The rapid proliferation of smart, personal technologies has given birth to smart Transportation Demand Management (TDM) systems that can give personalized incentives to users. This personalization capacity builds on accurate modeling of user behaviors; however, in practice, a user’s behavior data is often limited, and his preferences in the discrete choice-making process may change or evolve. In this paper, we propose a new online-updating model that can accurately and efficiently estimate an individual’s preferences from his discrete choices. Our model is built on the concept of canonical structure, where a set of canonical models are identified as the common preference patterns shared by the whole population, and a membership vector is also identified for each individual to capture the degrees of the resemblance of his preferences to those common preference patterns. To allow preference to vary in the choice-making process, a time-varying model can be integrated with the canonical structure. In the current study, we use a simple cubic polynomial model with a single variant and show the detailed formulation of the integrated model. An online-updating strategy is also proposed, such that it is possible to update the parameters partially in practice. The proposed model is suitable for modeling a heterogeneous population with insufficient data from each individual. Both simulation studies and a real-world application are taken in the current study. The results show that comparing with other frequently used models, the model we proposed has the highest accuracy in preference learning and behavior prediction.

智能个人技术的迅速普及催生了智能交通需求管理（TDM）系统，该系统可以为用户提供个性化的激励。这种个性化能力建立在对用户行为的准确建模上；然而，在实践中，用户的行为数据通常是有限的，并且他在离散选择过程中的偏好可能会改变或发展。在本文中，我们提出了一种新的在线更新模型，该模型可以根据个人的离散选择准确而有效地估计其偏好。我们的模型建立在规范结构的概念之上，在规范结构中，一组规范模型被确定为整个人群共有的共同偏好模式，并且还为每个个体标识了隶属度向量，以捕获其偏好与那些常见偏好模式的相似程度。为了使偏好在选择过程中发生变化，可以将时变模型与规范结构集成在一起。在当前的研究中，我们使用具有单个变量的简单三次多项式模型，并显示集成模型的详细公式。还提出了一种在线更新策略，从而可以在实践中部分更新参数。所提出的模型适用于对每个人的数据不足的异类种群进行建模。在当前研究中同时进行了仿真研究和实际应用。结果表明，与其他常用模型相比，