# Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning

arXiv: https://arxiv.org/abs/2501.10221 · HTML: https://mlsanity.com/paper/2501.10221/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 17 Jan 2025.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Variational Inference with Normalizing Flows](https://mlsanity.com/paper/1505.05770/) (2015)
- [An Introduction to Variational Autoencoders](https://mlsanity.com/paper/1906.02691/) (2019)
- [Conditional Image Generation with PixelCNN Decoders](https://mlsanity.com/paper/1606.05328/) (2016)
- [Generating Sentences from a Continuous Space](https://mlsanity.com/paper/1511.06349/) (2015)
- [Reliable Fidelity and Diversity Metrics for Generative Models](https://mlsanity.com/paper/2002.09797/) (2020)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [A note on the evaluation of generative models](https://mlsanity.com/paper/1511.01844/) (2015)
- [DRAW: A Recurrent Neural Network For Image Generation](https://mlsanity.com/paper/1502.04623/) (2015)
- [A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music](https://mlsanity.com/paper/1803.05428/) (2018)
- [A Deep Generative Model for Feasible and Diverse Population Synthesis](https://mlsanity.com/paper/2208.01403/) (2022)
- [Adaptive Density Estimation for Generative Models](https://mlsanity.com/paper/1901.01091/) (2019)
- [Deep Activity Model: A Generative Approach for Human Mobility Pattern Synthesis](https://mlsanity.com/paper/2405.17468/) (2024)
- [Scalable Population Synthesis with Deep Generative Modeling](https://mlsanity.com/paper/1808.06910/) (2018)
- [A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis](https://mlsanity.com/paper/2012.14574/) (2020)
- [Data Driven Validation Framework for Multi-agent Activity-based Models](https://mlsanity.com/paper/1502.07601/) (2015)

## Built on by (most cited first, 3 of 3)

- [Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives](https://mlsanity.com/paper/2509.06656/) (2025)
- [Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach](https://mlsanity.com/paper/2512.04223/) (2025)
- [Masked Diffusion for Travel Diaries Synthesis](https://mlsanity.com/paper/2610.17109/) (2026)

## Abstract (arXiv)

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.