Empirical Investigation of the Continuous Logit for Departure Time Choice using Bayesian Methods

Numerous models of travel timing have been calibrated in the literature. Some treat time as a discrete variable using familiar discrete choice methods, while others have treated time in a continuous fashion. Both approaches offer distinct advantages. Here, a continuous logit model of work tour departure time choice is estimated, which offers the advantage of continuous-time response using a random utility maximization structure, thus capitalizing on the key advantages of both main approaches to travel timing modeling. Bayesian techniques are used to estimate model parameters, and estimation results suggest a variety of predictive densities for departure times across different individuals. In addition, ordinary least squares (OLS) regression models are used to estimate travel times and their variance across times of day for the auto and transit modes. These network variables are used to inform estimation of the continuous logit model of departure time. The results are meaningful for multiple applications, and the continuous logit can readily be extended to a two-dimensional choice construct, such that the departure and return times can be modeled simultaneously. In addition, Bayesian estimation techniques allow for the utility function to take any number of forms, which may offer greater predictive ability.