# Interpretability Study of Gradient Information in Individual Travel Prediction
## 6 Conclusions  
This study is the first to transfer interpretability analysis methods from the field of image recognition to the domain of transportation travel, addressing the shortcomings of existing XAI methods in analyzing dynamic features of historical travel sequences. It systematically explores the interpretability value of gradient information in individual travel prediction models. Feature masking experiments based on real-world data show that gradient information effectively reveals the model's decision logic, but the influence of features exhibits complex nonlinear relationships. Specifically, masking the maximum gradient feature position results in a performance decline of about 30%, while masking other features causes performance fluctuations and a decaying trend, indicating that the collaborative effect between features has a composite impact on the prediction results. Notably, masking 20% of high-gradient features leads to a 50% performance degradation, while masking 40% of low-gradient features results in only a 20% performance decline, confirming that the model is highly sensitive to core feature combinations and exhibits strong robustness to non-critical features. We would like to point out several limitations of this study, which provide potential directions for future research. First, while gradient information performs well in explaining travel prediction model decisions, it remains an approximation of local linear relationships and thus struggles to fully capture the nonlinear decisions of deep neural networks. Second, contribution maps provide the extent and correlation of travel feature influences on the output result but cannot construct understandable causal chains or generate natural language explanations. Third, alternative variants based on gradient information have been proposed in the literature [41; 42; 43], and these variants have been applied in computer vision. Researching the performance of these alternatives in the context of travel prediction would be a productive direction for further study. Fourth, the experimental data is limited to a single scenario with medium-length sequences and does not cover the true complexity of multi-city, multi-modal features, which may cast doubt on the generalizability of the conclusions in longer periods or more dynamic scenarios. These limitations are both objective constraints on the current research and point to improvements for deepening interpretability studies. Figure 11: The predicted site label is 22. To conclude, while our analysis indicates that the proposed gradient-based interpretability method provides a valuable tool for understanding the underlying principles of models in the context of travel prediction, it must be acknowledged that the proposed method does not fully solve the black-box problem of deep neural networks because it does not completely explain the inner workings. Future work could focus on developing multi-modal interpretability frameworks integrating causal reasoning, establishing cross-domain interpretability evaluation standards, and building benchmark datasets that cover multiple scenarios and traffic feature scales, thereby advancing the practical implementation of explainable AI technologies in smart city decision-making systems. Conceptualization, Z.S., P.Z. and X.S. ; methodology, Z.S. and P.Z. ; software, Z.S. and Y.L. ; validation, Z.S., P.Z. and X.S. ; formal analysis, Z.S. and Y.L. ; investigation, Z.S. and Y.L. ; resources, Z.S. and X.S. ; data curation, Z.S., P.Z. and X.S. ; writing--original draft preparation, Z.S., P.Z. and X.S. ; writing--review and editing, P.Z., X.S. and Y.L. ; visualization, Z.S. and Y.L. ; supervision, Z.S., P.Z. and X.S. ; project administration, X.S. ; funding acquisition, P.Z. All authors have read and agreed to the published version of the manuscript.