Empirical Investigation of Continuous Logit for Departure Time Choice with Bayesian Methods

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