Abstract
Most discrete choice models assume steady state conditions and a fully equilibrated system when estimating unknown coefficients from real-world data. However, the estimated model can be biased when the data set used for the model estimation was drawn from non- or less-equilibrated traveler behavior. The resulting biased model could lead to a misunderstanding of the system. Such effects on discrete choice model estimation were examined by performing Monte Carlo simulation experiments. A day-to-day dynamic evolutionary framework was used to observe changes in traveler's choice and to compare the estimated results during the adjustment process with the true behavior parameters.