A Data-Driven Approach to Managing High-Occupancy Toll (HOT) Lanes in California

Research Team: Michael Zhang (lead), Hang Gao, Di Chen, and Yanlin Qi

UC Campus(es): UC Davis

Problem Statement: Managing traffic flow in high-occupancy toll (HOT) lanes is a difficult balancing act. Current tolling schemes often lead to either under- or over-utilization of HOT lane capacity. The inherent linear/nonlinear relationship between HOT lane tolls and traffic flow suggest 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.

Project Description: This project developed a data-driven model 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 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 I-580.

Status: In Progress

Budget: $79,880