基于强化学习算法的公交信号优先策略  

Transit Signal Priority Strategy Based on Reinforcement Learning Algorithm

作　　者：舒波[1] 李大铭[1] 赵新良[1] 

机构地区：[1]东北大学工商管理学院,辽宁沈阳110819

出　　处：《东北大学学报（自然科学版）》2012年第10期1513-1516,共4页Journal of Northeastern University(Natural Science)

基　　金：辽宁省教育厅人文社会科学基金资助项目(2009JD31)

摘　　要：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.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 th6 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.

关 键 词：公交系统 交通信号控制 公交信号优先 强化学习 回报函数 

分 类 号：U491[交通运输工程—交通运输规划与管理]