Abstract:Computer-based microsimulation is evolving as a useful tool for the collection of travel behavior data. Analysis of the route choice problem in particular demands sequential data to capture the behavioral dynamics involved. The use of microsimulations to collect data of this type is in its infancy, because microcomputers powerful enough for this type of simulation have only recently become available. One such simulation recently completed at the University of California at Davis resulted in a data set that will support dynamic modeling. The simulation collected 32 sequential binary route choice decisions made by 343 subjects under various experimental conditions. The experimental factors included information accuracy, feedback, provision of descriptive rationale for route advice, indication of one route alternative as a freeway, and control for stops on the side road route. An analysis of the experimental treatments used in the simulation is presented, and a dynamic probabilistic model of subjects' advice compliance is developed. A regression approach was used to estimate the factor effects of an analysis of variance model of the experimental treatments. Dynamics were introduced into the model by the development of a perception variable that, when it is incorporated, leads to the adaptive expectations model. A linearized model of relative frequencies incorporating lagged dependent variables to account for behavioral dynamics is formulated and estimated. Econometric methods of pooled cross-sectional, time-series analysis are used to estimate models that account for heteroskedasticity and autocorrelation.