# Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach

arXiv: https://arxiv.org/abs/2512.04223 · HTML: https://mlsanity.com/paper/2512.04223/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 46 references that are in the map.
- First posted on arXiv 3 Dec 2025.

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [FiLM: Visual Reasoning with a General Conditioning Layer](https://mlsanity.com/paper/1709.07871/) (2017)
- [MINE: Mutual Information Neural Estimation](https://mlsanity.com/paper/1801.04062/) (2018)
- [Semi-Supervised Learning with Deep Generative Models](https://mlsanity.com/paper/1406.5298/) (2014)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Autoregressive Video Generation without Vector Quantization](https://mlsanity.com/paper/2412.14169/) (2024)
- [Adaptive Density Estimation for Generative Models](https://mlsanity.com/paper/1901.01091/) (2019)
- [An improved tabular data generator with VAE-GMM integration](https://mlsanity.com/paper/2404.08434/) (2024)
- [A Differentially Private Multi-Output Deep Generative Networks Approach For Activity Diary Synthesis](https://mlsanity.com/paper/2012.14574/) (2020)
- [Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes](https://mlsanity.com/paper/2406.11659/) (2024)
- [Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning](https://mlsanity.com/paper/2501.10221/) (2025)

## Abstract (arXiv)

Modelling the complexity and diversity of human activity scheduling behaviour is inherently challenging. We demonstrate ActVAE, a deep conditional-generative machine learning approach for the modelling of activity schedules. Suitable for application in activity-based demand modelling frameworks, schedules are modelled as conditional on individual, household and schedule information, such as age, income, and access to public transit. We demonstrate the rapid generation of precise, realistic and diverse schedules dependent on input labels. We extensively evaluate and compare model capabilities against baseline models using a joint-density estimation framework. In addition to providing a novel alternative to existing scheduling approaches, our work highlights the value of explicitly modelling the randomness of complex and diverse human behaviours.