ActVAE: Modelling human activity schedules with a deep conditional generative approach
This work addresses the problem of realistic activity schedule generation for demand modeling frameworks, though it appears incremental as it combines existing generative and conditional methods.
The paper tackled the challenge of modeling complex and diverse human activity schedules by developing a deep conditional generative approach, resulting in a model that rapidly generates precise and realistic schedules with practical data and computational requirements.
Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging. We demonstrate a deep conditional-generative machine learning approach for the modelling of realistic activity schedules depending on input labels such as an individual's age, employment status, or other information relevant to their scheduling. We combine (i) a structured latent generative approach, with (ii) a conditional approach, through a novel Conditional VAE architecture. This allows for the rapid generation of precise and realistic schedules for different input labels. We extensively evaluate model capabilities using a joint density estimation framework and several case studies. We additionally show that our approach has practical data and computational requirements, and can be deployed within new and existing demand modelling frameworks. We evaluate the importance of generative capability more generally, by comparing our combined approach to (i) a purely generative model without conditionality, and (ii) a purely conditional model which outputs the most likely schedule given the input labels. This comparison highlights the usefulness of explicitly modelling the randomness of complex and diverse human behaviours using deep generative approaches.