智能交通信息物理融合云控制系统
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摘要
针对现代智能交通信息物理融合路网建设中的对象种类复杂、采集数据量大、传输及计算需求高以及实时调度控制能力弱等问题,基于云控制系统理论,以现代智能交通控制网络为研究对象,设计了智能交通信息物理融合云控制系统方案,包括智能交通边缘控制技术和智能交通网络虚拟化技术.基于智能交通流大数据,在云控制管理中心服务器上利用深度学习和超限学习机等智能学习方法对采集的交通流数据进行训练预测计算,能够预测城市道路的短时交通流和拥堵状况.进一步在云端利用智能优化调度算法得到实时的交通流调控策略,用于解决拥堵路段交通流分配难题,提高智能交通控制系统动态运行性能.仿真结果表明了本文方法的有效性.
Based on the theory of cloud control systems, an intelligent transportation cyber-physical cloud control system is designed due to the problems of complex objects, big data, high demand for transmission and calculation and poor real-time control ability in the modern intelligent transportation cyber-physical network. It includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on the big data of intelligent traffic flow, two intelligent learning methods, deep learning and extreme learning machine, are used to train and predict the traffic flow data on the servers of the cloud control management center. The short time traffic flow and the congestion of roads are predicted accurately. Then the real-time traffic flow control strategy is obtained by intelligent optimization scheduling algorithm in the cloud. The problem of traffic flow distribution in congested roads is solved and the dynamic performance of intelligent transportation control systems can be improved. The simulation results show the effectiveness of the proposed method.
Based on the theory of cloud control systems, an intelligent transportation cyber-physical cloud control system is designed due to the problems of complex objects, big data, high demand for transmission and calculation and poor real-time control ability in the modern intelligent transportation cyber-physical network. It includes intelligent transportation edge control technology and intelligent transportation network virtualization technology. Based on the big data of intelligent traffic flow, two intelligent learning methods, deep learning and extreme learning machine, are used to train and predict the traffic flow data on the servers of the cloud control management center. The short time traffic flow and the congestion of roads are predicted accurately. Then the real-time traffic flow control strategy is obtained by intelligent optimization scheduling algorithm in the cloud. The problem of traffic flow distribution in congested roads is solved and the dynamic performance of intelligent transportation control systems can be improved. The simulation results show the effectiveness of the proposed method.
引文
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2 Xia Y.Cloud control systems.IEEE/CAA Journal of Automatica Sinica,2015,2(2):134-142
3 Xia Yuan-Qing.Cloud control systems and its challenges.Acta Automatica Sinica,2016,42(1):1-12(夏元清.云控制系统及其面临的挑战.自动化学报,2016,42(1):1-12)
4 Xia Y.From networked control systems to cloud control systems.In:Proceedings of the 31st Chinese Control Conference(CCC).Hefei,China,2012.5878-5883
5 Ma Qing-Lu,Si Hai-Lin,Guo Jian-Wei.Cloud control strategy for urban traffic area linkage under the environment of Internet of things.Application Research of Computers,2013,30(9):2711-2714(马庆禄,斯海林,郭建伟.物联网环境下城市交通区域联动的云控制策略.计算机应用研究,2013,30(9):2711-2714)
6 Wang F Y,Zheng N N,Cao D,Martinez C M,Li L,Liu T.Parallel driving in CPSS:a unified approach for transport automation and vehicle intelligence.IEEE/CAA Journal of Automatica Sinica,2017,4(4):577-587
7 Chan K Y,Dillon T S,Singh J,Chang E.Neural-networkbased models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm.IEEE Transactions on Intelligent Transportation Systems,2012,13(2):644-654
8 Meng D,Jia Y.Finite-time consensus for multi-agent systems via terminal feedback iterative learning.IET Control Theory&Applications,2011,5(8):2098-2110
9 Nascimento J C,Silva J G,Marques J S,Lemos J M.Manifold learning for object tracking with multiple nonlinear models.IEEE Transactions on Image Processing,2014,23(4):1593-1604
10 Xue J,Shi Z.Short-time traffic flow prediction based on chaos time series theory.Journal of Transportation Systems Engineering and Information Technology,2014,8(5):68-72
11 Polson N G,Sokolov V O,Deep learning for short-term traffic flow prediction.Transportation Research Part C Emerging Technologies,2017,79,1-17
12 Kumar S V,Traffic flow prediction using Kalman filtering technique.Procedia Engineering,2017,187,582-587
13 Luo Xiang-Long,Jiao Qin-Qin,Niu Li-Yao,Sun ZhuangWen.Short term traffic flow prediction based on deep learning.Application Research of Computers,2017,34(1):91-93(罗向龙,焦琴琴,牛力瑶,孙壮文.基于深度学习的短时交通流预测.计算机应用研究,2017,34(1):91-93)
14 Xu Y,Kong Q,Klette R,Liu Y,Accurate and interpretable bayesian MARS for traffic flow prediction.IEEE Transactions on Intelligent Transportation Systems,2014,15(6):2457-2469
15 Oh S,Kim Y,Hong J,Urban traffic flow prediction system using a multifactor pattern recognition model.IEEE Transactions on Intelligent Transportation Systems,2015,16(5):2744-2755
16 Moretti F,Pizzuti S,Panzieri S,Annunziato M,Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling.Neurocomputing,2015,167(C):3-7
17 Jeong Y S,Byon Y J,Castro-Neto M M,Easa S M,Supervised weighting-online learning algorithm for short-term traffic flow prediction.IEEE Transactions on Intelligent Transportation Systems,2013,14(4):1700-1707
18 Chan K Y,Dillion T S.On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method.IEEE Transactions on Instrumentation and Measurement,2013,62(1):50-59
19 Man Rui-Jun,Liang Xue-Chun.Traffic flow forecasting based on multi-scale wavelet support vector machine.Computer Simulation,2013,30(11):156-159(满瑞君,梁雪春.基于多尺度小波支持向量机的交通流预测.计算机仿真,2013,30(11):156-159)
20 Huang W H,Song G J,Hong H K,Xie K.Deep architecture for traffic flow prediction:deep belief networks with multitask learning.IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201
21 Koesdwiady A,Soua R,Karray F.Improving traffic flow prediction with weather information in connected cars:a deep learning approach.IEEE Transactions on Vehicular Technology,2016,65(12):9508-9517
22 Hinton G,Osindero S,Teh Y.A fast learning algorithm for deep belief nets.Neurocomputing,2006,18(7):1527-1554
23 Kuremoto T,Kimura S,Kobayashi K,Obayashi M.Time series forecasting using a deep belief network with restricted Boltzmann machines.Neurocomputing,2014,137(15):47-56
24 Tan Juan,Wang Sheng-Chun.Traffic congestion prediction model based on deep learning.Application Research of Computers,2015,32(10):2951-2954(谭娟,王胜春.基于深度学习的交通拥堵预测模型研究.计算机应用研究,2015,32(10):2951-2954)
25 Lv Y,Duan Y,Wang W,Li Z,Wang F,Traffic flow prediction with big data:a deep learning approach.IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873
26 Huang G B,Chen L,Siew C K.Universal approximation using incremental constructive feedforward networks with random hidden nodes.IEEE Transactions on Neural Networks,2006,17(4):879-892
27 Huang G B,Zhou H,Ding X,Zhang R.Extreme learning machine for regression and multiclass classification.IEEETransactions on Systems Man and Cybernetics,2012,42(2):513-529
28 Huang W H,Song G J,Hong H K,Xie K.Deep architecture for traffic flow prediction:deep belief networks with multitask learning.IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201
29 Xia Y,Qin Y,Zhai D H,Chai S.Further results on cloud control systems.Science China Information Sciences,2016,59(7):073201
30 Xia Yuan-Qing,Mahmoud M S,Li Hui-Fang,Zhang JinHui.Interaction between control and computation theory:cloud control.Journal of Command and Control,2017,3(2):99-118(夏元清,Mahmoud M S,李慧芳,张金会.控制与计算理论的交互:云控制.指挥与控制学报,2017,3(2):99-118)
31 Kang D,Lv Y,Chen Y.Short-term traffic flow prediction with LSTM recurrent neural network.In:Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems.Yokohama,Japan:IEEE,2017.1-6
2 Xia Y.Cloud control systems.IEEE/CAA Journal of Automatica Sinica,2015,2(2):134-142
3 Xia Yuan-Qing.Cloud control systems and its challenges.Acta Automatica Sinica,2016,42(1):1-12(夏元清.云控制系统及其面临的挑战.自动化学报,2016,42(1):1-12)
4 Xia Y.From networked control systems to cloud control systems.In:Proceedings of the 31st Chinese Control Conference(CCC).Hefei,China,2012.5878-5883
5 Ma Qing-Lu,Si Hai-Lin,Guo Jian-Wei.Cloud control strategy for urban traffic area linkage under the environment of Internet of things.Application Research of Computers,2013,30(9):2711-2714(马庆禄,斯海林,郭建伟.物联网环境下城市交通区域联动的云控制策略.计算机应用研究,2013,30(9):2711-2714)
6 Wang F Y,Zheng N N,Cao D,Martinez C M,Li L,Liu T.Parallel driving in CPSS:a unified approach for transport automation and vehicle intelligence.IEEE/CAA Journal of Automatica Sinica,2017,4(4):577-587
7 Chan K Y,Dillon T S,Singh J,Chang E.Neural-networkbased models for short-term traffic flow forecasting using a hybrid exponential smoothing and Levenberg-Marquardt algorithm.IEEE Transactions on Intelligent Transportation Systems,2012,13(2):644-654
8 Meng D,Jia Y.Finite-time consensus for multi-agent systems via terminal feedback iterative learning.IET Control Theory&Applications,2011,5(8):2098-2110
9 Nascimento J C,Silva J G,Marques J S,Lemos J M.Manifold learning for object tracking with multiple nonlinear models.IEEE Transactions on Image Processing,2014,23(4):1593-1604
10 Xue J,Shi Z.Short-time traffic flow prediction based on chaos time series theory.Journal of Transportation Systems Engineering and Information Technology,2014,8(5):68-72
11 Polson N G,Sokolov V O,Deep learning for short-term traffic flow prediction.Transportation Research Part C Emerging Technologies,2017,79,1-17
12 Kumar S V,Traffic flow prediction using Kalman filtering technique.Procedia Engineering,2017,187,582-587
13 Luo Xiang-Long,Jiao Qin-Qin,Niu Li-Yao,Sun ZhuangWen.Short term traffic flow prediction based on deep learning.Application Research of Computers,2017,34(1):91-93(罗向龙,焦琴琴,牛力瑶,孙壮文.基于深度学习的短时交通流预测.计算机应用研究,2017,34(1):91-93)
14 Xu Y,Kong Q,Klette R,Liu Y,Accurate and interpretable bayesian MARS for traffic flow prediction.IEEE Transactions on Intelligent Transportation Systems,2014,15(6):2457-2469
15 Oh S,Kim Y,Hong J,Urban traffic flow prediction system using a multifactor pattern recognition model.IEEE Transactions on Intelligent Transportation Systems,2015,16(5):2744-2755
16 Moretti F,Pizzuti S,Panzieri S,Annunziato M,Urban traffic flow forecasting through statistical and neural network bagging ensemble hybrid modeling.Neurocomputing,2015,167(C):3-7
17 Jeong Y S,Byon Y J,Castro-Neto M M,Easa S M,Supervised weighting-online learning algorithm for short-term traffic flow prediction.IEEE Transactions on Intelligent Transportation Systems,2013,14(4):1700-1707
18 Chan K Y,Dillion T S.On-road sensor configuration design for traffic flow prediction using fuzzy neural networks and taguchi method.IEEE Transactions on Instrumentation and Measurement,2013,62(1):50-59
19 Man Rui-Jun,Liang Xue-Chun.Traffic flow forecasting based on multi-scale wavelet support vector machine.Computer Simulation,2013,30(11):156-159(满瑞君,梁雪春.基于多尺度小波支持向量机的交通流预测.计算机仿真,2013,30(11):156-159)
20 Huang W H,Song G J,Hong H K,Xie K.Deep architecture for traffic flow prediction:deep belief networks with multitask learning.IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201
21 Koesdwiady A,Soua R,Karray F.Improving traffic flow prediction with weather information in connected cars:a deep learning approach.IEEE Transactions on Vehicular Technology,2016,65(12):9508-9517
22 Hinton G,Osindero S,Teh Y.A fast learning algorithm for deep belief nets.Neurocomputing,2006,18(7):1527-1554
23 Kuremoto T,Kimura S,Kobayashi K,Obayashi M.Time series forecasting using a deep belief network with restricted Boltzmann machines.Neurocomputing,2014,137(15):47-56
24 Tan Juan,Wang Sheng-Chun.Traffic congestion prediction model based on deep learning.Application Research of Computers,2015,32(10):2951-2954(谭娟,王胜春.基于深度学习的交通拥堵预测模型研究.计算机应用研究,2015,32(10):2951-2954)
25 Lv Y,Duan Y,Wang W,Li Z,Wang F,Traffic flow prediction with big data:a deep learning approach.IEEE Transactions on Intelligent Transportation Systems,2015,16(2):865-873
26 Huang G B,Chen L,Siew C K.Universal approximation using incremental constructive feedforward networks with random hidden nodes.IEEE Transactions on Neural Networks,2006,17(4):879-892
27 Huang G B,Zhou H,Ding X,Zhang R.Extreme learning machine for regression and multiclass classification.IEEETransactions on Systems Man and Cybernetics,2012,42(2):513-529
28 Huang W H,Song G J,Hong H K,Xie K.Deep architecture for traffic flow prediction:deep belief networks with multitask learning.IEEE Transactions on Intelligent Transportation Systems,2014,15(5):2191-2201
29 Xia Y,Qin Y,Zhai D H,Chai S.Further results on cloud control systems.Science China Information Sciences,2016,59(7):073201
30 Xia Yuan-Qing,Mahmoud M S,Li Hui-Fang,Zhang JinHui.Interaction between control and computation theory:cloud control.Journal of Command and Control,2017,3(2):99-118(夏元清,Mahmoud M S,李慧芳,张金会.控制与计算理论的交互:云控制.指挥与控制学报,2017,3(2):99-118)
31 Kang D,Lv Y,Chen Y.Short-term traffic flow prediction with LSTM recurrent neural network.In:Proceedings of the 2017 IEEE 20th International Conference on Intelligent Transportation Systems.Yokohama,Japan:IEEE,2017.1-6