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智能体技术在城市交通信号控制系统中应用综述
引用本文:杨文臣,张轮,施弈骋,张孟.智能体技术在城市交通信号控制系统中应用综述[J].武汉理工大学学报(交通科学与工程版),2014(4):709-718.
作者姓名:杨文臣  张轮  施弈骋  张孟
作者单位:同济大学道路与交通工程教育部重点实验室,上海201804
基金项目:国家863计划项目(批准号:2012AA112307); 上海市创新基金项目(批准号:11ZZ27)资助
摘    要:为解决分布式复杂巨系统在动态环境中的不确定性问题,智能体计算技术发展迅速.交通运输系统在物理位置和控制逻辑上分散于动态变化的交通网络环境,非常适合采用智能体方法建模与描述.文中综述了智能体技术在交通信号控制系统中各个领域应用的技术与方法,包括系统架构、控制算法、建模与仿真,以及智能交通集成管理等方面;跟踪了智能体技术在国内外交通控制领域的具体应用,讨论了智能体技术在智能交通信号控制系统中应用的研究动向,提出发展基于多智能体的交通网络信号集成控制系统的关键问题在于系统交互性、自适应性和可拓展性.

关 键 词:智能运输系统  交通信号  智能体  多智能体系统  强化学习

Applications of Agent Technology in Urban Traffic Signal Control Systems:A Survey
YANG Wenchen,ZHANG Lun,SHI Yicheng,ZHANG Meng.Applications of Agent Technology in Urban Traffic Signal Control Systems:A Survey[J].journal of wuhan university of technology(transportation science&engineering),2014(4):709-718.
Authors:YANG Wenchen  ZHANG Lun  SHI Yicheng  ZHANG Meng
Institution:(Key Laboratory of Road and Traffic Engineering, Ministry of Education, Tongji University, Shanghai 201804, China)
Abstract:The agent computing is rapidly emerging as one of the powerful technologies to deal with the uncertainty of large-scale distributed systems in a dynamic environment.The domain of transportation systems is well suited for an agent-based approach because of their geographical and logical distribution in dynamic changing environments.This paper has surveyed the techniques and methods resulting from the field of agent and multi-agent systems that have been applied to many aspects of traffic control systems,including system architecture,control algorithm,modeling and simulation,and intelligent traffic management.Then,an agent-based approach and its applications in the domain of traffic control,both in China and abroad,are also examined.Finally,several future research directions toward the successful deployment of agent technology in traffic control systems are discussed,and there critical issues in developing agent-based traffic control systems for integrated network are addressed as interoperability,adaptability,and extendibility.
Keywords:intelligent transportation system  traffic signal  agent  multi-agent system  reinforcement learning
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