论文标题
关于人类流动性深度学习的调查
A Survey on Deep Learning for Human Mobility
论文作者
论文摘要
人类流动性的研究至关重要，因为它对我们社会的多个方面的影响，例如疾病传播，城市规划，福祉，污染等。数字移动数据的扩散，例如电话记录，GPS痕迹和社交媒体帖子，再加上人工智能的预测能力，触发了深度学习对人类流动性的应用。现有的调查专注于单个任务，数据源，机械或传统的机器学习方法，而对深度学习解决方案的全面描述则缺失。这项调查提供了有关移动性任务的分类法，讨论与每个任务相关的挑战以及如何克服传统模型的局限性，对上述移动性任务的最相关解决方案的描述以及对未来的相关挑战的描述。我们的调查是针对下一站点预测，人群流动预测，轨迹产生和流量产生的领先深度学习解决方案的指南。同时，它有助于深度学习的科学家和从业者了解人类流动性研究的基本概念和公开挑战。
The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the predictive power of artificial intelligence, triggered the application of deep learning to human mobility. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides a taxonomy of mobility tasks, a discussion on the challenges related to each task and how deep learning may overcome the limitations of traditional models, a description of the most relevant solutions to the mobility tasks described above and the relevant challenges for the future. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, trajectory generation, and flow generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.
