Simultaneous Estimation of Flexible Models and Associated Hyperparameters: An Application to Travel Activity-Duration Modeling:

Estimation of flexible-statistical models of travel demand involves tuning varying parameters, hyperparameters, manually and iteratively. Proper tuning of hyperparameters results in superior models. However, considerable expertise, including technical knowledge of statistics, data mining or machine learning, and experience are required to tune hyperparameters and consequently generate appropriate models. Moreover, tuning hyperparameters is prone to subjective error and consequently produces travel demand models that are difficult to reproduce and extend, and makes the development more an art than a science. There is a need for methods to reduce or eliminate subjectivity during the tuning process. This study proposed a framework to reduce subjectivity during the tuning of hyperparameters required for the estimation of nonparametric models of activity-duration. That is, a flexible-statistical framework, which leverages state-of-the-art innovations in Bayesian optimization (BO), was proposed to estimate Gaus...