattern. The heatmap reveals a highly consistent travel pattern, which is clearly reflected in the gradient information. It can be seen from the figure that specific origin-destination (OD) pairs (such as 55-56 and 5655) reappear at multiple time points, indicating that passengers' travel behavior is highly regular. Under this pattern, the model can easily capture these repetitive travel patterns, thus achieving high accuracy in predicting the next trip. The gradient information in this case is mainly concentrated on these repeatedly appearing OD pairs, showing high weights. This gradient distribution indicates that the model has high confidence in these regular patterns and therefore gives these patterns higher priority in prediction. Moreover, due to the regularity of the travel pattern, the model also has strong generalization ability when dealing with these data and can make accurate predictions even when facing a small amount of travel records. The gradient information under this pattern not only helps us understand the basis of the model's predictions but also provides  
opportunities for optimizing the model, such as by reducing the dependence on these highly repetitive patterns and increasing the model's adaptability to new emerging patterns. Figure 9 shows the contribution map when an anomaly occurs in a regular travel pattern, revealing how the model handles this sudden change. It can be seen from the figure that although most of the travel patterns still follow the original regularity (the repeated appearance of 18-10 and 10-18), the appearance of station 80 breaks this regularity and shows a high gradient weight. This may be because the last two travel behaviors (18-80 and 10-18) provide two high-probability options for the next trip (18-10 and 18-80). The model regards station 80 as an important feature because it represents a potential change in passengers' travel behavior. It also indicates that passengers' next travel choices are not only influenced by their recent travel behavior but also by their past travel, which is in line with our common sense. Figure 10 shows the contribution map when two anomaly stations appear in a regular travel pattern, displaying how the model deals with this more complex change. It can be seen from the figure that in addition to the original regular OD pairs (such as 81-14 and 14-81), the appearance of stations 3 and 35 increases the complexity of the travel pattern. This may be a home-work-supermarket-restaurant-home travel pattern, or it may be special activities or emergency matters on weekends. It can be seen from the contribution map that these two anomaly stations occupy high weights in the gradient information, which is also the sample where the maximum gradient position does not appear on the recent travel sequence in the example. This weight distribution may be because the model has identified a strong association between these stations and passengers' travel purposes. This association makes the model very confident in its prediction, without the need to rely on other features, even though these stations do not often appear in passengers' daily travel. This reflects the model's attempt to balance the conflict between known regular travel patterns and newly emerging anomaly patterns, and to stick to its prediction under high confidence.