Signal Priority Control for Trams Using Deep Reinforcement Learning
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摘要: 现有的有轨电车信号优先控制系统存在诸多问题, 如无法适应实时交通变化、优化求解较为复杂等. 本文提出了一种基于深度强化学习的有轨电车信号优先控制策略. 不依赖于交叉口复杂交通建模, 采用实时交通信息作为输入, 在有轨电车整个通行过程中连续动态调整交通信号. 协同考虑有轨电车与社会车辆的通行需求, 在尽量保证有轨电车无需停车的同时, 降低社会车辆的通行延误. 采用深度Q网络算法进行问题求解, 并利用竞争架构、双Q网络和加权样本池改善学习性能. 基于SUMO的实验表明, 该模型能够有效地协同提高有轨电车与社会车辆的通行效率.Abstract: Current trams-priority signal control systems have many problems, such as low adaptability to real-time traffic changes and high complexity in optimization solutions, etc. In this paper, an active signal priority control model is proposed for the trams based on deep reinforcement learning. Considering the traffic demands from tram and general vehicles, it can reduce the traffic delay of general vehicles while minimizing the need for trams to stop at the intersection. Real-time traffic information is used to dynamically adjust the sequence of traffic signals throughout the whole passing process of the tram, without relying on the complex traffic modeling. We use deep Q-network algorithm for problem-solving, and adopt dueling network, double Q network, and prioritized experience replay to improve the learning performance. Experiments based on SUMO have demonstrated that the proposed model can excellently improve the efficiency of trams and general vehicles simultaneously.
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Key words:
- Trams /
- signal priority /
- Markov decision process /
- deep reinforcement learning
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图 7 两种深度强化学习模型下有轨电车平均停车次数对比
Fig. 7 Comparison of tram mean stops under two deep reinforcement learning models
图 8 两种深度强化学习模型下累积奖励对比
Fig. 8 Comparison of cumulative reward under two deep reinforcement learning models
图 9 两种深度强化学习模型下各直行/右转车道平均停车等待时间对比
Fig. 9 Comparison of waiting time in direct/right turn lanes under two deep reinforcement learning models
图 10 两种深度强化学习模型下各左转车道平均停车等待时间对比
Fig. 10 Comparison of waiting time in left turn lanes under two deep reinforcement learning models
表 1 模型参数
Table 1 Model parameters
参数 取值 $N$ 20 000 $m$ 32 $\Delta \varepsilon$ −0.001 $\gamma$ 0.99 $\alpha$ 0.001 
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