# Interpretability Study of Gradient Information in Individual Travel Prediction
## 3 Methods
### Analogy Between an Image Recognition Task and an Individual Travel Prediction Task  
Before delving into the details of saliency methods, we first defined some terms and established the similarity between image recognition and travel prediction models. In travel prediction models, many concepts are similar to those in the field of machine learning, but the terminology differs. For ease of understanding, we provided a simple comparison as shown in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Image Recognition Tasks** & **Travel Prediction Task** \\ \hline Pixel & Feature \\ Intensity Size & Value Size \\ Label Category & Option/Forecast Target \\ Label Set & Option Set \\ \hline \hline \end{tabular}
\end{table}
Table 2: Image processing terminology and travel prediction modeling equivalents.  
Figure 3: Mapping relationships between interpretability methods and models.  
Image recognition tasks are a computer vision technique that involves taking image data as input and classifying it into predefined categories. In this process, the input image can be regarded as a matrix, where each pixel is composed of values from three color channels (in the case of RGB), with each channel's value ranging from 0 to 255. These values collectively determine the color and brightness of the pixel. The goal of image recognition is to assign the image to a specific category based on these pixel values, such as identifying whether the content of the image is a cat, a dog, or a vehicle. In image recognition tasks, each observation instance includes a pixel matrix of an image and a Q-dimensional vector representing the image's category; Q is the size of the set of categories. To simplify the discussion, we assume the image is grayscale, with each pixel having a single value indicating a range of brightness (for example, from 0 (black) to 255 (white)). The goal of image recognition is to classify the input data into a predefined category.  
In this framework, an analogy can be drawn between image recognition tasks and travel prediction tasks. In image recognition tasks, pixels are the basic units of input data, and their intensity values determine the visual features of the image. In contrast, in individual travel prediction tasks, the basic units of input data are travel features, such as travel time, location, or mode of transportation. The attribute values of these features (e.g., the specific numerical value of travel time) are analogous to the intensity values of image pixels.  
Since each trip is often related to the previous one, the relationships and patterns in travel data are continuous along the historical sequence dimension. With the help of word embedding techniques, originally discrete features (such as location identifiers) can be represented in the embedding space in the form of continuous numerical values. This means that there is a certain continuity between the various features of travel data (whether in rows or columns), similar to the spatial continuity of image pixel values.  
In both types of models, the goal is to identify and utilize patterns in the input data. In image recognition, these patterns are visual, while in travel prediction, they are patterns of travel behavior. Word embeddings [39] help the model capture these patterns in a low-dimensional space, similar to the capture of pixel patterns in image recognition. In image recognition, similar image content will exhibit similar distributions in pixel space. In travel prediction, similar travel features (such as locations that often appear together) will also exhibit similar distributions in the embedding space.