Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis

TL;DR

This paper introduces a novel deep generative model for human mobility that captures activity patterns and location trajectories, adaptable to diverse regions and useful for urban planning and public health.

Contribution

It presents a new generative deep learning approach that models human mobility by integrating activity and location data, with transferability across regions.

Findings

Outperforms existing models in generating realistic activity-location chains

Demonstrates high accuracy on nationwide US data

Shows transferability to regional datasets from California, Washington, and Mexico City

Abstract

Human mobility plays a crucial role in transportation, urban planning, and public health. Advances in deep learning and the availability of diverse mobility data have transformed mobility modeling. However, existing deep learning models often focus on spatio-temporal patterns and struggle to capture the semantic interdependencies among activities, while also being limited by specific data sources. These challenges reduce their realism and adaptability. Traditional activity-based models (ABMs) face issues as well, relying on rigid assumptions and requiring extensive data, making them costly and difficult to adapt to new regions, especially those with limited conventional travel data. To address these limitations, we develop a novel generative deep learning approach for human mobility modeling and synthesis that incorporates both activity patterns and location trajectories using open-source data. The model can be fine-tuned with local data, allowing it to adapt to and accurately represent mobility patterns across diverse regions. The model is evaluated on a nationwide dataset of the United States, where it demonstrates superior performance in generating activity-location chains that closely follow ground truth distributions. Further tests using state- or city-specific datasets from California, Washington, and Mexico City confirm its transferability. This innovative approach offers substantial potential to advance mobility modeling research, particularly in generating synthetic human mobility data. This can provide urban planners and policymakers with enhanced tools for simulating mobility in diverse regions and better informing decisions related to transportation, urban development, and public health.