基于车队离散模型的分布式多智能体信号协调控制算法(英文)

A distributed algorithm for signal coordination of multiple agents with embedded platoon dispersion model

为减少干线协调交叉口的车辆延误,基于多智能体系统与增强学习算法(RL)建立了一种新的分布式交通信号协调算法.增强学习算法通过训练各个智能体与外界环境的交互能力达到减少车辆延误的目的.为更精确地描述干线上的车队运动规律,引入了罗伯逊车队离散模型,并基于该模型以及HCM2000中的干线交叉口车流延误计算公式建立了RL中的回报函数.在Matlab中仿真验证了所建算法的控制效果,并在3种不同交通负荷下与传统信号协调算法进行对比.结果表明,该算法较传统算法能有效降低干线车流延误;并且随着干线饱和度的增加降低幅度逐渐增大.该结果验证了所建算法的可行性与有效性.

In order to reduce average arterial vehicle delay, a novel distributed and coordinated traffic control algorithm is developed using the multiple agent system and the reinforce learning （RL）. The RL is used to minimize average delay of arterial vehicles by training the interaction ability between agents and exterior environments. The Robertson platoon dispersion model is embedded in the RL algorithm to precisely predict platoon movements on arteries and then the reward function is developed based on the dispersion model and delay equations cited by HCM2000. The performance of the algorithm is evaluated in a Matlab environment and comparisons between the algorithm and the conventional coordination algorithm are conducted in three different traffic load scenarios. Results show that the proposed algorithm outperforms the conventional algorithm in all the scenarios. Moreover, with the increase in saturation degree, the performance is improved more significantly. The results verify the feasibility and efficiency of the established algorithm.