A Data-Driven Approach to Manage High-Occupancy Toll Lanes in California

Michael PhD Zhang, Hang PhD Gao, Di Chen and Yanlin Qi

Institute of Transportation Studies, Working Paper Series from Institute of Transportation Studies, UC Davis

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 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. 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 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.

Keywords: Engineering; High occupancy toll lanes; traffic flow; traffic models; highway traffic control systems (search for similar items in EconPapers)
Date: 2024-06-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-tre and nep-ure
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