ActVAE: Modelling human activity schedules with a deep conditional generative approach

TL;DR

This paper introduces ActVAE, a deep conditional generative model that efficiently produces realistic human activity schedules based on individual attributes, enhancing demand modeling with a novel architecture and comprehensive evaluation.

Contribution

The paper presents a new Conditional VAE architecture that models diverse human activity schedules conditioned on personal attributes, improving realism and computational efficiency.

Findings

The model accurately generates realistic activity schedules for various input labels.

It outperforms purely generative or purely conditional models in capturing schedule diversity.

The approach is practical for deployment in demand modeling frameworks.

Abstract

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.