基于动态规划的信号配时滚动优化算法
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摘要
随着智能交通技术的发展,交通信息获取的时间颗粒度将越来越小,这为城市动态交通信号配时优化模型和方法提出了新的挑战。为解决经典信号相位控制优化(COP)算法中未考虑交叉口预测区间内交通流量动态变化对信号配时方案控制效果的影响。文中提出了基于动态规划的单交叉口信号配时滚动优化算法。首先,在分析交叉口信号配时关键问题的基础上,构建了以交叉口车辆平均延误和平均排队最小为优化目标,交叉口各相位绿灯时间长度为约束条件的信号配时非线性整数优化模型;并设计了动态规划算法求解该模型。其次,为反映交叉口车流在预测区间内动态变化的特性,在动态规划算法的基础上提出了滚动优化策略,根据实时更新的预测数据滚动优化信号配时方案,并将信号配时方案实时传输到交叉口信号控制器中。最后,通过实际调查数据构建微观仿真环境,采用VISSIM COM二次编程开发技术结合MATLAB编程软件实现了文中模型和算法,并对比分析文中算法和经典的COP算法。通过改变交叉口的输入流量,测试不同流量条件下控制算法的控制效果。结果表明,与经典的COP算法相比,文中算法不仅能够使车辆在交叉口的平均延误减少20%,而且能够保证交叉口各个相位的车辆平均延误的均衡。
With the development of intelligent transport technology,the time granularity of traffic information acquisition is getting smaller and smaller,which presents a new challenge for urban dynamic traffic signal timing optimization models and methods. To solve the problem that the classical controlled optimization of phases( COP) algorithm does not consider the influence of the dynamic change of the traffic flow in the intersection forecasting interval on the signal timing scheme,traffic signal timing rolling optimization algorithm for intersection based on dynamic programming is developed. First,on the basis of the analysis of key problems of signal timing at intersection,regarding the minimization of average vehicle delay and queue length as the optimization objectives and setting the green time duration of each phase as constraint,a signal timing nonlinear integer optimization model is proposed, which is solved by the designed dynamic programming algorithm. Then,to reflect the dynamic change of traffic flow in the intersection forecasting interval,the signal timing scheme is optimized by a rolling optimization strategy according to the real-time updated forecast data,and is transmitted to the intersection signal controller in real time. Finally,the microsimulation environment is constructed based on the actual survey data, the VISSIM COM secondary programming development technology is combined with MATLAB programming software to realize the model and the proposed algorithm,and the classical COP algorithm is compared with the proposed algorithm. By changing the input flow at the intersection,the control effect of the control algorithm under different traffic conditions is tested. The result shows that compared with the classical COP algorithm,the proposed algorithm can reduce the average vehicle delay at intersection by 20%,and can guarantee the equilibrium of average vehicle delay of each phase of the intersection. The results can be used for adaptive control of urban intersection.
With the development of intelligent transport technology,the time granularity of traffic information acquisition is getting smaller and smaller,which presents a new challenge for urban dynamic traffic signal timing optimization models and methods. To solve the problem that the classical controlled optimization of phases( COP) algorithm does not consider the influence of the dynamic change of the traffic flow in the intersection forecasting interval on the signal timing scheme,traffic signal timing rolling optimization algorithm for intersection based on dynamic programming is developed. First,on the basis of the analysis of key problems of signal timing at intersection,regarding the minimization of average vehicle delay and queue length as the optimization objectives and setting the green time duration of each phase as constraint,a signal timing nonlinear integer optimization model is proposed, which is solved by the designed dynamic programming algorithm. Then,to reflect the dynamic change of traffic flow in the intersection forecasting interval,the signal timing scheme is optimized by a rolling optimization strategy according to the real-time updated forecast data,and is transmitted to the intersection signal controller in real time. Finally,the microsimulation environment is constructed based on the actual survey data, the VISSIM COM secondary programming development technology is combined with MATLAB programming software to realize the model and the proposed algorithm,and the classical COP algorithm is compared with the proposed algorithm. By changing the input flow at the intersection,the control effect of the control algorithm under different traffic conditions is tested. The result shows that compared with the classical COP algorithm,the proposed algorithm can reduce the average vehicle delay at intersection by 20%,and can guarantee the equilibrium of average vehicle delay of each phase of the intersection. The results can be used for adaptive control of urban intersection.
引文
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[2]叶宝林.城市路网交通信号协调控制理论与方法研究[D].杭州:浙江大学,2015.YE Bao-lin.Research on Traffic Signal Coordination Control Theories and Methods in Urban Road Networks[D].Hangzhou:Zhejiang University,2015.
[3]姚志洪,蒋阳升,韩鹏,等.基于神经网络的小时间粒度交通流预测模型[J].交通运输系统工程与信息,2017,17(1):67-73.YAO Zhi-hong,JIANG Yang-sheng,HAN Peng,et al.Traffic Flow Prediction Model Based on Neural Network in Small Time Granularity[J].Journal of Transportation Systems Engineering and Information Technology,2017,17(1):67-73.
[4]姚志洪,沈旅欧,巫威眺,等.基于行程时间分布的异质交通流车队离散模型[J].中国公路学报,2016,29(8):134-142,151.YAO Zhi-hong,SHEN Lü-ou,WU Wei-tiao,et al.Heterogeneous Traffic Flow Platoon Dispersion Model Based on Travel Time Distribution[J].China Journal of Highway and Transport,2016,29(8):134-142,151.
[5]SIMS A G,DOBINSON K W.The Sydney Coordinated Adaptive Traffic(SCAT)System Philosophy and Benefits[J].IEEE Transactions on Vehicular Technology,1980,29(2):130-137.
[6]HUNT P B,ROBERTSON D I,BRETHERTON R D,et al.SCOOT:A Traffic Responsive Method of Coordinating Signals,LR 1014 Monograph[R].Crowthorne:Road Research Laboratory,1981.
[7]HENRY J J,FARGES J L,TUFFAL J.The PRODYNReal Time Traffic Algorithm[C]//The 4th IFAC/IFIP/IFORS Conference.Baden-Baden:Elsevier,1984:305-310.
[8]DONATI F,MAURO V,RONCOLINI G,et al.AHierarchical-decentralized Traffic Light Control System.The First Realisation:“Progetto Torino”[J].IFACProceedings Volumes,1984,17(2):2853-2858.
[9]GARTNER N H.OPAC:Strategy for Demand-responsive Decentralized Traffic Signal Control[J].IFACProceedings Volumes,1990,23(2):241-244.
[10]BOILLOT F,MIDENET S,PIERRELE J C.The Realtime Urban Traffic Control System CRONOS:Algorithm and Experiments[J].Transportation Research Part C:Emerging Technologies,2006,14(1):18-38.
[11]MIRCJANDANI P,HEAD L.A Real-time Traffic Signal Control System:Architecture,Algorithms,and Analysis[J].Transportation Research Part C:Emerging Technologies,2001,9(6):415-432.
[12]ASTHANA R,AHUJA N J,DARBAARI M,et al.ACritical Review on the Development of Urban Traffic Models and Control Systems[J].International Journal of Scientific and Engineering Research,2012,3(1):1-6.
[13]SEN S,HEAD K L.Controlled Optimization of Phases at an Intersection[J].Transportation Science,1997,31(1):5-17.
[14]FENG Y,HEAD K L,KHOSHMAGHAM S,et al.AReal-time Adaptive Signal Control in a Connected Vehicle Environment[J].Transportation Research Part C:Emerging Technologies,2015,55:460-473.
[15]PRIEMER C,FRIEDRICH B.A Decentralized Adaptive Traffic Signal Control Using V2I Communication Data[C]//International IEEE Conference on Intelligent Transportation Systems.Louis:IEEE,2009:1-6.
[16]何兆成,曾伟良.变权系数遗传算法在交叉口信号控制中的应用[J].公路交通科技,2011,28(11):121-125.HE Zhao-cheng,ZENG Wei-liang.Genetic Algorithm Based on Variable Weighting Coefficient and Its Application in Traffic Signal Control[J].Journal of Highway and Transportation Research and Development,2011,28(11):121-125.
[17]CEYLAN H,BELL M G H.Traffic Signal Timing Optimisation Based on Genetic Algorithm Approach,Including Drivers’Routing[J].Transportation Research Part B:Methodological,2004,38(4):329-342.
[18]南天伟,敖梦雅,魏丽英.基于模糊神经网络的城市干道信号协调控制[J].公路交通科技,2012,29(1):145-149.NAN Tian-wei,AO Meng-ya,WEI Li-ying.Traffic Signal Coordination for Urban Arterial Roads Based on Fuzzy Neural Network[J].Journal of Highway and Transportation Research and Development,2012,29(1):145-149.
[19]SAITO M,FAN J Z.Artificial Neural Network-based Heuristic Optimal Traffic Signal Timing[J].Computeraided Civil and Infrastructure Engineering,2000,15(4):293-307.
[20]杨文臣,张轮,张孟,等.城市交通信号三级模糊滑动优化控制方法[J].同济大学学报:自然科学版,2014(12):1846-1853,1867.YANG Wen-chen,ZHANG Lun,ZHANG Meng,et al.Rolling-horizon Optimization-based Threestage Fuzzy Logic Controller for Urban Traffic Signals[J].Journal of Tongji University:Natural Science Edition,2014(12):1846-1853,1867.
[21]TRABIA M B,KASEKO M S,ANDE M.A Two-stage Fuzzy Logic Controller for Traffic Signals[J].Transportation Research Part C:Emerging Technologies,1999,7(6):353-367.
[22]URBANIK T,TANAKA A,LOZNER B,et al.Signal Timing Manual[M].Washington,D.C.:Transportation Research Board,2015.