Reinforcement Learning Applied to Select Traffic Scheduling Method in Intersections
Abstract: Effective scheduling of traffic is vital for a city to function optimally. For high-density traffic in urban areas, intersections and how they schedule traffic plays an integral part in preventing congestion. Current traffic light scheduling methods predominantly consist of using fixed time intervals to schedule traffic, a method not taking advantage of the technological leaps of recent years. With the unpredictable characteristic of traffic and urban population ever-expanding, conventional traffic scheduling becomes less effective due to them being nonadaptive. Therefore, the study sought out to investigate if a traffic scheduler utilising reinforcement learning could perform better than traditional traffic scheduling policies used today, more specifically fixedinterval scheduling. A solution involving a reinforcement agent choosing different predefined scheduling methods with varied characteristics was implemented. This implementation was successful in lowering the average waiting time of cars passing the intersection compared to fixed-interval scheduling. This was made possible by the agent regularly applying suitable scheduling method for the present traffic conditions. Reinforcement learning could, therefore, be a viable approach to scheduling traffic in intersections. However, the reinforcement agent had a limited overview of the current traffic environment at its disposal which could have consequences for the result.
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