A deep learning framework to generate realistic population and mobility data

• URL: http://arxiv.org/abs/2211.07369v1
• Date: Mon, 14 Nov 2022 14:05:09 GMT
• Title: A deep learning framework to generate realistic population and mobility data
• Authors: Eren Arkangil, Mehmet Yildirimoglu, Jiwon Kim, Carlo Prato
• Abstract summary: Census and Household Travel Survey datasets are regularly collected from households and individuals. These datasets often represent a limited sample of the population due to privacy concerns or are given aggregated. 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)
• Score: 5.180648702293017
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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