Pick-up and delivery (PD) services such as online food ordering are playing an increasingly important role in serving people's daily demands. Accurate PD route prediction (PDRP) is important for service providers to efficiently schedule riders to improve service quality. It is crucial to model the decision-making process behind the route choice of riders for PDRP. Recent years have witnessed the success of utilizing imitation learning (IL) to model user decision-making process. Therefore, we propose to deploy an IL framework to solve the PDRP problem. However, there still exist three main challenges: (1) the rider's route decision is affected by multi-source and heterogeneous features and the complex relationships among these features make it hard to explore how they influence the rider's route decision-making; (2) the large route decision-making space make it easy to explore and predict unreasonable routes; (3) the rider's personalized preference is important in modeling the route decision-making process but cannot be fully explored. To tackle the above challenges, we propose ILRoute, a Graph-based imitation learning method for PDRP. ILRoute utilizes a multi-graph neural network (multi-GNN) to extract the multi-source and heterogeneous features and model their complex relationships. To address the large route decision-making space, ILRoute introduces a mobility regularity-aware constraint as prior route choice knowledge to reduce the exploration route decision-making space. To model the personalized preferences of the rider, ILRoute utilizes a personalized constraint mechanism to enhance the personalization of the rider's route decision-making process. Offline experiments conducted on three real-world datasets and online comparisons demonstrate the superiority of our proposed model.

The use of scatterplots is an important method for multivariate data visualization. The point distribution on the scatterplot, along with variable values represented by each point, can help analyze underlying patterns in data. However, determining the multivariate data variation on a scatterplot generated using projection methods, such as multidimensional scaling, is difficult. Furthermore, the point distribution becomes unclear when the data scale is large and clutter problems occur. These conditions can significantly decrease the usability of scatterplots on multivariate data analysis. In this study, we present a cluster-based visual abstraction method to enhance the visualization of multivariate scatterplots. Our method leverages an adapted multilabel clustering method to provide abstractions of high quality for scatterplots. An image-based method is used to deal with large scale data problem. Furthermore, a suite of glyphs is designed to visualize the data at different levels of detail and support data exploration. The view coordination between the glyph-based visualization and the table lens can effectively enhance the multivariate data analysis. Through numerical evaluations for data abstraction quality, case studies and a user study, we demonstrate the effectiveness and usability of the proposed techniques for multivariate data analysis on scatterplots.

Video annotation is an effective way to facilitate content-based analysis for videos. Automatic machine learning methods are commonly used to accomplish this task. Among these, active learning is one of the most effective methods, especially when the training data cost a great deal to obtain. One of the most challenging problems in active learning is the sample selection. Various sampling strategies can be used, such as uncertainty, density, and diversity, but it is difficult to strike a balance among them. In this paper, we provide a visualization-based batch mode sampling method to handle such a problem. An iso-contour-based scatterplot is used to provide intuitive clues for the representativeness and informativeness of samples and assist users in sample selection. A semisupervised metric learning method is incorporated to help generate an effective scatterplot reflecting the high-level semantic similarity for visual sample selection. Moreover, both quantitative and qualitative evaluations are provided to show that the visualization-based method can effectively enhance sample selection in active learning.

Similar historical forecasts (also called analogs) are important references for the forecasters in their routine work. The forecasts are multi-variate data, and it is difficult to determine whether two forecasts are similar. In this study, we propose a radar glyph based visualization system to analyze the similarity of analogs using multiple variables. The radar glyph, nested in a radar chart, can effectively provide the forecasters with the similarity based on different variables, while keeping an overall similarity context. An interactive system is developed to support the analog data analysis and help the forecasters detect potential biases in the forecast. The usability of the system is then demonstrated using two case studies.