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首页> 外文期刊>Transportation Research Part B: Methodological >Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation
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Analyzing heterogeneity and unobserved structural effects in route-switching behavior under ATIS: a dynamic kernel logit formulation

机译：在ATIS下分析路由切换行为中的异质性和未观察到的结构效应：动态内核logit公式

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This paper focuses on modeling unobserved effects in route-switching dynamics under advanced traveler information systems (ATIS). The analysis explicitly accounts for the presence of heterogeneity in behavior and a general stochastic pattern for the unobservables. The dynamic kernel logit (DKL) framework (also referred to as dynamic mixed logit) is proposed and applied to model route-switching dynamics (with 55 repeated decisions per user), based on data from interactive simulator experiments. In contrast to the multinomial probit framework, the DKL is well-suited for calibrating dynamic travel behavior models with a large number of panel periods. To increase computational efficiency, the proposed formulation exploits a components of variance scheme to represent the correlation of error-terms (both within-day and day-today). The empirical results indicate that unobserved effects account significantly for the observed variability in route-switching behavior. Among the observed effects, users' route-switching behavior is influenced by the nature, timeliness, and extent of real-time information, as also its quality. In addition, route switching is influenced by the level-of-service attributes on the alternative routes and users' prior traffic experience. Among the unobserved effects, the results present evidence of considerable heterogeneity in route switching. The significance of experience variables, and the correlation of unobservables over time and within-day, indicate the presence of dynamic learning and adjustment processes in user behavior under ATIS. Although observed and unobserved preference and response heterogeneity are all significant, the largest improvement in model fit is achieved by incorporating observed heterogeneity followed by unobserved preference and response heterogeneity respectively. These findings have significant applications in route assignment models under information, design and evaluation of ATIS products and services, and assessment of various policy measures aimed at travel demand management.
机译：本文着重于在高级旅行者信息系统（ATIS）下对路线转换动力学中未观察到的影响进行建模。该分析明确考虑了行为异质性的存在以及不可观察对象的一般随机模式。提出了动态内核logit（DKL）框架（也称为动态混合logit），并基于来自交互式模拟器实验的数据，将其应用于模型路由切换动力学（每个用户有55个重复决策）。与多项式概率框架相比，DKL非常适合用于校准具有大量面板周期的动态旅行行为模型。为了提高计算效率，提出的公式利用方差方案的组件来表示误差项的相关性（包括日内和日间）。实验结果表明，未观察到的影响显着说明了路由切换行为中观察到的变化。在观察到的效果中，用户的路由切换行为受实时信息的性质，及时性和范围以及其质量的影响。此外，路由切换还受备用路由上的服务级别属性和用户之前的流量体验的影响。在未观察到的影响中，结果提供了路由切换中大量异质性的证据。体验变量的重要性以及随着时间的推移和一天之内无法观察到的相关性，表明在ATIS下用户行为中存在动态学习和调整过程。尽管观察到的和未观察到的偏好和响应异质性都很重要，但模型拟合的最大改进是通过合并观察到的异质性，然后分别合并未观察到的偏好和响应异质性。这些发现在信息，ATIS产品和服务的设计和评估以及针对旅行需求管理的各种政策措施的评估下的路线分配模型中具有重要应用。

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