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arXiv · 2501.10221

Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning

Fred Shone·Tim Hillel
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Abstract

Using a deep generative machine learning approach, we synthesise human activity participations and scheduling; i.e. the choices of what activities to participate in and when. Activity schedules are a core component of many applied transport, energy, and epidemiology models. Our data-driven approach directly learns the distributions resulting from human preferences and scheduling logic without the need for complex interacting combinations of sub-models and custom rules. This makes our approach significantly faster and simpler to operate than existing approaches to synthesise or anonymise schedule data. We additionally contribute a novel schedule representation and a comprehensive evaluation framework. We evaluate a range of schedule encoding and deep model architecture combinations. The evaluation shows our approach can rapidly generate large, diverse, novel, and realistic synthetic samples of activity schedules.

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Fred Shone, Tim Hillel. 2025-01-17. Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning. https://doi.org/10.1016/j.trc.2025.105273

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