Abstract
This paper proposes a decentralized traffic light control system in a multi-agent framework. Each signal controller at an intersection is modeled as an intelligent agent capable of making actions for signal operations according to received detection information. The controller agent works with a turning movement based phasing scheme. Duration of turning movement is determined by a multi-criteria reinforcement learning algorithm. In the design of agent, both traffic mobility and energy efficiency are taken into account. Then, a case study is carried out to assess the performance of the proposed decentralized signal control system. The simulation results outperforms an optimized vehicle-actuated control system by reducing average travel delay and average fuel consumption for vehicles. In particular, the decentralized control system is queue responsive and able to adapt to demand in its green time allocation.
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