# Interpretability Study of Gradient Information in Individual Travel Prediction
## 1 Introduction  
As the global urbanization process accelerates, the spatiotemporal complexity of urban transportation systems grows exponentially, driving intelligent transportation systems (ITS) to become a strategic technology for optimizing urban operational efficiency [1]. Individual travel prediction, as the core technical support for ITS, provides decision-making basis for dynamic traffic scheduling and resource optimization by mining the spatiotemporal association features in passenger behavior patterns [2, 3, 4]. However, traditional machine learning methods face limitations in representing high-dimensional, nonlinear traffic data. While deep learning models can capture complex patterns, their inherent opacity makes the prediction mechanisms difficult to interpret, which severely restricts their application in decision-sensitive scenarios [5]. In recent years, explainable artificial intelligence (XAI) methods [6] have made significant progress across various fields, but they face unique challenges in the context of travel behavior prediction. Current research largely relies on mainstream interpreters such asSHAP [7] and LIME [8], which perform excellently in static feature attribution analysis, but have significant limitations when it comes to explaining temporal features. Existing methods can only explain the impact of static features and fail to reveal the dynamic association mechanisms of historical travel sequences, as shown in Figure 1. For example, in epidemic transmission chain tracing, decision-makers not only need to predict the future travel locations of individuals but also need to identify the contribution weights of key historical nodes (e.g., specific dates and locations) to the current prediction. This is crucial for formulating effective control strategies [9]. Existing XAI methods, due to their lack of capability to deconstruct temporal associations, lead to explanatory results that are insufficient to meet decision-making needs [10]. To address the interpretability bottleneck in individual travel prediction, this study introduces for the first time a gradient-based saliency method into the travel prediction field, filling the gap in existing XAI methods in analyzing the dynamic features of historical travel sequences. This method constructs a spatiotemporal feature contribution map by calculating the gradient response of input features to the prediction result. Its technical advantages are reflected in three dimensions: first, it breaks through the limitation of traditional interpreters that only analyze static features, allowing the tracking of contribution weights of key time points in the historical travel sequence; second, by generating spatiotemporal heatmaps with the same dimensions as the original data, it converts abstract features like departure time and station numbers into visual decision-making references; third, compared to the exponential computational demands of mainstream interpreters [11], this method has linear complexity with respect to model parameters, offering significant computational advantages in long-sequence analysis. This choice of technical path does not question the theoretical value of existing XAI methods but instead provides new perspectives and methods for expanding interpretability in the travel prediction domain through domain transfer. The study conducts experiments based on real travel data from a city's subway system, and the results show that gradient information can effectively quantify feature contribution, with the most significant gradient features accounting for over 30% of the impact on prediction performance. This finding not only provides a new method for the interpretability of individual travel prediction models but also lays a theoretical foundation for trustworthy decision-making in intelligent transportation systems.