Road Artery Traffic Light Optimization with Use of Reinforcement Learning
نویسندگان
چکیده
The basic principle of optimal traffic control is the appropriate real-time response to dynamic traffic flow changes. Signal plan efficiency depends on a large number of input parameters. An actuated signal system can adjust very well to traffic conditions, but cannot fully adjust to stochastic traffic volume oscillation. Due to the complexity of the problem analytical methods are not applicable for use in real time, therefore the purpose of this paper is to introduce heuristic method suitable for traffic light optimization in real time. With the evolution of artificial intelligence new possibilities for solving complex problems have been introduced. The goal of this paper is to demonstrate that the use of the Q learning algorithm for traffic lights optimization is suitable. The Q learning algorithm was verified on a road artery with three intersections. For estimation of the effectiveness and efficiency of the proposed algorithm comparison with an actuated signal plan was carried out. The results (average delay per vehicle and the number of vehicles that left road network) show that Q learning algorithm outperforms the actuated signal controllers. The proposed algorithm converges to the minimal delay per vehicle regardless of the stochastic nature of traffic. In this research the impact of the model parameters (learning rate, exploration rate, influence of communication between agents and reward type) on algorithm effectiveness were analysed as well.
منابع مشابه
Project Design and Organization of Autonomous Systems : Intelligent Traffic Light Control
Because of the increasing density of the traffic flow in urban areas there is a need for optimal performance of traffic lights. In this paper we will describe an existing approach of reinforcement learning applied to the optimization of traffic light configurations, and introduce a new approach. Our approach uses implicit cooperation between traffic lights, letting cars take into account the tr...
متن کاملTraffic Light Control by Multiagent Reinforcement Learning Systems
Traffic light control is one of the main means of controlling road traffic. Improving traffic control is important because it can lead to higher traffic throughput and reduced congestion. This chapter describes multiagent reinforcement learning techniques for automatic optimization of traffic light controllers. Such techniques are attractive because they can automatically discover efficient con...
متن کاملNatural Actor-Critic for Road Traffic Optimisation
Current road-traffic optimisation practice around the world is a combination of hand tuned policies with a small degree of automatic adaption. Even state-ofthe-art research controllers need good models of the road traffic, which cannot be obtained directly from existing sensors. We use a policy-gradient reinforcement learning approach to directly optimise the traffic signals, mapping currently ...
متن کاملReinforcement Learning for Traffic Optimization
In this paper we apply reinforcement learning techniques to traffic light policies with the aim of increasing traffic flow through intersections. We model intersections with states, actions, and rewards, then use an industry-standard software platform to simulate and evaluate different policies against them. We compare various policies including fixed cycles, longest queue first (LQF), and the ...
متن کاملDoas 2006 Project: Reinforcement Learning of Traffic Light Controllers Adapting to Accidents
Last year we started a project concerned with intelligent traffic control. Using a simulator that models urban road traffic, we developed an improved traffic light controller based on measuring traffic congestion on the roads and reinforcement learning. This year an important focus will be on dealing with traffic accidents. In this student project we want to investigate a learning traffic contr...
متن کامل