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A nonparametric Bayesian approach is proposed to estimate an integrated travel demand model of work duration and commute mode. The proposed nonparametric model, a generalized additive Gaussian process model, precludes the need to prespecify a functional form. The additive structure of the model enables computational tractability in high dimensions of covariates. Bayesian estimation was adopted for inference to quantify uncertainty using probability distributions.
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  • An Integrated Travel Demand Model of Work Duration and Commute-mode Choices: A Flexible Modeling Approach

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This study proposed a Bayesian nonparametric framework to capture implicitly hidden structure in time-series having limited data. The proposed framework, a Gaussian process with a spectral mixture kernel, was applied to time-series process for insider-threat detection. The proposed framework addresses two current challenges when analyzing quite noisy time-series having limited data whereby the time series are visualized for noticeable structure such as periodicity, growing or decreasing trends and hard coding them into pre-specified functional forms.



Automating Hyperparameter tuning

Bayesian optimization provides a principled approach to automate hyperparameter tuning.

Integrated Travel Demand Modeling

Currently, there’s growing research interest in integrated travel demand models.

Latent Travel Patterns from Mobile Phone Data

Currently, there’s growing research interest in integrated travel demand models.