During the coronavirus disease 2019 pandemic, the activity engagement and travel behavior of city residents have been impacted by government restrictions, such as temporary city-wide lockdowns, the closure of public areas and public transport suspension. Based on multiple heterogeneous data sources, which include aggregated mobility change reports and household survey data, this paper proposes a machine learning approach for dynamic activity chain pattern estimation with improved interpretability for examining behavioral pattern adjustments. Based on historical household survey samples, we first establish a computational graph-based discrete choice model to estimate the baseline travel tour parameters before the pandemic. To further capture structural deviations of activity chain patterns from day-by-day time series, we define the activity-oriented deviation parameters within an interpretable utility-based nested logit model framework, which are further estimated through a constrained optimization problem. By incorporating the long short-term memory method as the explainable module to capture the complex periodic and trend information before and after interventions, we predict day-to-day activity chain patterns with more accuracy. The performance of our model is examined based on publicly available datasets such as the 2017 National Household Travel Survey in the United States and the Google Global Mobility Dataset throughout the epidemic period. Our model could shed more light on transportation planning, policy adaptation and management decisions during the pandemic and post-pandemic phases.

2019年冠状病毒病大流行期间，城市居民的活动参与和出行行为受到政府限制措施的影响，例如全市临时封锁、公共区域关闭和公共交通暂停。基于多个异构数据源，包括汇总的流动性变化报告和家庭调查数据，本文提出了一种用于动态活动链模式估计的机器学习方法，并提高了检查行为模式调整的可解释性。基于历史家庭调查样本，我们首先建立基于计算图的离散选择模型来估计大流行前的基线旅行参数。为了进一步捕获每日时间序列中活动链模式的结构偏差，我们在可解释的基于效用的嵌套 Logit 模型框架内定义了面向活动的偏差参数，并通过约束优化问题进一步估计这些参数。通过将长短期记忆方法作为可解释的模块来捕获干预前后复杂的周期性和趋势信息，我们可以更准确地预测日常活动链模式。我们的模型的性能是根据公开的数据集（例如美国 2017 年全国家庭旅行调查和整个疫情期间的谷歌全球移动数据集）进行检验的。我们的模型可以更好地阐明大流行期间和大流行后阶段的交通规划、政策调整和管理决策。