Optimal Dynamic Pricing Strategies for High-Occupancy/Toll Lanes

This paper proposes a self-learning approach for determining pricing strategies for high-occupancy/toll lane operations. The approach learns recursively motorists’ willingness to pay by mining the loop detector data, and specifies toll rates to maximize the freeway’s throughput while ensuring a superior free-flow travel service to the users of the toll lanes. In determination of the tolls, a multi-lane hybrid traffic flow model is used to explicitly consider the impacts of the lane-changing behaviors before the entry points of the toll lanes. Simulation experiments are conducted to demonstrate and validate the proposed approach, and provide insights on when to convert high-occupancy lanes to toll lanes.