Distance-Based Dynamic Pricing Strategy for Managed Toll Lanes

Congestion in highway networks inflicts economic and emotional stress on drivers and a considerable cost to society. To mitigate some of these effects, managed toll lanes are being designed and built in the United States. These managed toll lanes guarantee to drivers that they will be able to travel at free-flow speed during peak hours when the general lanes are congested. The strategy used to price tolls on managed toll lanes has become an important issue; however, few studies have focused on dynamic optimal pricing strategies. This paper formulates the pricing problem on the basis of a stochastic macroscopic traffic flow model and investigates the methodology used to find a pricing strategy to maximize the total expected revenue. A simulation-based numerical algorithm that obtains the optimal prices efficiently in real time is proposed. The methodology is also applicable and readily adjusted for other objective purposes of administrators, such as maximization of total throughput. The general pricing model developed in this paper is not limited to one specific underlying traffic flow model and is readily adapted to other macroscopic traffic models, such as the classical Lighthill–Whitham–Richards model.