Robust Pricing of Transportation Networks Under Uncertainty

摘要

Both public and private entities are concerned with the impacts of future toll revenue, and the effects of tolled facilities on system congestion. Due to the inherent complexity of transportation systems, it is impossible to predict travel demand and congestion conditions exactly, and simplistic attempts to account for this consistently underestimate true levels of congestion. Thus, in the context of roadway pricing, there is a need to develop mathematical models which explicitly account for both demand and supply uncertainty in both the short-term and long-term time scales. This project will develop these models, which will be suitable either to determine the best pricing policies to maximize revenue or minimize congestion, or to evaluate alternative toll policies according to these metrics. Thus, these models will produce more accurate predictions of toll revenues and congestion levels than are available using current methods.