Human Mobility Question Answering (Vision Paper)
This work proposes a new paradigm for mobility prediction research, which could benefit fields like urban planning and personalized recommendations, though it is incremental as it extends existing QA frameworks to a new data type.
The paper introduces a novel task called Human Mobility Question Answering (MobQA) to address the unexplored area of question answering systems using human mobility data, aiming to enable intelligent systems to learn from such data and answer related questions, with potential applications in smart city planning and pandemic management.
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.