Comparative Analysis of Dynamic Pricing Strategies for Managed Lanes

PI:  Jorge Laval, Ph.D., Georgia Institute of Technology

Co-PIs: Yafeng Yin, Ph.D., University of Florida; Yingyan Lou, Ph.D., The University of Alabama

UTC Project Information


There are currently more than 20 transportation infrastructure facilities in operation today in the United States subject to congestion pricing. Most of them (12) are Managed Lanes (ML), and this number is increasing rapidly. The pricing strategies in these facilities are inspired by the congestion pricing concept borrowed from the economics literature. This concept has been thoroughly adapted to the case of traffic flow for the case of static network conditions. Little has been done, however, in the dynamic case, where travel time delays due to congestion may change rapidly in time. We do know, for example, that the marginal cost of an alternative is a decreasing function of time, and that pricing according to marginal cost may not lead to minimum system cost. But these results have not been transferred to practice. This is unfortunate because existing real-time pricing strategies, which are mostly ad-hoc adaptations, will not lead to the desired result, and in some cases (as our initial results suggest), may lead to significantly worse system performance compared to time-of-day pricing.

The objective of this research is to investigate the performances of different dynamic pricing strategies for ML facilities. The focus will be on the traffic dynamics resulting from each pricing strategy and the benefits and costs thereof. The problem will be analyzed from three different perspectives: the users, the tolling authority (i.e. DOT), and the society, which leads to three different performance measures. Our research will tackle the problem by combining empirical data from existing ML facilities, analytical models of simple networks, and simulation of larger networks.