arXiv subjects

Andrew Crooks

Publications and source records attributed to Andrew Crooks.

4 recordsLinked to original sources

arXiv2026

SF-LIFE: A Large-Scale Simulated Movement Dataset for the San Francisco Bay Area

We introduce SF-LIFE, a large-scale simulated movement dataset designed to accelerate research in transportation, mobility, and machine learning. The dataset contains 3,024,000,000,000 location records capturing complete, noise-free, multi-modality trajectories of 500,000 simulated agents observed at a 1Hz frequency navigating the San Francisco Bay Area network over a 70-day period. The data captures (1) needs-driven daily agendas of individual agents generated by an agent-based simulation of human patterns of life and (2) detailed kinematic trajectories moving agents across the OpenStreetMap representation of San Francisco using data from 40+ transit agencies across 9 counties. SF-LIFE provides unprecedented scale and detail as trajectories are based on real transit infrastructure using San Francisco General Transit Feed Specification (GTFS) data, having agent movements across multiple modalities, including bus, rail, bike, automobile, and walking. For this high-fidelity simulated representation of San Francisco, we provide (1) the full trajectory data annotated with transportation mode labels, (2) reduced-size versions of the trajectory data with reduced temporal frequency, (3) agent activity information describing the causal activity why an agent visits a place, (4) agent demographic data, and (5) the underlying OSM road network and building data. As the first dataset of its scale and level of detail, SF-LIFE overcomes the privacy, noise, and completeness limitations inherent in real-world tracking data, providing a robust and ethically sourced resource for research in transit optimization, human mobility analysis, and urban computing.

physics.soc-ph↗

arXiv2026

Using Zero-Shot LLM-Generated Survey Data for Geographically Explicit Population Synthesis

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 intelligence in all walks of life. This paper evaluates whether zero-shot large language model (LLM)-generated health survey data can serve as inputs to a conventional iterative proportional fitting (IPF) workflow for geographically explicit population synthesis. Using the 2023 Behavioral Risk Factor Surveillance System (BRFSS), we generate synthetic survey records for the U.S. states of Colorado and Mississippi with GPT-4.1 and Gemini-2.5-Pro. We use the generated data in an IPF-based synthesis pipeline and evaluate the resulting census tract-level synthetic populations against external benchmarks. Results show both LLMs capture several major state-level contrasts, indicating zero-shot generation produces geographically differentiated survey data. However, performance is strongly variable-dependent. Downstream effects in population synthesis are mixed, as IPF sometimes amplifies or reduces errors in the generated data. Spatial validation shows that LLM-based populations reproduce census tract-level patterns reasonably well, especially for variables that were more aligned with the ground truth data. Overall, the LLM-generated survey data shows promise as supplementary input, but not yet as a replacement for real survey data.

cs.CY↗

arXiv2026

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

Understanding individual-level human mobility is critical for a wide range of applications. Real-world trajectory datasets provide valuable insights into movement behaviors and patterns of life but are often constrained by data sparsity and participation bias. Synthetic data, by contrast, offers scalability and flexibility but frequently lacks realism. % To address this gap, we introduce a comprehensive software pipeline for generating, calibrating, processing, and visualizing large-scale individual-level human mobility datasets that combine the realism of empirical data with the control and extensibility simulations. % Our system consists of four integrated components: (1) a data generation engine that constructs geographically grounded simulations using OpenStreetMap data to produce diverse mobility logs; (2) a genetic algorithm--based calibration module that fine-tunes simulation parameters to align with real-world mobility characteristics; (3) a data processing suite that transforms raw simulation logs into structured formats suitable for downstream applications; and (4) a visualization module that extracts and presents key mobility patterns and insights from the processed datasets for improved interpretability. Evaluation of generated trajectory datasets for the Atlanta, Georgia, USA region show realistic behavior that, despite emerging from a simulation without any reference to real human individuals, exhibits realistic human behavior that closely matches aggregate metrics of real-world datasets. We also provide a sensitivity analysis to study what simulation parameters affect simulation runtime. Code and simulated datasets are shared to provide the broad research community with large-scale dataset that, albeit not real, exhibit realistic human behavior while being orders of magnitudes larger than any open real-world mobility dataset.

cs.SE↗

arXiv2024

The Patterns of Life Human Mobility Simulation

We demonstrate the Patterns of Life Simulation to create realistic simulations of human mobility in a city. This simulation has recently been used to generate massive amounts of trajectory and check-in data. Our demonstration focuses on using the simulation twofold: (1) using the graphical user interface (GUI), and (2) running the simulation headless by disabling the GUI for faster data generation. We further demonstrate how the Patterns of Life simulation can be used to simulate any region on Earth by using publicly available data from OpenStreetMap. Finally, we also demonstrate recent improvements to the scalability of the simulation allows simulating up to 100,000 individual agents for years of simulation time. During our demonstration, as well as offline using our guides on GitHub, participants will learn: (1) The theories of human behavior driving the Patters of Life simulation, (2) how to simulate to generate massive amounts of synthetic yet realistic trajectory data, (3) running the simulation for a region of interest chosen by participants using OSM data, (4) learn the scalability of the simulation and understand the properties of generated data, and (5) manage thousands of parallel simulation instances running concurrently.

cs.MA↗