While activity-based travel demand generation has improved over the last few decades, the behavioural richness and intuitive interpretation remain challenging. This paper argues that it is essential to understand why people travel the way they do and not only be able to predict the overall activity patterns accurately. If one cannot understand the “why?” then a model’s ability to evaluate the impact of future interventions is severely diminished. Bayesian networks (BNs) provide the ability to investigate causality and is showing value in recent literature to generate synthetic populations. This paper is novel in extending the application of BNs to daily activity tours. Results show that BNs can synthesise both activity and trip chain structures accurately. It outperforms a frequentist approach and can cater for infrequently observed activity patterns, and patterns unobserved in small sample data. It can also account for temporal variables like activity duration.

在过去的几十年中，尽管基于活动的旅行需求的产生有所改善，但行为丰富性和直观解释仍然具有挑战性。本文认为，了解人们为什么要以自己的方式旅行是至关重要的，不仅要能够准确地预测总体活动模式。如果无法理解“为什么？” 则模型评估未来干预措施影响的能力将大大降低。贝叶斯网络（BNs）提供了调查因果关系的能力，并且在最近的文献中显示了产生合成种群的价值。本文在将BN的应用扩展到日常活动旅行中是新颖的。结果表明，BNs可以准确地合成活性和绊链结构。它优于常规方法，可满足不经常观察到的活动模式以及小样本数据中未观察到的模式。它还可以考虑诸如活动持续时间之类的时间变量。