Work

Synthesising activity participations and scheduling with deep generative machine learning

Year: 2025

Type: article

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.

Source: Transportation Research Part C Emerging Technologies

DOI: 10.1016/j.trc.2025.105273

Authors: Fred Shone, Tim Hillel

Institution: University College London

Language: English

Topic: demographic modeling and climate adaptation

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $4,040

Funder: Engineering and Physical Sciences Research Council