Understanding travelers' route choice behavior under uncertainty

The overall goal of this research is to measure drivers' attitudes towards uncertain and unreliable routes. The route choice modeling is done within the discrete choice modeling framework and involved use of stated preference data. The first set of analysis elicits travelers' attitudes towards unreliable routes. The results of the analysis provide useful information in relation to how commuters value the occurrence/chances of experiencing delay days on their routes. The frequency of days with unexpected delays also measures the travel time reliability in a way that is easy to understand by day-to-day commuters. As such, behaviorally more realistic values are obtained from this analysis in order to capture travelers' attitudes towards reliability. Then, we model attitudes toward travel time uncertainty using non-expected utility theories within the random utility framework. Unlike previous studies that only include risk attitudes, we incorporate attitudes toward ambiguity too, where drivers are assumed to have imperfect knowledge of travel times. To this end, we formulated non-linear logit models capable of embedding probability weighting, and risk/ambiguity attitudes. A more realistic willingness to pay structure is then derived which takes into account travel time uncertainty and behavioral attitudes. Finally, we present a conceptual framework to use a descriptive utility theory, i.e. cumulative prospect theory in forecasting the demand for a variable tolled lane. We have highlighted the issues that arise when a prescriptive model of behavior is applied to forecast demand for a tolled lane.