STRATEGIES FOR SELECTING ADDITIONAL TRAFFIC COUNTS FOR IMPROVING O-D TRIP TABLE ESTIMATION

Traditional traffic counting location (TCL) problem is to determine the number and locations of counting stations that would best cover the network for the purpose of estimating origin-destination (O-D) trip tables. It is well noted that the quality of the estimated O-D trip table depends on the estimation methods, an appropriate set of links with traffic counts, and the quality of the traffic counts. In this paper, we develop strategies in the screen-line-based TCL model for selecting additional traffic counts for improving O-D trip table estimation. Using these selected traffic counts, the O-D trip table is estimated using a modified path flow estimator that is capable of handling traffic count inconsistency internally. To illustrate the impact of the additional number of traffic counts on O-D estimation, we set up a unique experiment in a real world setting to visually observe the evolution of O-D estimation as the number of traffic counting locations increases. By comparing the O-D trip tables in a GIS, we visualize the actual impacts of counting locations on the estimation results. Various spatial properties of O-D trip tables estimated from traffic counts of different locations are identified as results of the study.

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

[2]  N. Megiddo,et al.  The Maximum Coverage Location Problem , 1983 .

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

[4]  M. G. H. Bell.  THE ESTIMATION OF JUNCTION TURNING VOLUMES FROM TRAFFIC COUNTS: THE ROLE OF PRIOR INFORMATION , 1984 .

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

[6]  H. Spiess.  A MAXIMUM LIKELIHOOD MODEL FOR ESTIMATING ORIGIN-DESTINATION MATRICES , 1987 .

[7]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[8]  Nicholas J. Garber,et al.  Traffic and Highway Engineering , 1988 .

[9]  C. S. Fisk,et al.  ON COMBINING MAXIMUM ENTROPY TRIP MATRIX ESTIMATION WITH USER OPTIMAL ASSIGNMENT , 1988 .

[10]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[11]  W. H. K. Lam,et al.  Accuracy of O-D estimates from traffic counts , 1990 .

[12]  Hai Yang,et al.  An analysis of the reliability of an origin-destination trip matrix estimated from traffic counts , 1991 .

[13]  R. Sivanandan,et al.  A linear programming approach for synthesizing origin-destination trip tables from link traffic volumes , 1994 .

[14]  Hai Yang,et al.  Estimation of origin-destination matrices from link traffic counts on congested networks , 1992 .

[15]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

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

[17]  Hai Yang.  Heuristic algorithms for the bilevel origin-destination matrix estimation problem , 1995 .

[18]  Michael G.H. Bell,et al.  A Log-Linear Model for Path Flow Estimation , 1996 .

[19]  Y Iida,et al.  Transportation Network Analysis , 1997 .

[20]  Michael G.H. Bell,et al.  A STOCHASTIC USER EQUILIBRIUM PATH FLOW ESTIMATOR , 1997 .

[21]  Michael G.H. Bell,et al.  Transportation Network Analysis: Bell/Transportation Network Analysis , 1997 .

[22]  L A Hoel,et al.  TRAFFIC AND HIGHWAY ENGINEERING. SECOND EDITION , 1997 .

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

[24]  Dimitri P. Bertsekas,et al.  Network optimization : continuous and discrete models , 1998 .

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

[26]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

[27]  Martin L. Hazelton,et al.  Estimation of origin-destination matrices from link flows on uncongested networks , 2000 .

[28]  Hai Yang,et al.  Determing Cordons and Screen Lines for Origin-destination Trip Studies , 2001 .

[29]  Michael J. Maher,et al.  A bi-level programming approach for trip matrix estimation and traffic control problems with stochastic user equilibrium link flows , 2001 .

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

[31]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

[32]  Michael G.H. Bell,et al.  Sensitivity analysis on Stochastic Equilibrium transportation networks using genetic algorithm , 2004 .

[33]  Hai Yang,et al.  Traffic Counting Location and Error Bound in Origin-Destination Matrix Estimation Problems , 2005 .

[34]  Wilfred W. Recker,et al.  Improved Path Flow Estimator for Origin-Destination Trip Tables , 2005 .

[35]  Anthony Chen,et al.  A BI-OBJECTIVE TRAFFIC COUNTING LOCATION PROBLEM FOR ORIGIN-DESTINATION TRIP TABLE ESTIMATION , 2005 .

[36]  Anthony Chen,et al.  Examining the Quality of Synthetic Origin-Destination Trip Table Estimated by Path Flow Estimator , 2005 .

[37]  Chao Yang,et al.  Models and algorithms for the screen line-based traffic-counting location problems , 2006, Comput. Oper. Res..

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

[39]  Yuval Rabani,et al.  Linear Programming , 2007, Handbook of Approximation Algorithms and Metaheuristics.