A deep learning framework to generate realistic population and mobility data

TL;DR

This paper introduces a deep learning framework for generating realistic synthetic population and mobility data, addressing privacy and aggregation issues in census datasets for improved modeling applications.

Contribution

The paper presents a novel deep learning framework that synthesizes detailed population and trip chain data, enhancing data availability for travel and demographic analysis.

Findings

Model outperforms recent approaches on multiple metrics

Generates realistic socioeconomic and mobility data

Addresses privacy and data aggregation challenges

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.