基于强化学习算法的公交信号优先策略

综合分析了影响城市公共交通系统运行的多种因素,提出了一种新型的基于强化学习算法的城市公交信号优先控制策略.该策略利用强化学习算法的试错-改进机制,根据不同交通环境下信号控制策略实施后反馈的结果,迭代优化路口的公交信号优先控制策略,从而使其具备了自学习的能力.基于Paramics的仿真实验表明,该算法能够在保障路口正常交通秩序的同时,显著提高公交车运行效率.

Transit Signal Priority Strategy Based on Reinforcement Learning Algorithm

Factors affecting public transit system were synthetically analyzed. An innovative transit signal priority (TSP) strategy based on reinforcement learning algorithm was proposed. The trial and error mechanism of reinforcement learning were utilized, so the signal plans could be optimized iteratively by implementing them and estimating the rewards. The proposed idea made the TSP strategy have a capability of self-learning. Based on the software of Paramics, simulations were carried out. And the results demonstrated that the proposed TSP strategy could not only improve the efficiency of transit operation, but also reduce the impacts on general traffic at signalized intersections.