兼顾管理者需求和用户可接受性的交通诱导系统MPC设计
|
摘要
针对交通诱导系统中可变信息标志(VMS)的控制策略设计,该文在宏观Metanet交通流模型的基础上,研究了VMS诱导控制信号对驾驶员路径选择行为的影响,建立了路网诱导控制的状态方程模型。在模型预测控制(MPC)框架下设计了VMS诱导控制策略,以优化路网所有车辆的总旅行时间和VMS切换性能为目标函数,通过对诱导控制变量引入用户可接受约束,有效兼顾了交通管理者对系统最优的控制需求和交通用户对VMS推荐路径的可接受性。最后设计了基于禁忌搜索的双层并行求解算法对MPC诱导策略进行求解,利用并行计算保证MPC控制的实时性要求。仿真结果表明,该文设计的诱导控制策略能缓解路网拥挤,提高路网通行能力,对系统随机扰动具有较好的抗干扰能力。与基于系统最优的诱导策略和基于用户均衡的诱导策略相比,该控制策略不仅能满足管理者的控制需求,而且能均衡路网车流分布,减少交通用户的延误时间,提高用户对交通诱导系统的满意度。
In order to design the variable message sign( VMS) control strategy for the traffic guidance system,the influence of the VMS control signal on the driver route-choice behaviour is discussed based on the macroscopic Metanet traffic flow model,and the state-space model for the network routing control is established.Then the VMS control strategy is designed in the model predictive control( MPC) framework,in which the total time spent( TTS) of all the vehicles and the switching performance of the VMS signal are considered as the objective function,and the user acceptance with the VMS route recommendation is introduced as the constraint.Both the requirements of the system optimal from traffic management and the user acceptance with the recommended route from the VMS are taken into account.Finally,the two loop searching algorithm based on the tabu search( TS) in the par-allel computing( PC) is designed to solve the MPC routing control strategy,and the parallel computing is utilized to guarantee the real-time implementation of MPC control.The simulation results show that the proposed routing control strategy can relieve congestion,improve network capacity,and perform anti-jamming to system stochastic disturbances.Compared to the system-optimal-based routing strategy and the user-equilibrium-based routing strategy,the proposed strategy can meet the requirements of traffic management,balance the network flow distribution,decrease the user's total time delay,and improve the user's satisfaction with the route guidance system.
In order to design the variable message sign( VMS) control strategy for the traffic guidance system,the influence of the VMS control signal on the driver route-choice behaviour is discussed based on the macroscopic Metanet traffic flow model,and the state-space model for the network routing control is established.Then the VMS control strategy is designed in the model predictive control( MPC) framework,in which the total time spent( TTS) of all the vehicles and the switching performance of the VMS signal are considered as the objective function,and the user acceptance with the VMS route recommendation is introduced as the constraint.Both the requirements of the system optimal from traffic management and the user acceptance with the recommended route from the VMS are taken into account.Finally,the two loop searching algorithm based on the tabu search( TS) in the par-allel computing( PC) is designed to solve the MPC routing control strategy,and the parallel computing is utilized to guarantee the real-time implementation of MPC control.The simulation results show that the proposed routing control strategy can relieve congestion,improve network capacity,and perform anti-jamming to system stochastic disturbances.Compared to the system-optimal-based routing strategy and the user-equilibrium-based routing strategy,the proposed strategy can meet the requirements of traffic management,balance the network flow distribution,decrease the user's total time delay,and improve the user's satisfaction with the route guidance system.
引文
[1]杨兆升.城市道路交通系统智能协同理论与实施方法[M].北京:中国铁道出版社,2002.
[2]Mammar S,Messmer A,Jensen P,et al.Automatic control of variable message signs in Aalborg[J].Transportation Research Part C-Emerging Technologies,1996,4(3):131-150.
[3]Baskar L D,Schutter B D,Hellendoorn H.Optimal routing for automated highway systems[J].Transportation Research Part C-Emerging Technologies,2013,30:1-22.
[4]Jindahra P,Choocharukul K.Shortrun diversion:A empirical investigation into variable message sign design and policy experiments[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(1):388
[5]Zhou Shiquan,Zhou Lizhen,Ma Shoufeng,et al.Guidance compliance behaviors of drivers under different information release modes on VMS[J].Information Sciences,2014,289:117-132.
[6]Kotsialos A,Papageorgiou M,Mangeas M,et al.Coordinated and integrated control of motorway networks via non-linear optimal control[J].Transportation Research Part C-Emerging Technologies,2002,10(1):65-84.
[7]Wang Y B,Papageorgiou M,Messmer A.Predictive feedback routing control strategy for freeway network traffic[J].Journal of the Transportation Research Record,2003,1856(1):62-73.
[8]周永华.交通诱导预测控制仿真研究[J].控制与决策,2009,24(12):1869-1872.Zhou Yonghua.Study on the simulation of traffic navigation predictive control[J].Control and Decision,2009,24(12):1869-1872.
[9]Xu T D,Sun L J,Peng Z R,et al.Integrated route guidance and ramp metering consistent with drivers’enroute diversion behavior[J].IET Intelligent Transport Systems,2011,5(4):267-276.
[10]He Zhengbing,Guan Wei,Ma Shoufeng.A traffic-condition-based route guidance strategy for a single destination road network[J].Transportation Research Part C-Emerging Technologies,2013,32:89-102.
[11]Spiliopoulou A,Kontorinaki M,Papamichai L I,et al.Real-time route diversion control at congested off-ramp areas-Part II Route guidance versus off-ramp closure[J].Procedia-Social and Behavioral Sciences,2014,111(5):1102-1111.
[12]Messmer A,Papageorgiou M.Route diversion control in motorway networks via nonlinear optimization[J].IEEE Transactions on Control Systems Technology,1995,3(1):144-154.
[13]Paz A,Peeta S.Behavior-consistent real-time traffic routing under information provision[J].Transportation Research Part C-Emerging Technologies,2009,17(6):642-661.
[14]孙伟力,李平,王慧.高速路网可变信息标志诱导的最优开关控制设计[J].上海交通大学学报,2011,45(8):1246-1250.Sun Weili,Li Ping,Wang Hui.Optimal on-off control of VMS for highway route guidance[J].Journal of Shanghai Jiaotong University,2011,45(8):1246-1250.
[15]马寿峰,卜军峰,张安训.交通诱导中系统最优与用户最优的博弈协调[J].系统工程学报,2006,20(1):30-37.Ma Shoufeng,Bu Junfeng,Zhang Anxun.Game-based coordination method between system optimum and user equilibrium in route guidance system[J].Journal of System Engineering,2006,20(1):30-37.
[16]丁宝苍,预测控制的理论与方法[M].北京:机械工业出版社,2008.
[17]李少远.工业过程系统的预测控制[J].控制工程,2010,17(4):407-414.Li Shaoyuan.Model-based predictive control for industrial process-A survey[J].Control Engineering of China,2010,17(4):407-414.
[18]Mayne D Q,Rawlings J B,Rao C V,et al.Constrained model predictive control:Stability and optimality[J].Automatic,2000,36(6):789-814.
[19]Frejo J R D,Camacho E F,Horowitz R.A parameter identification algorithm for the Metanet model with a limited number of loop detectors[C]//IEEE Conference on Decision and Control.Maui,Hawaii,US:IEEE,2012:6983-6988.
[20]Spiliopoulou A,Papamichail I,Papageorgiou M,et al.Macroscopic traffic flow model calibration using different optimization algorithms[J].Transportation Research Procedia,2015,6:144-157.
[21]Fontes F A C C.A general framework to design stabilizing nonlinear model predictive controllers[J].Systems&Control Letters,2001,42(2):127-143.
[22]Jadbabaie A,Hauser J.On the stability of receding horizon control with a general terminal cost[J].IEEE Transactions on Automatic Control,2005,50(5):674-678.
[23]Diehl M,Amrit R,Rawlings J B.A Lyapunov function for economic optimizing model predictive control[J].IEEE Transactions on Automatic Control,2011,56(3):703-707.
[24]Zheng Tao.Model predictive control[M].Rejeka,Croatia:Sciyo,2010.
[25]Taillard E,Badeau P,Gendreau M.A tabu search heuristic for the vehicle routing problem with soft time windows[J].Transportation Science,1997,31(2):170-186.
[26]刘光远,贺一,温万惠.禁忌搜索算法及应用[M].北京:科学出版社,2014.
[27]Luo L H,Ge Y E,Zhang F W,et al.Real-time route diversion control in a model predictive control framework with multiple objectives:Traffic efficiency,emission reduction and fuel economy[J].Transportation Research Part D:Transport and Environment,2016,48:332-356.
[2]Mammar S,Messmer A,Jensen P,et al.Automatic control of variable message signs in Aalborg[J].Transportation Research Part C-Emerging Technologies,1996,4(3):131-150.
[3]Baskar L D,Schutter B D,Hellendoorn H.Optimal routing for automated highway systems[J].Transportation Research Part C-Emerging Technologies,2013,30:1-22.
[4]Jindahra P,Choocharukul K.Shortrun diversion:A empirical investigation into variable message sign design and policy experiments[J].IEEE Transactions on Intelligent Transportation Systems,2013,14(1):388
[5]Zhou Shiquan,Zhou Lizhen,Ma Shoufeng,et al.Guidance compliance behaviors of drivers under different information release modes on VMS[J].Information Sciences,2014,289:117-132.
[6]Kotsialos A,Papageorgiou M,Mangeas M,et al.Coordinated and integrated control of motorway networks via non-linear optimal control[J].Transportation Research Part C-Emerging Technologies,2002,10(1):65-84.
[7]Wang Y B,Papageorgiou M,Messmer A.Predictive feedback routing control strategy for freeway network traffic[J].Journal of the Transportation Research Record,2003,1856(1):62-73.
[8]周永华.交通诱导预测控制仿真研究[J].控制与决策,2009,24(12):1869-1872.Zhou Yonghua.Study on the simulation of traffic navigation predictive control[J].Control and Decision,2009,24(12):1869-1872.
[9]Xu T D,Sun L J,Peng Z R,et al.Integrated route guidance and ramp metering consistent with drivers’enroute diversion behavior[J].IET Intelligent Transport Systems,2011,5(4):267-276.
[10]He Zhengbing,Guan Wei,Ma Shoufeng.A traffic-condition-based route guidance strategy for a single destination road network[J].Transportation Research Part C-Emerging Technologies,2013,32:89-102.
[11]Spiliopoulou A,Kontorinaki M,Papamichai L I,et al.Real-time route diversion control at congested off-ramp areas-Part II Route guidance versus off-ramp closure[J].Procedia-Social and Behavioral Sciences,2014,111(5):1102-1111.
[12]Messmer A,Papageorgiou M.Route diversion control in motorway networks via nonlinear optimization[J].IEEE Transactions on Control Systems Technology,1995,3(1):144-154.
[13]Paz A,Peeta S.Behavior-consistent real-time traffic routing under information provision[J].Transportation Research Part C-Emerging Technologies,2009,17(6):642-661.
[14]孙伟力,李平,王慧.高速路网可变信息标志诱导的最优开关控制设计[J].上海交通大学学报,2011,45(8):1246-1250.Sun Weili,Li Ping,Wang Hui.Optimal on-off control of VMS for highway route guidance[J].Journal of Shanghai Jiaotong University,2011,45(8):1246-1250.
[15]马寿峰,卜军峰,张安训.交通诱导中系统最优与用户最优的博弈协调[J].系统工程学报,2006,20(1):30-37.Ma Shoufeng,Bu Junfeng,Zhang Anxun.Game-based coordination method between system optimum and user equilibrium in route guidance system[J].Journal of System Engineering,2006,20(1):30-37.
[16]丁宝苍,预测控制的理论与方法[M].北京:机械工业出版社,2008.
[17]李少远.工业过程系统的预测控制[J].控制工程,2010,17(4):407-414.Li Shaoyuan.Model-based predictive control for industrial process-A survey[J].Control Engineering of China,2010,17(4):407-414.
[18]Mayne D Q,Rawlings J B,Rao C V,et al.Constrained model predictive control:Stability and optimality[J].Automatic,2000,36(6):789-814.
[19]Frejo J R D,Camacho E F,Horowitz R.A parameter identification algorithm for the Metanet model with a limited number of loop detectors[C]//IEEE Conference on Decision and Control.Maui,Hawaii,US:IEEE,2012:6983-6988.
[20]Spiliopoulou A,Papamichail I,Papageorgiou M,et al.Macroscopic traffic flow model calibration using different optimization algorithms[J].Transportation Research Procedia,2015,6:144-157.
[21]Fontes F A C C.A general framework to design stabilizing nonlinear model predictive controllers[J].Systems&Control Letters,2001,42(2):127-143.
[22]Jadbabaie A,Hauser J.On the stability of receding horizon control with a general terminal cost[J].IEEE Transactions on Automatic Control,2005,50(5):674-678.
[23]Diehl M,Amrit R,Rawlings J B.A Lyapunov function for economic optimizing model predictive control[J].IEEE Transactions on Automatic Control,2011,56(3):703-707.
[24]Zheng Tao.Model predictive control[M].Rejeka,Croatia:Sciyo,2010.
[25]Taillard E,Badeau P,Gendreau M.A tabu search heuristic for the vehicle routing problem with soft time windows[J].Transportation Science,1997,31(2):170-186.
[26]刘光远,贺一,温万惠.禁忌搜索算法及应用[M].北京:科学出版社,2014.
[27]Luo L H,Ge Y E,Zhang F W,et al.Real-time route diversion control in a model predictive control framework with multiple objectives:Traffic efficiency,emission reduction and fuel economy[J].Transportation Research Part D:Transport and Environment,2016,48:332-356.