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Hierarchical control for stochastic network traffic with reinforcement learning

Author

Listed:
  • Su, Z.C.
  • Chow, Andy H.F.
  • Fang, C.L.
  • Liang, E.M.
  • Zhong, R.X.

Abstract

This study proposes a hierarchical control framework to maximize the throughput of a road network driven by travel demand with uncertainties. In the upper level, a perimeter controller regulates the traffic influx into the core road network. The upper level uses a reinforcement learning algorithm that learns and responds to the traffic dynamics in the core road network without the need for an underlying system model and macroscopic fundamental diagram. The lower level is a local signal control system that regulates the spatial distribution of traffic flow within the core network. The results show that the hierarchical control framework can improve road network throughput by coordinating control actions conducted at the two levels. The improvement in system-wide performance is validated by a range of performance metrics and macroscopic flow-accumulation patterns realized under different control settings. The study contributes to the management of urban road networks with advanced computing technologies.

Suggested Citation

  • Su, Z.C. & Chow, Andy H.F. & Fang, C.L. & Liang, E.M. & Zhong, R.X., 2023. "Hierarchical control for stochastic network traffic with reinforcement learning," Transportation Research Part B: Methodological, Elsevier, vol. 167(C), pages 196-216.
    • Handle: RePEc:eee:transb:v:167:y:2023:i:c:p:196-216
    DOI: 10.1016/j.trb.2022.12.001
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    References listed on IDEAS

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