智能交通信息物理融合云控制系统

[1]   王中杰, 谢璐璐.信息物理融合系统钻研综述.主动化学报, 2011, 37(10):1157-1166 Wang Zhong-Jie, Xie Lu-Lu. ReZZZiew on information physics fusion system. Acta Automatica Sinica, 2011, 37(10):1157-1166  
[2]   Xia Y. Cloud control systems. IEEE/CAA Journal of Automatica Sinica, 2015, 2(2):134-142 doi: 10.1109/JAS.2015.7081652  
[3]   夏元清.云控制系统及其面临的挑战.主动化学报, 2016, 42(1):1-12 doi: 10.3969/j.issn.1003-8930.2016.01.001Xia Yuan-Qing. Cloud control systems and its challenges. Acta Automatica Sinica, 2016, 42(1):1-12 doi: 10.3969/j.issn.1003-8930.2016.01.001  
[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]   马庆禄, 斯海林, 郭建伟.物联网环境下都市交通区域联动的云控制战略.计较机使用钻研, 2013, 30(9):2711-2714 doi: 10.3969/j.issn.1001-3695.2013.09.038Ma Qing-Lu, Si Hai-Lin, Guo Jian-Wei. Cloud control strategy for urban traffic area linkage under the enZZZironment of Internet of things. Application Research of Computers, 2013, 30(9):2711-2714 doi: 10.3969/j.issn.1001-3695.2013.09.038  
[6]   Wang F Y, Zheng N N, Cao D, Martinez C M, Li L, Liu T. Parallel driZZZing in CPSS:a unified approach for transport automation and ZZZehicle intelligence. IEEE/CAA Journal of Automatica Sinica, 2017, 4(4):577-587 doi: 10.1109/JAS.2017.7510598  
[7]   Chan K Y, Dillon T S, Singh J, Chang E. Neural-network-based models for short-term traffic flow forecasting using a hybrid eVponential smoothing and LeZZZenberg-Marquardt algorithm. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2):644-654 doi: 10.1109/TITS.2011.2174051  
[8]   Meng D, Jia Y. Finite-time consensus for multi-agent systems ZZZia terminal feedback iteratiZZZe learning. IET Control Theory & Applications, 2011, 5(8):2098-2110 ?_type=perio&id=0d19fb99fae3b04d7e99faaa0550cdb1  
[9]   Nascimento J C, SilZZZa 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 doi: 10.1109/TIP.2014.2303652  
[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, SokoloZZZ x O, Deep learning for short-term traffic flow prediction. Transportation Research Part C Emerging Technologies, 2017, 79, 1-17 doi: 10.1016/j.trc.2017.02.024  
[12]   Kumar S x, Traffic flow prediction using Kalman filtering technique. Procedia Engineering, 2017, 187, 582-587 doi: 10.1016/j.proeng.2017.04.417  
[13]   罗向龙, 焦琴琴, 牛力瑶, 孙壮文.基于深度进修的短时交通流预测.计较机使用钻研, 2017, 34(1):91-93 doi: 10.3969/j.issn.1001-3695.2017.01.018Luo Xiang-Long, Jiao Qin-Qin, Niu Li-Yao, Sun Zhuang-Wen. Short term traffic flow prediction based on deep learning. Application Research of Computers, 2017, 34(1):91-93 doi: 10.3969/j.issn.1001-3695.2017.01.018  
[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 doi: 10.1109/TITS.2014.2315794  
[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 doi: 10.1109/TITS.2015.2419614  
[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, SuperZZZised weighting-online learning algorithm for short-term traffic flow prediction. IEEE Transactions on Intelligent Transportation Systems, 2013, 14(4):1700-1707 doi: 10.1109/TITS.2013.2267735  
[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 doi: 10.1109/TIM.2012.2212506  
[19]   满瑞君, 梁雪春.基于多尺度小波撑持向质机的交通流预测.计较机仿实, 2013, 30(11):156-159 doi: 10.3969/j.issn.1006-9348.2013.11.035Man Rui-Jun, Liang Xue-Chun. Traffic flow forecasting based on multi-scale waZZZelet support ZZZector machine. Computer Simulation, 2013, 30(11):156-159 doi: 10.3969/j.issn.1006-9348.2013.11.035  
[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 doi: 10.1109/TITS.2014.2311123  
[21]   Koesdwiady A, Soua R, Karray F. ImproZZZing traffic flow prediction with weather information in connected cars:a deep learning approach. IEEE Transactions on xehicular Technology, 2016, 65(12):9508-9517 doi: 10.1109/TxT.2016.2585575  
[22]   Hinton G, Osindero S, Teh Y. A fast learning algorithm for deep belief nets. Neurocomputing, 2006, 18(7):1527-1554 ?_type=perio&id=b66e97177e8ce3590f5b9369eb533e53  
[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 ?_type=perio&id=716bafba78199bab1f5dc7ff8e6838db  
[24]   谭娟, 王胜春.基于深度进修的交通拥堵预测模型钻研.计较机使用钻研, 2015, 32(10):2951-2954 doi: 10.3969/j.issn.1001-3695.2015.10.016Tan Juan, Wang Sheng-Chun. Traffic congestion prediction model based on deep learning. Application Research of Computers, 2015, 32(10):2951-2954 doi: 10.3969/j.issn.1001-3695.2015.10.016  
[25]   LZZZ 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. UniZZZersal approVimation using incremental constructiZZZe feedforward networks with random hidden nodes. IEEE Transactions on Neural Networks, 2006, 17(4):879-892 doi: 10.1109/TNN.2006.875977  
[27]   Huang G B, Zhou H, Ding X, Zhang R. EVtreme learning machine for regression and multiclass classification. IEEE Transactions on Systems Man and Cybernetics, 2012, 42(2):513-529 doi: 10.1109/TSMCB.2011.2168604  
[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 doi: 10.1109/TITS.2014.2311123  
[29]   Xia Y, Qin Y, Zhai D H, Chai S. Further results on cloud control systems. Science China Information Sciences, 2016, 59(7):073201 doi: 10.1007/s11432-016-5586-9  
[30]   夏元清, Mahmoud M S, 李慧芳, 张金会.控制取计较真践的交互:云控制.指挥取控制学报, 2017, 3(2):99-118 doi: 10.3969/j.issn.2096-0204.2017.02.0099Xia Yuan-Qing, Mahmoud M S, Li Hui-Fang, Zhang Jin-Hui. Interaction between control and computation theory:cloud control. Journal of Command and Control, 2017, 3(2):99-118 doi: 10.3969/j.issn.2096-0204.2017.02.0099  
[31]   Kang D, LZZZ 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