基于CPS的单交叉口信号滚动优化控制模型OA北大核心CSTPCD
CPS-based rolling optimization control model for single intersection signal
为了采用现代信息技术提升单交叉口的通行效率,综合考虑信息物理系统(CPS)的特性和功能,构建了基于CPS的单交叉口信号滚动优化控制架构;提出了交叉口信号优化控制的分层建模思路与框架,实现了对交叉口不同层级的状态描述;针对不同流量环境,分别以单位时间车均预计延误和加权排队长度比作为优化目标,进行实时信号优化,构建了基于CPS的分层滚动优化模型;搭建了基于SUMO与Python的实时交互仿真环境,针对未饱和、过饱和以及饱和状态变化3种流量场景进行案例分析.算例结果表明,与Transyt、Synchro、Webster、感应控制、承载力方法相比,所提模型均取得了更优的控制效果,车均延误和平均排队长度最多分别可减少43.75%和16.05%,溢流时间最多可延长37.93%.
To improve the traffic efficiency at single intersections using modern information technology,a roll-ing optimization control architecture for the single intersection signal was developed based on the cyber-physi-cal systems(CPS)by comprehensively considering the characteristics and functions of the CPS.A layered modeling approach and framework for optimizing signal control at intersections was proposed to describe the intersection states at different levels.A CPS-based layered rolling optimization model was constructed for real-time signal optimization in different traffic environments using the expected average delay per unit time for ve-hicles and the weighted queue length ratio as the optimization objectives,respectively.A real-time interactive simulation environment based on SUMO and Python was established for conducting case studies on three traffic scenarios including unsaturated,oversaturated,and varying saturation states.The results show that compared with Transyt,Synchro,Webster,induction control,and traffic carrying capacity methods,the proposed model achieves better control effects.The average vehicle delay and the average queue length can be decreased by 43.75%and 16.05%at most,respectively.The overflow time can be extended by 37.93%at most.
卢凯;陈恒宇;江书妍;李余;樊舒颖;李林
华南理工大学土木与交通学院,广州 510640||东南大学现代城市交通技术江苏高校协同创新中心,南京 211189||人工智能与数字经济广东省实验室(广州),广州 510330华南理工大学土木与交通学院,广州 510640深圳市城市交通规划设计研究中心股份有限公司,深圳 518000
交通运输
交通工程信息物理系统信号交叉口滚动优化分层建模
traffic engineeringcyber-physical systemssignal intersectionrolling optimizationlayered modeling
《东南大学学报(自然科学版)》 2024 (002)
379-388 / 10
国家自然科学基金面上资助项目(52172326,61773168)、广东省基础与应用基础研究基金资助项目(2020B1515120095)、广州市重点研发计划重点专项资助项目(202103050002).
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