Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning
This addresses the need for efficient and realistic schedule data synthesis in applied transport, energy, and epidemiology models, representing an incremental improvement over prior methods.
The paper tackles the problem of synthesizing human activity participations and scheduling using a deep generative machine learning approach, resulting in a method that is significantly faster and simpler than existing approaches and can generate large, diverse, novel, and realistic synthetic samples.
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.