HD-GEN: A High-Performance Software System for Human Mobility Data Generation Based on Patterns of Life

Abstract: Understanding individual-level human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into actual movement behaviors but are often constrained by data sparsity and participant bias. Synthetic data, by contrast, offer scalability and flexibility but frequently lack realism. To address this gap, we introduce a comprehensive software pipeline for calibrating, generating, processing, and visualizing large-scale individual-level human mobility datasets that combine the realism of empirical data with the control and extensibility of Patterns-of-Life simulations. Our system consists of four integrated components. (1) a data generation engine constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs. (2) a genetic algorithm-based calibration module fine-tunes simulation parameters to align with real-world mobility characteristics, such as daily trip counts and radius of gyration, enabling realistic behavioral modeling. (3) a data processing suite transforms raw simulation logs into structured formats suitable for downstream applications, including model training and benchmarking. (4) a visualization module extracts key mobility patterns and insights from the processed datasets and presents them through intuitive visual analytics for improved interpretability.