Abstract
Managing traffic flow in high-occupancy toll (HOT) lanes is a tough balancing act and current tolling schemes often lead to either under- or over-utilization of HOT lane capacity. The inherent linear/nonlinear relationship between flow and tolls in HOT lanes suggests that recent advances in machine learning and the use of a data-driven model may help set toll rates for optimal flow and lane use. In this research project, a data-driven model was developed, using long short-term memory (LSTM) neural networks to capture the underlying flow-toll pattern on both HOT and general-purpose lanes. Then, a dynamic control strategy, using a linear quadratic regulator (LQR) feedback controller was implemented to fully utilize the HOT lane capacity while maintaining congestion-free conditions. A case study of the I-580 freeway in Alameda County, California was carried out. The control system was evaluated in terms of vehicle hours traveled and person-hours traveled for solo drivers and carpoolers. Results show that the tolling strategy helps to mitigate congestion in HOT and general-purpose lanes, benefiting every traveler on the I-580.
