Question answering (QA) systems have attracted much attention from the
artificial intelligence community as they can learn to answer questions based
on the given knowledge source (e.g., images in visual question answering).
However, the research into question answering systems with human mobility data
remains unexplored. Mining human mobility data is crucial for various
applications such as smart city planning, pandemic management, and personalised
recommendation system. In this paper, we aim to tackle this gap and introduce a
novel task, that is, human mobility question answering (MobQA). The aim of the
task is to let the intelligent system learn from mobility data and answer
related questions. This task presents a new paradigm change in mobility
prediction research and further facilitates the research of human mobility
recommendation systems. To better support this novel research topic, this
vision paper also proposes an initial design of the dataset and a potential
deep learning model framework for the introduced MobQA task. We hope that this
paper will provide novel insights and open new directions in human mobility
research and question answering research.

本文引入了一个新颖的任务，即人员移动问答 (MobQA)，它旨在让智能系统从人员移动数据中学习并回答相关问题，为人员移动预测研究和人员移动推荐系统的研究带来了新的范式变革。该论文还提出了数据集的初始设计和引入的 MobQA 任务的潜在深度学习模型框架，希望能在人员移动研究和问题回答研究领域提供新的见解和打开新的方向。