A synthetic population of Sweden: datasets of agents, households, and activity-travel patterns

A synthetic population is a simplified microscopic representation of an actual population. Statistically representative at the population level, it provides valuable inputs to simulation models (especially agent-based models) in research areas such as transportation, land use, economics, and epidemiology. This article describes the datasets from the Synthetic Sweden Mobility (SySMo) model using the stateof-art methodology, including machine learning (ML), iterative proportional fitting (IPF), and probabilistic sampling. The model provides a synthetic replica of over 10 million Swedish individuals (i.e., agents), their household characteristics, and activity-travel plans. This paper briefly explains the methodology for the three datasets: Person, Households, and Activity-travel patterns. Each agent contains sociodemographic attributes, such as age, gender, civil status, residential zone, personal income, car ownership, employment, etc. Each agent also has a household and corresponding attributes such as household size, number of children <= 6 years old, etc. These characteristics are the basis for the agents' daily activity-travel schedule, including type of activity, start-end time, duration, sequence, the location of each activity, and the travel mode between activities. (c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license