01155139

Component

https://doi.org/10.3141/2165-07

http://scholar.google.com...lman&publication_year=2010

Numerous models of travel timing have been calibrated and reported in the literature. Some studies have treated time as a discrete variable by using familiar discrete choice methods, whereas 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; this model offers the advantage of a continuous-time response. A random utility maximization structure is used to capitalize on the key advantages of both main approaches to the modeling of travel timing. 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 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.

01220491

10-1380

English

pp 59-68

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309142977

Print

Figures (3) ; References (34) ; Tables (3)

Automobile travel; Departure time; Empirical methods; Least squares method; Logits; Periods of the day; Public transit; Regression analysis; Travel time; Work trips

Bayesian approach; Random utility; Utility maximization

Highways; Planning and Forecasting; Public Transportation; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 25 2010 10:37AM

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