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Road traffic states estimation algorithm based on matching of regional traffic attracters 被引量:3

Road traffic states estimation algorithm based on matching of regional traffic attracters
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摘要 To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states,a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work.First of all,the road traffic running states were divided into several different modes.The concept of the regional traffic attracters of the target link was put forward for effective matching.Then,the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established.In addition,the current and historical regional traffic attracters of the target link were matched through certain matching rules,and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data,which were processed with Kalman filter to obtain the final recovery data.Finally,some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm.The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy. To effectively solve the traffic data problems such as data invalidation in the process of the acquisition of road traffic states, a road traffic states estimation algorithm based on matching of the regional traffic attracters was proposed in this work. First of all, the road traffic running states were divided into several different modes. The concept of the regional traffic attracters of the target link was put forward for effective matching. Then, the reference sequences of characteristics of traffic running states with the contents of the target link's traffic running states and regional traffic attracters under different modes were established. In addition, the current and historical regional traffic attracters of the target link were matched through certain matching rules, and the historical traffic running states of the target link corresponding to the optimal matching were selected as the initial recovery data, which were processed with Kalman filter to obtain the final recovery data. Finally, some typical expressways in Beijing were adopted for the verification of this road traffic states estimation algorithm. The results prove that this traffic states estimation approach based on matching of the regional traffic attracters is feasible and can achieve a high accuracy.
出处 《Journal of Central South University》 SCIE EI CAS 2014年第5期2100-2107,共8页 中南大学学报(英文版)
基金 Projects(D07020601400707,D101106049710005)supported by the Beijing Science Foundation Plan Project,China Projects(2006AA11Z231,2012AA112401)supported by the National High Technology Research and Development Program of China(863 Program) Project(61104164)supported by the National Natural Science Foundation of China
关键词 road traffic regional traffic attracter traffic state data recovery MATCHING 状态估计算法 道路交通 区域交通 匹配算法 运行状态 交通状态 卡尔曼滤波 交通数据
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  • 1张赫,王炜,顾怀中.Application of cluster analysis and stepwise regression in predicting the traffic volume of lanes[J].Journal of Southeast University(English Edition),2005,21(3):359-362. 被引量:5
  • 2WILLIAMS B M,DURVASULA P K,BROWN D E.Urban freeway traffic flow prediction:Application of seasonal autoregressive integrated moving average and exponential smoothing models[C]//Transportation Research Board 1644,Transportation Research Board.Washington,DC:TRB,1998:132-141.
  • 3RAMSEY B,HAYDEN G.AutoCounts:A way to analyse automatic traffic count data[J].Traffic Engineering and Control,1994,35(4):245-247.
  • 4LINT J W C,HOOGENDOORN S P,ZUYLEN H J.Accurate freeway travel time prediction with state-space neural networks under missing data[J].Transportation Research,2005,13(5/6):347-369.
  • 5HARTIKAINEN J,SASRKKAS.Optimal filtering with Kalman filters and smoothers-a Manual for Maltab toolbox EKF/UKF[EB/OL].[2009-07-01].http://www.cs.unc.edu/-welch/kalman.2007-08.
  • 6ZHONG M,LINGRAS P,SHARMA S.Estimation of missing traffic counts using factor,genetic,neural,and regression techniques[J].Transportation Research,2004,12(2):139-166.
  • 7STEPHANEDES Y J,MICHALOPOULOS P G,PLUM R A.Improved estimation of traffic flow for real-time control[C]//Transportation Research Record 795.Washington,DC:[s.n.],1981:28-39.
  • 8DAUBECHIES I.Ten lectures on wavelets[M].Philadelphia:Society for Industrial and Applied Mathematics,1992.
  • 9SMITH B L, SCHERER W T, CONKIAN J H. Explorin1 imputation techniques for missing data in transportation man1 agement systems[J]. Transportation Research Record,I 2003(1836) : 132 142.
  • 10ZHONG Ming, I.INGRAS P, SHARMA S. Estimation of missing traffic counts using factor, genetic, neural, and regression techniques[J].Transportation Research Part C: Emerging Technologies, 2004, 12(2): 139-166.

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  • 1Cascetta E;Inaudi D;Marquis G.Dynamic estimators of origin-destination matrices using traffic counts,1993(04).
  • 2Chung-Cheng Lu,Xuesong Zhou,Kuilin Zhang.Dynamic origin–destination demand flow estimation under congested traffic conditions[J]. Transportation Research Part C . 2013
  • 3Ennio Cascetta,Andrea Papola,Vittorio Marzano,Fulvio Simonelli,Iolanda Vitiello.Quasi-dynamic estimation of o–d flows from traffic counts: Formulation, statistical validation and performance analysis on real data[J]. Transportation Research Part B . 2013
  • 4Xuesong Zhou,Hani S. Mahmassani.A structural state space model for real-time traffic origin–destination demand estimation and prediction in a day-to-day learning framework[J]. Transportation Research Part B . 2007 (8)
  • 5Pei-Wei Lin,Gang-Len Chang.A generalized model and solution algorithm for estimation of the dynamic freeway origin–destination matrix[J]. Transportation Research Part B . 2006 (5)
  • 6Michael P.Dixon,L. R.Rilett.Real‐Time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data[J]. Computer‐Aided Civil and Infrastructure Engineering . 2002 (1)
  • 7Hanif D. Sherali,Taehyung Park.Estimation of dynamic origin–destination trip tables for a general network[J]. Transportation Research Part B . 2001 (3)
  • 8Srinivas Peeta,Athanasios K. Ziliaskopoulos.Foundations of Dynamic Traffic Assignment: The Past, the Present and the Future[J]. Networks and Spatial Economics . 2001 (3)
  • 9Gang-Len Chang,Xianding Tao.An integrated model for estimating time-varying network origin–destination distributions[J]. Transportation Research Part A . 1999 (5)
  • 10Cremer, M.,Keller, H.NEW CLASS OF DYNAMIC METHODS FOR THE IDENTIFICATION OF ORIGIN-DESTINATION FLOWS. Transportation Research . 1987

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