Observability of traffic networks. Optimal location of counting and scanning devices

In this paper, we deal with the observability problem in traffic networks and the optimal location of counting and scanning devices. After explaining what we mean by observability, the problems of what to observe, how to observe traffic data and how to incorporate prior or obsolete information together with the cases of genuine and pseudo-samples of flow data are discussed. Plate scanning information is dealt with and the flow amount of information measure of information corresponding to a subset of scanned links is analysed. Some pivoting and matrix techniques are given for solving the most common problems of observability of traffic flows in a network. Finally, the problem of optimal location of counters and plate scanning cameras is analysed and several examples are given.

[1]  Enrique Castillo,et al.  Trip matrix and path flow reconstruction and estimation based on plate scanning and link observations , 2008 .

[2]  Ioannis Anagnostopoulos,et al.  A License Plate-Recognition Algorithm for Intelligent Transportation System Applications , 2006, IEEE Transactions on Intelligent Transportation Systems.

[3]  Enrique F. Castillo,et al.  An Orthogonally Based Pivoting Transformation of Matrices and Some Applications , 2000, SIAM J. Matrix Anal. Appl..

[4]  Enrique F. Castillo,et al.  The Observability Problem in Traffic Network Models , 2008, Comput. Aided Civ. Infrastructure Eng..

[5]  Monica Gentili,et al.  Locating Active Sensors on Traffic Networks , 2005, Ann. Oper. Res..

[6]  Giuseppe Confessore,et al.  A Network Based Model for Traffic Sensor Location with Implications on O/D Matrix Estimates , 2001, Transp. Sci..

[7]  Shing Chung Josh Wong,et al.  Estimation of multiclass origin-destination matrices from traffic counts , 2005 .

[8]  Santos Sánchez-Cambronero García-Moreno Traffic prediction models using bayesian nerworks and others tools , 2008 .

[9]  Enrique Castillo,et al.  Optimal traffic plate scanning location for OD trip matrix and route estimation in road networks , 2010 .

[10]  Miles Logie,et al.  MVESTM MATRIX ESTIMATION , 1990 .

[11]  William H. K. Lam,et al.  EVALUATION OF COUNT LOCATION SELECTION METHODS FOR ESTIMATION OF O-D MATRICES , 1998 .

[12]  Enrique F. Castillo,et al.  Traffic Estimation and Optimal Counting Location Without Path Enumeration Using Bayesian Networks , 2008, Comput. Aided Civ. Infrastructure Eng..

[13]  David Watling,et al.  MAXIMUM LIKELIHOOD ESTIMATION OF AN ORIGIN-DESTINATION MATRIX FROM A PARTIAL REGISTRATION PLATE SURVEY , 1994 .

[14]  E. Castillo,et al.  A ternary-arithmetic topological based algebraic method for networks traffic observability , 2011 .

[15]  M. J. Hodgson A Flow-Capturing Location-Allocation Model , 2010 .

[16]  Enrique F. Castillo,et al.  The Observability Problem in Traffic Models: Algebraic and Topological Methods , 2008, IEEE Transactions on Intelligent Transportation Systems.

[17]  Enrique Castillo,et al.  Obtaining simultaneous solutions of linear subsystems of inequalities and duals , 2002 .

[18]  Enrique Castillo,et al.  Orthogonal sets and polar methods in linear algebra : applications to matrix calculations, systems of equations, inequalities, and linear programming , 1999 .

[19]  Hani S. Mahmassani,et al.  Sensor Coverage and Location for Real-Time Traffic Prediction in Large-Scale Networks , 2007 .

[20]  Michael G.H. Bell,et al.  The optimisation of traffic count locations in road networks , 2006 .

[21]  Enrique F. Castillo,et al.  Optimal Use of Plate-Scanning Resources for Route Flow Estimation in Traffic Networks , 2010, IEEE Transactions on Intelligent Transportation Systems.

[22]  Hani S. Mahmassani,et al.  Number and Location of Sensors for Real-Time Network Traffic Estimation and Prediction , 2006 .

[23]  Enrique F. Castillo,et al.  Deriving the Upper Bound of the Number of Sensors Required to Know All Link Flows in a Traffic Network , 2013, IEEE Transactions on Intelligent Transportation Systems.

[24]  Enrique F. Castillo,et al.  Matrix Tools for General Observability Analysis in Traffic Networks , 2010, IEEE Transactions on Intelligent Transportation Systems.

[25]  Enrique Castillo,et al.  Dealing with Error Recovery in Traffic Flow Prediction Using Bayesian Networks Based on License Plate Scanning Data , 2011 .

[26]  Hai Yang,et al.  Optimal traffic counting locations for origin–destination matrix estimation , 1998 .

[27]  Enrique Castillo,et al.  Observability in traffic networks. Plate scanning added by counting information , 2012 .

[28]  Francisco G. Benitez,et al.  An approach to estimating and updating origin-destination matrices based upon traffic counts preserving the prior structure of a survey matrix , 2005 .

[29]  Srinivas Peeta,et al.  Identification of vehicle sensor locations for link-based network traffic applications , 2009 .

[30]  Enrique F. Castillo,et al.  Link Flow Estimation in Traffic Networks on the Basis of Link Flow Observations , 2011, J. Intell. Transp. Syst..

[31]  Sang Nguyen,et al.  A unified framework for estimating or updating origin/destination matrices from traffic counts , 1988 .

[32]  Manwo Ng Synergistic sensor location for link flow inference without path enumeration: A node-based approach , 2012 .

[33]  L. Bianco,et al.  Combinatorial aspects of the sensor location problem , 2006, Ann. Oper. Res..

[34]  Hani S. Mahmassani,et al.  Dynamic origin-destination demand estimation using automatic vehicle identification data , 2006, IEEE Transactions on Intelligent Transportation Systems.

[35]  E. Cascetta Estimation of trip matrices from traffic counts and survey data: A generalized least squares estimator , 1984 .