This paper presents a methodology for enhancing discrete choice models for managed lane travel behavior with personal trip history. We refer to this process as personalization and the enhanced model as a personalized choice model. With the objective of better understanding managed lane choices and improving the model’s prediction capability, personalization was carried out at two levels. First, we used each traveler’s habits and travel experiences before each trip for constructing a set of explanatory variables that could be used with any model structure. Second, under a logit mixture framework, the distribution of random parameters was updated with Bayesian inference according to personal trip history. The structure of the parameter distribution explicitly considered preference variations across individuals (interpersonal heterogeneity), as well as preference variations across trips performed by the same individual (intrapersonal heterogeneity). The proposed methodology is especially relevant for modeling revealed preference (RP) data from automatic vehicle identification sensors, for which limited socioeconomic characteristics of travelers are available. An empirical study was conducted on an operational managed lane corridor near Dallas/Fort Worth Airport in Texas. Available trip records over a 5-month period were utilized. A hierarchical Bayes estimator was adopted for efficient model estimation. The results suggest significant inter- and intrapersonal heterogeneity and that the proposed personalization method improves the model’s explanatory power and prediction capability. To the best of our knowledge, this paper represents the first introduction of personalization in managed lane choice behavior modeling and the first attempt to estimate intrapersonal heterogeneity with RP data.

本文提出了一种方法，可通过个人出行历史来增强管理车道出行行为的离散选择模型。我们将此过程称为个性化，将增强模型称为个性化选择模型。为了更好地了解可管理的车道选择并提高模型的预测能力，在两个级别上进行了个性化设置。首先，我们在每次旅行之前利用每个旅行者的习惯和旅行经历来构建一套可以与任何模型结构一起使用的解释变量。其次，在logit混合框架下，根据个人旅行历史记录，使用贝叶斯推断更新了随机参数的分布。参数分布的结构明确考虑了个体之间的偏好变化（人际异质性），以及同一个人跨旅程的偏好变化（人际异质性）。所提出的方法特别适用于对来自自动车辆识别传感器的显示的偏好（RP）数据进行建模，对于这些数据，旅行者的社会经济特征有限。在得克萨斯州达拉斯/沃思堡机场附近的可管理行车道走廊上进行了实证研究。利用了5个月内的可用行程记录。采用分层贝叶斯估计器进行有效的模型估计。结果表明，人与人之间存在很大的异质性，并且所提出的个性化方法提高了模型的解释力和预测能力。据我们所知，