Strategic Traffic Assignment: Models and Applications to Capture Day-to-Day Flow Volatility

Abstract

Traffic assignment models continue to play a critical role in the transportation planning process. Furthermore, day-to-day traffic flow volatility is a wellacknowledged phenomenon that planners and researchers alike view as increasingly important. Consequentially, current research advances have been addressing more complex assignment models capable of representing various aspects of volatility. However despite the importance of accounting for volatility, deployed assignment models capable of large-scale application have continued relying on traditional assumptions of determinism and perfect information.

This research focuses on the impact of day-to-day demand uncertainty on equilibrium-based traffic models by advancing the concept of strategic traffic assignment. In the strategic user equilibrium (StrUE) model, the daily travel demand is treated as a random variable, and users are assumed to have knowledge about the day-to-day demand but are unaware of the specific traffic conditions they will experience during travel. Therefore, drivers make a strategic route choice to minimize their expected travel cost and follow that route independent of the experienced conditions. The result is an equilibrium assignment based on link flow proportions, as opposed to link flow volumes. Furthermore, as the day-to-day demand realization changes, the equilibrium flow proportions will remain the same. Thus, the resulting flows may appear volatile on a day-to-day basis, but can actually be represented by a higher level mathematical equilibrium.