论文标题
一个深度学习框架，以产生现实的人群和流动性数据
A deep learning framework to generate realistic population and mobility data
论文作者
论文摘要
人口普查和家庭旅行调查数据集定期从家庭和个人中收集，并提供有关其日常旅行行为具有人口和经济特征的信息。这些数据集具有从旅行需求估计到基于代理的建模的重要应用程序。但是，由于隐私问题，它们通常代表有限的人口样本，或者被汇总。综合数据增强是应对这些挑战的有希望的途径。在本文中，我们提出了一个框架来产生一个综合人群，其中包括社会经济特征（例如年龄，性别，行业）和跳闸链（即活动位置）。我们的模型经过测试，并将其与其他最近提出的模型进行了多个评估指标的比较。
Census and Household Travel Survey datasets are regularly collected from households and individuals and provide information on their daily travel behavior with demographic and economic characteristics. These datasets have important applications ranging from travel demand estimation to agent-based modeling. However, they often represent a limited sample of the population due to privacy concerns or are given aggregated. Synthetic data augmentation is a promising avenue in addressing these challenges. In this paper, we propose a framework to generate a synthetic population that includes both socioeconomic features (e.g., age, sex, industry) and trip chains (i.e., activity locations). Our model is tested and compared with other recently proposed models on multiple assessment metrics.
