7 papers
SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area
Chanuka Algama, Taylor Anderson, Henrique Ferraz de Arruda +14
We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000…
Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis
Taylor Anderson, Sara Von Hoene, Orhan Yagizer Cinar +4
There is a growing interest in utilizing synthetic populations for a diverse range of applications. At the same time, we are witnessing a tremendous growth in artificial intelligen…
Towards Universal Urban Patterns-of-Life Simulation
Sandro M. Reia, Henrique F. de Arruda, Shiyang Ruan +3
Understanding urban mobility requires models that capture how people interact with and navigate the built environment. We present a scalable, generalizable agent-based framework in…
HD-GEN: A High-Performance Software System for Human Mobility Data Generation Based on Patterns of Life
Hossein Amiri, Joon-Seok Kim, Hamdi Kavak +4
Understanding individual-level human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into movement behaviors and pat…
Evaluation of A Spatial Microsimulation Framework for Small-Area Estimation of Population Health Outcomes Using the Behavioral Risk Factor Surveillance System
Emma Von Hoene, Aanya Gupta, Hamdi Kavak +2
This study introduces the Spatial Health and Population Estimator (SHAPE), a spatial microsimulation framework that applies hierarchical iterative proportional fitting (IPF) to est…
All Models Are Wrong, But Can They Be Useful? Lessons from COVID-19 Agent-Based Models: A Systematic Review
Emma Von Hoene, Sara Von Hoene, Szandra Peter +9
The COVID-19 pandemic prompted a surge in computational models to simulate disease dynamics and guide interventions. Agent-based models (ABMs) are well-suited to capture population…