用深度生成模型预测人类活动日程，支持个性化与多样化建模。
Black box behavioural modelling: Predicting human activity schedules with a deep conditional generative approach
- 基于条件生成框架，结合个人与家庭特征建模日程
- 能快速生成精确、真实且多样的活动日程
- 适合交通需求建模等需捕捉行为随机性的场景
人类活动日程的复杂性与多样性给建模带来挑战。本文提出ActVAE，一种深度条件生成机器学习方法，用于建模活动日程。该模型以个体、家庭及日程相关信息（如年龄、收入、公共交通可达性）为条件，生成依赖输入标签的精准、真实且多样化的日程。通过联合密度估计框架，我们对模型性能进行了全面评估与基准对比。本工作不仅提供了一种新型日程建模方式，更凸显了显式建模复杂多变人类行为随机性的价值。
原文摘要 · Abstract (English)
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.
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