As an emerging dynamic modeling method that incorporates time-dependent heterogeneity, hidden Markov models (HMM) are receiving increased research attention with regards to travel behavior modeling and travel demand forecasting. This paper focuses on the model transferability of HMM. Based on a series of transferability and goodness-of-fit measures, it finds that HMMs have a superior performance in predicting future transportation mode choice, compared to conventional choice models. Aimed at further enhancing its transferability, this paper proposes a Bayesian conditional recalibration approach that maps the model prediction directly to the context data. Compared to traditional model transferring methods, the proposed approach does not assume fixed parameterization and recalibrates the utilities and the prediction directly. A comparison between the proposed approach and the traditional transfer-scaling favors our approach, with higher goodness-of-fit. This paper fills the gap in understanding the transferability of HMM and proposes a practical method that enables potential applications of HMM.

中文翻译：

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测量和增强用于动态旅行行为分析的隐马尔可夫模型的可转移性

作为一种结合时间相关异质性的新兴动态建模方法，隐马尔可夫模型 (HMM) 在旅行行为建模和旅行需求预测方面受到越来越多的研究关注。本文重点研究HMM的模型可迁移性。基于一系列的可转移性和拟合优度度量，发现与传统的选择模型相比，HMMs 在预测未来交通方式选择方面具有优越的性能。为了进一步增强其可迁移性，本文提出了一种贝叶斯条件重新校准方法，将模型预测直接映射到上下文数据。与传统的模型转移方法相比，所提出的方法不假设固定参数化，而是直接重新校准效用和预测。所提出的方法与传统的转移缩放之间的比较有利于我们的方法，具有更高的拟合优度。本文填补了在理解 HMM 可转移性方面的空白，并提出了一种实用的方法，可以实现 HMM 的潜在应用。