用深度生成模型快速合成真实多样的人类活动安排。
Synthesising Activity Participations and Scheduling with Deep Generative Machine Learning
- 基于深度生成模型直接学习人类行为偏好与时间安排逻辑。
- 可快速生成大量新颖且真实的活动日程样本。
- 适合交通、能源、流行病学等需日程数据的建模研究者。
我们采用深度生成机器学习方法，合成人类活动参与及时间安排，即选择参与哪些活动以及何时参与。活动日程是交通、能源和流行病学等诸多应用模型的核心组成部分。该数据驱动方法无需复杂的子模型组合与定制规则，直接学习由人类偏好和调度逻辑产生的分布，显著提升了现有日程数据合成或匿名化方法的速度与操作简便性。我们还提出一种新型日程表示方法与全面评估框架，评估了多种编码方式与深度模型架构的组合。结果表明，该方法能快速生成大规模、多样化、新颖且真实的合成活动日程样本。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。