To obtain more credible estimates of time-dependent travel demand, various data sources should be exploited jointly to improve the observability of origin–destination (O-D) trip tables. A comprehensive case study uses a real freeway network to reveal how different data coverage affects the quality of estimated O-D tables. The dynamic O-D estimation problem is formulated as a variational inequality (VI) problem that provides a flexible framework to incorporate different data sources and to encapsulate realistic traffic flow dynamics. Traffic surveillance data considered include traffic counts from vehicle detectors, historical O-D tables (static or dynamic), and travel time measurements on subpaths. A novel method is employed to evaluate marginal path travel times, which is a key procedure to properly incorporate travel time measurements into the VI formulation. Numerical experiments generate a number of guidelines to the proper selection of data coverage for obtaining improved O-D estimates.
Get full access to this article
View all access options for this article.
References
1.
CremerM., and KellerH.Dynamic Identification of O-D Flow from Traffic Counts at Complex Intersections. In Proc., 8th International Symposium on Transportation and Traffic Theory, Utrecht, Netherlands, Elsevier, New York, 1981.
2.
CremerM., and KellerH.A Systems Dynamics Approach to the Estimation of Entry and Exit O-D Flows. In Proc., 9th International Symposium on Transportation and Traffic Theory, Toronto, Canada, Brill Academic Publishers, 1984.
3.
NihanN. L., and DavisG. A.Recursive Estimation of Origin-Destination Matrices from Input/Output Counts. Transportation Research, Vol. 21B, 1987, pp. 149–163.
4.
DixonM. P., and RilettL. R.Real Time OD Estimation Using Automatic Vehicle Identification and Traffic Count Data. Computer-Aided Civil and Infrastructure Engineering, Vol. 17, 2002, pp. 7–21.
5.
CremerM., and KellerH.A New Class of Dynamic Methods for the Identification of Origin-Destination Flows. Transportation Research, Vol. 21B, 1987, pp. 117–132.
6.
NihanN. L., and DavisG. A.Application of Prediction-Error Minimisation and Maximum Likelihood to Estimate Intersection O-D Matrices from Traffic Counts. Transportation Science, Vol. 23, 1989, pp. 77–90.
7.
NihanN. L., and HamedM. M.Fixed-Point Approach to Estimating Freeway Origin-Destination Matrices and the Effect of Erroneous Data on Estimate Precision. In Transportation Research Record 1357, TRB, National Research Council, Washington, D.C., 1992, pp. 18–28.
8.
SheraliH. D., AroraN., and HobeikaA. G.Parameter Optimization Methods for Estimating Dynamic Origin–Destination Trip Tables. Transportation Research, Vol. 31B, 1997, pp. 141–157.
9.
BellM. G. H.The Real Time Estimation of Origin–Destination Flows in the Presence of Platoon Dispersion. Transportation Research, Vol. 25B, 1991, pp. 115–125.
10.
ChangG.-L., and WuJ.Recursive Estimation of Time-Varying Origin-Destination Flows from Traffic Counts in Freeway Corridors. Transportation Research, Vol. 28B, 1994, pp. 141–160.
11.
WuJ., and ChangG.-L.Estimation of Time-Varying Origin-Destination Distributions with Dynamic Screenline Flows. Transportation Research, Vol. 30B, 1996, pp. 277–290.
12.
OkutaniI.The Kalman Filtering Approach in Some Transportation and Traffic Problems. In Transportation and Traffic Theory: Proceedings of the 10th International Symposium on Transportation and Traffic Theory, Elsevier Applied Science, 1987, pp. 397–416.
13.
ChangG.-L., and TaoX.Estimation of Dynamic O-D Distribution for Urban Network. In Proc., 13th International Symposium on Transportation and Traffic Theory, Pergamon, Oxford, England, 1996, pp. 1–20.
14.
AshokK., and Ben-AkivaM.Dynamic Origin–Destination Matrix Estimation and Prediction for Real-Time Traffic Management Systems. In Transportation and Traffic Theory: Proc., 12th International Symposium on the Theory of Traffic Flow and Transportation, Berkeley, Calif., Elsevier Science, Ltd., 1993, pp. 465–484.
15.
AshokK.Estimation and Prediction of Time-Dependent Origin-Destination Flows.PhD thesis. Massachusetts Institute of Technology, Cambridge, Mass., 1996.
16.
AshokK., and Ben-AkivaM.Alternative Approaches for Real-Time Estimation and Prediction of Time-Dependent Origin–Destination Flows. Transportation Science, Vol. 34, 2000, pp. 21–36.
17.
WillumsenK. G.Estimating Time-Dependent Trip Matrices from Traffic Counts. In Proc., 9th International Symposium on Transportation and Traffic Theory, Toronto, Canada, Brill Academic Publishers, 1984, pp. 397–411.
18.
CascettaE., InaudiD., and MarquisG.Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts. Transportation Science, Vol. 27, 1993. pp. 363–373.
19.
SheraliH. D., and ParkT.Estimation of Dynamic Origin-Destination Trip Tables for a General Network. Transportation Research, Vol. 35B, 2001, pp. 217–235.
20.
KangY.Estimation and Prediction of Dynamic Origin–Destination (O-D) Demand and System Consistency Control for Real-Time Dynamic Traffic Assignment Operation.PhD thesis. University of Texas, Austin, 1999.
21.
ZhouX.Dynamic Origin–Destination Demand Estimation and Prediction for Off-Line and On-Line Dynamic Traffic Assignment Operation.PhD thesis. University of Maryland, College Park, 2004.
22.
TavanaH.Internally-Consistent Estimation of Dynamic Network Origin-Destination Flows from Intelligent Transportation Systems Data Using Bi-Level Optimization.PhD thesis. University of Texas, Austin, 2001.
23.
NieY.A Variational Inequality Approach for Inferring Dynamic Origin–Destination Travel Demand.PhD thesis. University of California, Davis, 2006.
24.
ShenW., NieY., and ZhangH. M.On Path Marginal Cost Analysis and Its Relation to Dynamic System Optimal Traffic Assignment. In Proc., 17th International Symposium on Transportation and Traffic Theory, 2007, pp. 327–360.
