The increased interest in time use among transport researchers has led to a search for flexible but tractable models of time use, such as Bhat's Multiple Discrete Continuous Extreme Value (MDCEV) model. MDCEV formulations typically model aggregate time allocation into different activity types during a given period, such as the amount of time spent working and shopping in a day. While these applications provide valuable insights into activity participation, they ignore disaggregate activity-episodes, that is the fact that people might split their total time spent working in multiple separate blocks, with breaks or other activities in between. Insights into this splitting into episodes are necessary for predicting trips and understanding time use satiation. We propose a modified MDCEV model where an activity-episode, rather than an activity type, is the basic choice alternative, using a modified utility function to capture the reduced likelihood of individuals performing a very large number of episodes of the same activity. Results from two large revealed preference datasets exhibit equivalent forecast accuracy between the traditional and proposed approach at an aggregate level, but the latter also provides insights on the number and duration of activity-episodes with significant accuracy.

交通研究人员对时间使用的兴趣日益浓厚，因此开始寻找灵活但易于处理的时间使用模型，例如 Bhat 的多重离散连续极值 (MDCEV) 模型。MDCEV 公式通常对给定时间段内不同活动类型的总时间分配进行建模，例如一天中工作和购物的时间量。虽然这些应用程序提供了对活动参与的宝贵见解，但它们忽略了分解的活动片段，即人们可能将他们花费在多个单独块中工作的总时间分开，中间有休息或其他活动。洞察这种分裂成的情节对于预测旅行和理解时间使用饱和度是必要的。我们提出了一个改进的 MDCEV 模型，其中一个活动集，而不是活动类型，是基本的选择替代方案，使用修改后的效用函数来捕捉个体执行大量相同活动的可能性降低。来自两个大型显示偏好数据集的结果在总体水平上表现出传统方法和提议方法之间等效的预测准确性，但后者还以显着的准确性提供了有关活动集的数量和持续时间的见解。