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首页> 外文会议>Mediterranean Conference on Control and Automation >Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows
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Reinforcement Learning Based Variable Speed Limit Control for Mixed Traffic Flows

机译：基于加固基于学习的混合交通流量的变速限制控制

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摘要

Today’s urban mobility requires results for resolving increasingly complex demands on the traffic management system. Hence, the main problem is to achieve a satisfactory level of service for urban motorways as part of the urban traffic network. In addition, with the introduction of Connected and Autonomous Vehicles (CAVs), additional challenges for modern control systems arise. This study focuses on the Variable Speed Limit (VSL) based on Q-Learning with CAVs as actuators in the control loop. The Q-Learning algorithm is combined with the two-step Temporal Difference target to increase the effectiveness of the algorithm for learning the VSL control policy for mixed traffic flows. Different CAV penetration rates are analyzed, and the results are compared with a rule-based VSL and the no control case. The obtained results show that Q-Learning based VSL can learn the control policy and improve the Total Travel Time and Mean Travel Time for different CAV penetration rates. The results are most apparent in the case of low CAV penetration rates. There is also an indication that the increase of the CAV penetration rate reduces the need for separate VSL control.
机译：今天的城市流动需要解决对交通管理系统日益复杂的需求。因此，主要问题是为城市交通网络的一部分实现城市高速公路的令人满意的服务水平。此外，通过引入连接和自主车辆（CAV），出现了现代控制系统的额外挑战。本研究专注于基于Q-Learning的可变速度限制（VSL）与控制回路中的致动器。 Q学习算法与两步时间差异目标组合，以提高算法学习用于学习混合业务流量的VSL控制策略的有效性。分析不同的脉冲速率，并将结果与​​基于规则的VSL和NO控制案例进行比较。所获得的结果表明，基于Q学习的VSL可以学习控制策略，并改善不同脉冲速率的总行程时间和平均行程时间。在低腔渗透率的情况下，结果最明显。还有一个指示腔渗透率的增加会降低对单独的VSL控制的需求。

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