Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning

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.

References in corpus (6)

• Auto-Encoding Variational Bayes
• Denoising Diffusion Probabilistic Models
• Neural Discrete Representation Learning
• Scalable Population Synthesis with Deep Generative Modeling
• Data Driven Validation Framework for Multi-agent Activity-based Models
• Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis