Time Dependent Origin-destination Estimation from Traffic Count without Prior Information

Existing research works on time-dependent origin-destination (O-D) estimation focus on the surveillance data usually assume the prior information of the O-D matrix (or transition matrix) is known (or at least partially known). In this paper, we relax such assumption by combining Gibbs sampler and Kalman filter in a state space model. A solution algorithm with parallel chain convergence control is proposed and implemented. To enhance its efficiency, a parallel structure is suggested with efficiency and speedup demonstrated using PC-cluster. Two numerical examples (one for Taipei Mass Rapid Transit network and the other for Taiwan Area National Freeway network) are included to show the proposed model could be effective of time-dependent origin-destination estimation.

[1]  V F Hurdle,et al.  DYNAMIC IDENTIFICATION OF FLOWS FROM TRAFFIC COUNTS AT COMPLEX INTERSECTIONS , 1983 .

[2]  Siu-Chung Wong,et al.  Pedestrian simulation model for Hong Kong underground stations , 2001, ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585).

[3]  Donald Geman,et al.  Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images , 1984 .

[4]  Pei-Wei Lin,et al.  Incomplete Information Analysis for the Origin-Destination Survey Table , 2006 .

[5]  I. Okutani.  THE KALMAN FILTERING APPROACHES IN SOME TRANSPORTATION AND TRAFFIC PROBLEMS , 1987 .

[6]  Christian P. Robert,et al.  Convergence Control of MCMC Algorithms , 1998 .

[7]  Ennio Cascetta,et al.  Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts , 1993, Transp. Sci..

[8]  Terry L. Friesz,et al.  Dynamic Systems, Variational Inequalities and Control Theoretic Models for Predicting Time-Varying Urban Network Flows , 1996, Transp. Sci..

[9]  Gang-Len Chang,et al.  Recursive estimation of time-varying origin-destination flows from traffic counts in freeway corridors , 1994 .

[10]  Gang-Len Chang,et al.  An advanced computing architecture for large-scale network O-D estimation , 1995, Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future.

[11]  H. M. Zhang,et al.  A Relaxation Approach for Estimating Origin–Destination Trip Tables , 2010 .

[12]  C. Robert.  Discretization and Mcmc Convergence Assessment , 1998 .

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

[14]  S. Frühwirth-Schnatter.  Data Augmentation and Dynamic Linear Models , 1994 .

[15]  Carlos F. Daganzo,et al.  TRANSPORTATION AND TRAFFIC THEORY , 1993 .

[16]  Laurence R. Rilett,et al.  Population Origin-Destination Estimation Using Automatic Vehicle Identification and Volume Data , 2005 .

[17]  M. Pitt,et al.  Analytic Convergence Rates and Parameterization Issues for the Gibbs Sampler Applied to State Space Models , 1999 .

[18]  Henk J van Zuylen,et al.  The most likely trip matrix estimated from traffic counts , 1980 .

[19]  Moshe E. Ben-Akiva,et al.  Estimation and Prediction of Time-Dependent Origin-Destination Flows with a Stochastic Mapping to Path Flows and Link Flows , 2002, Transp. Sci..

[20]  R. Kohn,et al.  On Gibbs sampling for state space models , 1994 .

[21]  Terry L. Friesz,et al.  Dynamic Network User Equilibrium with State-Dependent Time Lags , 2001 .

[22]  James Roberts,et al.  Traffic Matrix Inference in IP Networks , 2004 .

[23]  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 .

[24]  Kalidas Ashok,et al.  DYNAMIC ORIGIN-DESTINATION MATRIX ESTIMATION AND PREDICTION FOR REAL- TIME TRAFFIC MANAGEMENT SYSTEMS , 1993 .

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

[26]  H. Lo,et al.  Simultaneous estimation of an origin-destination matrix and link choice proportions using traffic counts , 2003 .

[27]  D. Gamerman.  Markov chain Monte Carlo for dynamic generalised linear models , 1998 .

[28]  M. Bell.  THE ESTIMATION OF ORIGIN-DESTINATION MATRICES BY CONSTRAINED GENERALISED LEAST SQUARES , 1991 .

[29]  S. Koopman,et al.  Monte Carlo estimation for nonlinear non-Gaussian state space models , 2007 .

[30]  M. Maher.  INFERENCES ON TRIP MATRICES FROM OBSERVATIONS ON LINK VOLUMES: A BAYESIAN STATISTICAL APPROACH , 1983 .