基于卡尔曼滤波的城市快速路交通密度估计与拥堵识别
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摘要
针对城市快速路网中只有部分路段检测器可用的情况,为准确地估计交通密度并基于此快速识别路网所有路段的交通拥堵情况,研究了基于宏观交通流模型的卡尔曼滤波器设计方法。结合动态图混杂自动机(DGHA)与元胞传输模型(CTM)对快速路网建模,在此基础上推导出分段仿射线性系统(PWALS)模型。基于所得到的模型设计出切换型卡尔曼滤波器进行交通密度估计,并通过将路段密度估计值与临界拥堵密度进行对比来对快速路网的拥堵进行识别。以京通快速路为例进行实验,结果表明,真实值与估计值的平均绝对误差为MAE=0.625 988,显示了所提方法的有效性。
For the situation where only apart of traffic detectors are available to obtain traffic information in urban freeway networks,a Kalman filter based ona macroscopic traffic flow model is studied in order to accurately estimate traffic density,and moreover,to quickly identify traffic congestion of all road sections.A macroscopic traffic flow model of urban freeway networks is developed by combining Dynamic Graph Hybrid Automata(DGHA)with Cell Transmission Model(CTM),and a Piecewise Affine Linear System(PWALS)model is deduced.Traffic density is estimated in the switched Kalman filter designed by this model,and congestion of urban freeway networks can be identified by comparing the road density estimation with the critical congestion density.The experiment takes Jingtong freeway in Beijing as a case study,and the Mean Absolute Error(MAE)which is generated by estimated value and actual value is 0.625 988.The results indicate the effectiveness of the proposed method.
For the situation where only apart of traffic detectors are available to obtain traffic information in urban freeway networks,a Kalman filter based ona macroscopic traffic flow model is studied in order to accurately estimate traffic density,and moreover,to quickly identify traffic congestion of all road sections.A macroscopic traffic flow model of urban freeway networks is developed by combining Dynamic Graph Hybrid Automata(DGHA)with Cell Transmission Model(CTM),and a Piecewise Affine Linear System(PWALS)model is deduced.Traffic density is estimated in the switched Kalman filter designed by this model,and congestion of urban freeway networks can be identified by comparing the road density estimation with the critical congestion density.The experiment takes Jingtong freeway in Beijing as a case study,and the Mean Absolute Error(MAE)which is generated by estimated value and actual value is 0.625 988.The results indicate the effectiveness of the proposed method.
引文
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[2]姜桂艳,常安德,牛世峰.基于车牌识别数据的交通拥堵识别方法[J].哈尔滨工业大学学报,2011,43(4):131-135.JIANG Guiyan,Chang Andeand Niu Shifeng,Traffic congestion identification method based on license plate recognition data[J].Journal of Harbin Institute of Technology,2011,43(4):131-135.(in Chinese)
[3]XU L,YUE Y,LI Q.Identifying urban traffic congestion pattern from historical floating car data[J].Procedia-Social and Behavioral Sciences,2013(96):2084-2095.
[4]TAO S,MANOLOPOULOS V,RODRIGUEZ S,et al.Real-time urban traffic state estimation with A-GPS mobile phones as probes[J].Journal of Transportation Technologies,2012,2(1):22-31.
[5]LIU C,QIN K,KANG C.Exploring time-dependent traffic congestion patterns from taxi trajectory data[C].Proceedings of the IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services,Fuzhou,China:IEEE,2015:39-44.
[6]CARLI R,DOTOLI M,EPICOCO N,et al.Automated evaluation of urban traffic congestion using bus as a probe[C].Proceedings of the IEEE International Conference on Automation Scienceand Engineering,Suzhou,China:IEEE,2015:967-972.
[7]邢珊珊,谷远利,沈立杰,等.基于速度的城市快速路交通拥堵预测研究[J].交通信息与安全,2016,34(2):48-54.XING Shanshan,GU Yuanli,SHEN Lijie,et al.Atraffic congestion prediction model based on vehicle speed of urban expressways[J].Journal of Transport Information and Safety,2016,34(2):48-54.(in Chinese)
[8]He Z C,Xi W F,Zhuang L J,et al.On-line mapmatching framework for floating car data with low sampling rate in urban road networks[J].Intelligent Transport Systems IET,2013,7(4):404-414.
[9]杨兆升,朱中.基于卡尔曼滤波理论的交通流量实时预测模型[J].中国公路学报,1999(3):63-67.YANG Zhaosheng,ZHU Zhong,A real-time traffic volume predict ion model based on the Kalman filtering theory[J].China Journal of Highway and Transport,1999(9):74-78(in Chinese).
[10]徐天东,孙立军,郝媛.城市快速路实时交通状态估计和行程时间预测[J].同济大学学报(自然科学版),2008(10):1355-1361XU Tiandong,Sun Lijun,Hao Yuan,Real time traffic state estimation and travel time prediction on urban expressway[J].Journal of Tongji University(Natural Science Edition)2008(10):1355-1361.(in Chinese).
[11]DAGANZO,C F.The cell transmission model:a dynamic representation of highway traffic consistent with the hydrodynamic theory[J].Transportation Research Part B:Methodological,1994,28(4):269-287.
[12]DAGANZO,C F.The cell transmission model,part II:network traffic[J].Transportation Research Part B:Methodological,1995,29(2):79-93.
[13]MUOZ L,SUN X,HOROWITZ R,et al.Traffic density estimation with the cell transmission model[C].Proceedings of the IEEE American Control Conference,Colorado,USA:IEEE,2003.
[14]SUN X,MUOZ L,HOROWITZ R.Mixture kalman filter based highway congestion mode and vehicle density estimator and its application[C].Proceedings of the IEEE American Control Conference,Boston:IEEE,2004.
[15]De-WIT C C,OJEDA L L,KIBANGOU A Y,Graph constrained-CTM observer design for the Grenoble south ring[C].Proceedings of the 13th IF-AC,Chennai,INDIA:IEEE,2012:197-202.
[16]MORBIDI F,OJEDA L L,De WIT C C,et al.Anew robust approach for highway traffic density estimation[C].Proceedings of the European Control Conference,Chennai,INDIA:IEEE 2014:2575-2580.
[17]CHEN Y,GUO Y,WANG Y,et al.Modeling freeway network by using dynamic graph hybrid automata and estimating its states by designing state observer[C].Proceedings of the IEEE Chinese Automation Congress,Yinchuan,China:IEEE,2015:237-242.
[18]李楠,赵光宙.基于交通流混合模型的高速公路状态估计[J].浙江大学学报(工学版),2012(10):1846-1850,1879.LI Nan,Zhao Guangzhou,State estimation of freeway based on traffic flow hybrid model[J].Journal of Zhejiang University(Engineering Science Edition)2012(10):1846-1850,1879.