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

Author

Listed:
  • Zhang, Michael PhD
  • Gao, Hang PhD
  • Chen, Di
  • Qi, Yanlin

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.

Suggested Citation

  • Zhang, Michael PhD & Gao, Hang PhD & Chen, Di & Qi, Yanlin, 2024. "A Data-Driven Approach to Manage High-Occupancy Toll Lanes in California," Institute of Transportation Studies, Working Paper Series qt71d0h6hz, Institute of Transportation Studies, UC Davis.
    • Handle: RePEc:cdl:itsdav:qt71d0h6hz
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    References listed on IDEAS

    as
    1. Ling Shen & Jian Lu & Dongdong Geng & Ling Deng, 2020. "Peak Traffic Flow Predictions: Exploiting Toll Data from Large Expressway Networks," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
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    More about this item

    Keywords

    Engineering; High occupancy toll lanes; traffic flow; traffic models; highway traffic control systems;
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