The Future of Transportation Planning: Dynamic Travel Behavior Analyses based on Stochastic Decision-Making Styles

Over the past half-century, the progress of behavior research and demand forecasting has been spear headed and continuously propelled by the micro-economic theories, specifically utility maximization. There is no denying that the demand models today are sophisticated and in most cases capable of forecasting the main stream of behavior in urban areas. However, a quick scan of those models or behavior analyses reveals great discrepancies between what is expected and the actual capabilities of the models. These discrepancies go beyond the statistical errors between actual behavior and demand forecasts, based on theoretical assumptions. The authors of this paper approach the behavior analysis differently. They posed and attempted to answer the following questions: are we applying the appropriate assumptions to the right people for their behavior? Are the assumptions we use to reflect the actual decision-making processes for the travelers correct? The proposed approach suggests that we stand back and look at a few levels up along the decision-making process. The conceptual framework of this approach includes: a travel behavior survey that collects data on decision-making styles; stochastic processes to capture the linkage between decision-making styles and traveler's characteristics; and dynamic assignments of choice models according to identified decision-making styles. It is clear that great challenges lie ahead for the approach proposed here. The lack of development of less researched decision-making styles creates great challenges. However it also provides opportunities for transportation professionals to explore along multiple paths of investigation. The conceptual framework and initial attempts presented here may serve as a stimulus for further explorations. Reasonable representations for different types of decision-making styles will help transportation professionals to understand the fundamentals of behaviors. The better understanding of the behavior and demand will further help in developing more efficient transportation systems.