基于上下文多摇臂赌博机的交通信号控制算法  

Traffic Signal Control Algorithm Based on Contextual Multi-armed Bandit

作　　者：邵俊杰 肖明军[1,2] SHAO Jun-Jie;XIAO Ming-Jun(School of Computer Science and Technology,University of Science and Technology of China,Hefei 230027,China;Suzhou Institute for Advanced Research,University of Science and Technology of China,Suzhou 215127,China)

机构地区：[1]中国科学技术大学计算机科学与技术学院,合肥230027 [2]中国科学技术大学苏州高等研究院,苏州215127

出　　处：《计算机系统应用》2024年第10期183-189,共7页Computer Systems & Applications

基　　金：国家自然科学基金面上项目(62172386);江苏省自然科学基金面上项目(BK20231212)。

摘　　要：近年来,由于交通拥堵问题日益严重,引起了学术界对交通信号灯控制算法研究的广泛关注.现有研究表明,基于深度强化学习(DRL)的方法在模拟环境中表现良好,但在实际应用中存在着数据和计算资源需求大、难以实现路口之间协同等问题.为解决这一问题,本文提出了一种基于上下文多摇臂赌博机的新型交通信号控制算法.与传统方法相比,本文所提算法通过从路网中提取主干道的方式,实现了路口之间的高效协同,并利用上下文多摇臂赌博机模型实现了交通信号的快速、有效控制.最后,通过在真实数据集以及合成数据集上进行充分的实验验证,证明了本文算法相较于过去算法的优越性.In recent years,the exacerbation of traffic congestion has sparked widespread interest in the research on traffic signal control algorithms.Current studies indicate that methods based on deep reinforcement learning(DRL)exhibit promising performance in simulated environments.However,challenges persist in their practical application,including substantial requirements for data and computational resources,as well as difficulties in achieving coordination between intersections.To address these challenges,this study proposes a novel traffic signal control algorithm based on a contextual multi-armed bandit model.In contrast to conventional algorithms,the proposed algorithm achieves efficient coordination between intersections by extracting the main arteries from the road network.Moreover,it employs a contextual multi-armed bandit model to facilitate rapid and effective traffic signal control.Finally,through extensive experimentation on both real and synthetic datasets,the superiority of the proposed algorithm over previous algorithms is empirically demonstrated.

关 键 词：智能交通 强化学习 上下文多臂赌博机 多智能体系统 交通信号控制 

分 类 号：U491.54[交通运输工程—交通运输规划与管理] TP18[交通运输工程—道路与铁道工程]