Modeling Differences in Driver Left-Turn Gap Acceptance Behavior Using Bayesian and Bootstrap Approaches

A Bayesian and Bootstrap logistic left-turn gap acceptance model is developed using 2,730 field observations. The variables that are considered in the model include the gap duration; the driver's wait time in search of an appropriate acceptable gap; the time traveled by a driver to clear the conflict point; and the rain intensity. The model demonstrates that the acceptable time gap decreases as a function of the driver's wait time and increases as the rain intensity increases. The Bayesian and Bootstrap approaches are demonstrated to estimate consistent model parameters. A procedure for modeling the Bayesian realizations that captures parameter correlations without the need to store all parameter combinations is developed. The proposed procedure is demonstrated to produce results that are consistent with the use of the Bayesian realizations. The study then demonstrates how the model produces stochastic realizations of opposed saturation flow rates using a Monte Carlo simulation example application. © 2011 Published by Elsevier Ltd.