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

2026-07-22 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Software Development & Engineering · Depth: Advanced, extended

Summary

HD-GEN is a high-performance software system designed to generate large-scale, realistic synthetic human mobility data by integrating empirical data realism with Patterns-of-Life (POL) simulations. The system comprises four core components: a data generation engine that constructs geographically grounded simulations using OpenStreetMap data; a genetic algorithm-based calibration module that fine-tunes simulation parameters to align with real-world mobility characteristics, such as daily trip counts, using datasets like GeoLife; a data processing suite that transforms raw simulation logs into structured formats, including coordinate system conversions from EPSG:26782 to EPSG:4326; and a visualization module for extracting and presenting key mobility patterns. HD-GEN enhances the underlying POL framework with features like checkpointing, real-time log streaming, improved scalability for parallel execution, and Python-based usability tools. It can generate massive datasets, including those over 1.5 TB, and supports injecting controlled anomalies with varying intensity levels (red, orange, yellow) for anomaly detection research, demonstrating efficiency on a 2.40 GHz Intel i5-1135G7 processor.

Key takeaway

For Machine Learning Engineers or Research Scientists needing realistic human mobility data, HD-GEN offers a robust solution. You can generate large-scale, calibrated synthetic datasets that mirror real-world patterns, overcoming privacy and sparsity issues. Utilize its genetic algorithm to fine-tune simulations against empirical metrics, or inject specific anomalies for anomaly detection model training. This system provides scalable, reproducible data for diverse applications.

Key insights

HD-GEN combines realistic empirical data features with scalable Patterns-of-Life simulations for synthetic human mobility data generation.

Principles

• Human mobility simulations benefit from modeling needs and routines.
• Genetic algorithms can calibrate synthetic data to real-world metrics.
• Deterministic simulations enable reproducible scenario branching.

Method

HD-GEN's pipeline involves map generation, parallel POL simulation, genetic algorithm calibration against real data metrics (e.g., ADT, ADA, MXD, MDD), log processing, and visual analytics.

In practice

• Use OpenStreetMap data to ground mobility simulations geographically.
• Calibrate simulation parameters using real-world datasets like GeoLife.
• Inject controlled anomalies for robust anomaly detection research.

Topics

• Human Mobility Data
• Synthetic Data Generation
• Patterns-of-Life Simulation
• Genetic Algorithm Calibration
• Geospatial Trajectories
• Anomaly Detection

Code references

• onspatial/hd-gen-large-scale-human-mobility-generator

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.