Methods, systems, and processor-readable media for adaptive cycle-level traffic signal control are described. An adaptive cycle-level traffic signal controller and control method that operate within a continuous action space. A reinforcement learning algorithm called Proximal Policy Optimization (PPO), which is a type of actor-critic model for reinforcement learning, may be used to generate signal cycle phase durations selected from a continuous range of values. The controller thus does not treat the action space as discrete, but instead produces continuous values as output. The generated phase durations may define a full traffic signal cycle. The inputs to the controller may indicate current and past states of the traffic environment. The average duration of delay of vehicles in the traffic environment may be used to calculate the reward for the reinforcement learning model that drives the behavior of the controller.


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    Titel :

    Method and system for adaptive cycle-level traffic signal control


    Beteiligte:

    Erscheinungsdatum :

    2023-10-10


    Medientyp :

    Patent


    Format :

    Elektronische Ressource


    Sprache :

    Englisch


    Klassifikation :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen



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