Planning-level Methodology for Evaluating Traveler Information Provision Strategies under Stochastic Capacity Conditions

In this study, a non-linear optimization-based conceptual framework is proposed for incorporating stochastic capacity, travel time performance functions and varying degrees of traveler knowledge in an advanced traveler information provision environment. The proposed method categorizes commuters into two classes: (1) those with access to perfect traffic information every day, and (2) those with knowledge of the expected traffic conditions across different days. Using a gap function framework (for describing the user equilibrium under different information availability), a mathematical programming model is formulated to describe the route choice behavior of the perfect information (PI) and expected travel time (ETT) user classes under stochastic day-dependent travel time. Driven by an operational algorithm suitable for large-scale networks, the model was applied to a simple corridor and medium-scale networks to illustrate the effectiveness of traveler information under stochastic capacity conditions.