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Dynamic Travel Mode Searching and Switching Analysis Considering Hidden Modal Preference and Behavioral Decision Processes

机译:考虑隐性模式偏好和行为决策过程的动态出行模式搜索与切换分析

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This paper proposes a conceptual framework to model the travel mode searching and switchingdynamics. The proposed approach is structurally different from existing mode choice models inthe way that a non-homogeneous hidden Markov model (HMM) has been constructed andestimated to model the dynamic mode searching process. In the proposed model, each hiddenstate represents the latent modal preference of each traveler. Interestingly, the empiricalapplication suggests that the states can be interpreted as car-loving and carpool/transit loving,respectively. At each time period, the transitions between the states are functions of time-varyingcovariates such as travel time and travel cost of the habitual modes. The level-of-service (LOS)changes are believed to have an enduring impact by shifting travelers to a different state. Whilelongitudinal data is not readily available, the paper develops an easy-to-implement memory recallsurvey to collect required process data for the empirical estimation. Bayesian estimationand Markov chain Monte Carlo method have been applied to implement full Bayesian inference.As demonstrated in the paper, the estimated HMM is reasonably sensitive to various scenariosregarding mode-specific LOS changes and can capture individual and system dynamics. Onceintegrated with travel demand and/or traffic simulation models, the proposed model can emittime-dependent multimodal behavior responses to various planning/policy stimuli.
机译:本文提出了一个用于旅行模式搜索和转换建模的概念框架。 动力学。所提出的方法在结构上与现有的模式选择模型不同 非均匀隐马尔可夫模型(HMM)的构造方法以及 估计可以对动态模式搜索过程进行建模。在建议的模型中,每个隐藏 状态代表每个旅行者的潜在模态偏好。有趣的是,经验 应用建议将状态解释为爱车和拼车/过境, 分别。在每个时间段,状态之间的转换都是随时间变化的函数 协变量,例如习惯模式的出行时间和出行成本。服务水平(LOS) 人们认为,通过将旅行者转移到其他国家/地区,变化会产生持久的影响。尽管 纵向数据尚不可用,该论文开发了易于实现的记忆调用 进行调查以收集所需的过程数据以进行经验估算。贝叶斯估计 马尔可夫链和蒙特卡罗方法已用于实现完整的贝叶斯推理。 如本文所示,估计的HMM对各种情况都相当敏感 有关特定于模式的LOS变化的信息,并且可以捕获个人和系统动态。一次 与旅行需求和/或交通模拟模型集成后,所提出的模型可以发出 对各种计划/政策刺激的时间依赖性多模式行为反应。

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