基于分层控制结构的迭代学习城市交通信号控制
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摘要
分层控制是大规模城市路网实施高效信号控制的有效手段。根据城市路网规模庞大且结构复杂的特点,提出了一种基于分层控制结构、协调各子区交通状态的迭代学习城市交通信号控制策略。上层利用交通数据,刻画路网内各子区的宏观基本图(Macroscopic fundamental diagram, MFD),基于MFD分析得到子区车辆累积数与流量的关系,并以道路占有率均衡为目标,设计各子区理想的道路占有率;下层基于道路交通流模型,通过迭代学习获得各路口的信号配时方案,使子区内的道路占有率达到上层的要求。提出的分层控制策略使路网内交通流分布均衡,提高路网整体通行能力。Matlab和Vissim的仿真结果与Webster固定配时信号控制的对比显示了该控制策略的有效性和优越性。
Hierarchical framework is essential for efficient control of large-scale urban traffic network. Under the complex traffic conditions, a two-level hierarchical signal control strategy is proposed to coordinate the traffic of subnetworks. At the upper level, the macroscopic fundamental diagrams(MFD) which reveal the relationship between the vehicle accumulation and outflow of the concerned subnetworks are derived, and the optimal balanced occupancy rate is provided for each subnetwork. At the lower level, based on the dynamic model of the traffic flow, the signal timing plan is designed to satisfy the optimal occupancy rate by using iterative learning technique. By using the aforementioned signal control strategy, the link densities are balanced within the whole network, and such feature of network homogeneity facilitates the improvement of traffic mobility and performances. The applicability of the proposed strategy is evaluated by Vissim and Matlab simulation, its efficiency is also provided by comparing with Webster fixed-time control.
Hierarchical framework is essential for efficient control of large-scale urban traffic network. Under the complex traffic conditions, a two-level hierarchical signal control strategy is proposed to coordinate the traffic of subnetworks. At the upper level, the macroscopic fundamental diagrams(MFD) which reveal the relationship between the vehicle accumulation and outflow of the concerned subnetworks are derived, and the optimal balanced occupancy rate is provided for each subnetwork. At the lower level, based on the dynamic model of the traffic flow, the signal timing plan is designed to satisfy the optimal occupancy rate by using iterative learning technique. By using the aforementioned signal control strategy, the link densities are balanced within the whole network, and such feature of network homogeneity facilitates the improvement of traffic mobility and performances. The applicability of the proposed strategy is evaluated by Vissim and Matlab simulation, its efficiency is also provided by comparing with Webster fixed-time control.
引文
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[2] RAMEZANI M,HADDAD J,GEROLIMINIS N.Dynamics of heterogeneity in urban networks:aggregated traffic modeling and hierarchical control[J].Transportation research part B,2015,74:1-19.
[3] 沈国江,钱晓杰.主干道动态协调控制技术[J].控制与决策,2013,28(12):1907-1911.
[4] 沈国江,陈文峰.方案选择式区域协调控制方法及应用[J].浙江工业大学学报,2016,44(3):237-241.
[5] ZHOU Z,SCHUTTER B D,LIN S,et al.Two-level hierarchical model-based predictive control for large-scale urban traffic networks[J].IEEE transactions on control systems technology,2017,25(2):496-508.
[6] FU H,LIU N,HU G.Hierarchical perimeter control with guaranteed stability for dynamically coupled heterogeneous urban traffic[J].Transportation research part C emerging technologies,2017,83:18-38.
[7] RAMEZANI M,HADDAD J,GEROLIMINIS N.Two-level hierarchical traffic control for heterogeneous urban networks[C]//Proceedings of European Control Conference.Linz,Austria:IEEE,2015:3484-3489.
[8] ZHOU Z,LIN S,XI Y,et al.A hierarchical urban network control with integration of demand balance and traffic signal coordination[J].IFAC-papers online,2016,49(3):31-36.
[9] GEROLIMINIS N,HADDAD J,RAMEZANI M.Optimal perimeter control for two urban regions with macroscopic fundamental diagrams:a model predictive approach[J].IEEE transactions on intelligent transportation systems,2013,14(1):348-359.
[10] ABOUDOLAS K,GEROLIMINIS N.Perimeter and boundary flow control in multi-reservoir heterogeneous networks[J].Transportation research part B methodological,2013,55(9):265-281.
[11] HAJIAHMADI M,HADDAD J,SCHUTTER B D,et al.Optimal hybrid perimeter and switching plans control for urban traffic networks[J].IEEE transactions on control systems technology,2015,23(2):464-478.
[12] GEROLIMINIS N,DAGANZO C F.Existence of urban-scale macroscopic fundamental diagrams:some experimental findings[J].Transportation research part B methodological,2008,42(9):759-770.
[13] 侯忠生,许建新.数据驱动控制理论及方法的回顾和展望[J].自动化学报,2009,35(6):650-667.
[14] 金奎,孙明轩.输入限幅下的迭代学习控制[J].浙江工业大学学报,2011,39(5):579-585.
[15] 孙明轩,余林江.离散时变系统的自适应迭代学习控制[J].浙江工业大学学报,2013,41(1):84-90.
[16] HOU Z,XU J X,ZHONG H.Freeway traffic control using iterative learning control-based ramp metering and speed signaling[J].IEEE transactions on vehicular technology,2007,56(2):466-477.
[17] 侯忠生,金尚泰,赵明.宏观交通流模型参数的迭代学习辨识方法[J].自动化学报,2008,34(1):64-71.
[18] ZHOU Z,LIN S,XI Y.A dynamic network partition method for heterogenous urban traffic networks[C]//Proceedings of International IEEE Conference on Intelligent Transportation Systems.San Diego,USA:IEEE,2012:820-825.
[19] WEBSTER F V.Traffic signal settings[Z].London:Road Research Laboratory,1958.
[20] 闫飞,田福礼,史忠科.城市区域交通信号迭代学习控制策略[J].控制与决策,2015,30(8):1411-1416.