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Citation |
Yu P, Davis GA. Transp. Res. Rec. 1994; 1457: 127-133. |
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Copyright |
(Copyright © 1994, Transportation Research Board, National Research Council, National Academy of Sciences USA, Publisher SAGE Publishing) |
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DOI |
unavailable |
PMID |
unavailable |
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Abstract |
To enable the efficient use of existing roadway capacity, researchers and practitioners are developing advanced traffic management systems (ATMSs), which has led to an increased interest in problems connected to the estimation of origin-destination (O-D) flows using information provided by freeway surveillance and control systems. A number of methods based on a linear traffic assignment model have been applied successfully to single intersections, and some of these estimators were extended to a section of freeway. The results from Monte Carlo simulation suggest that ordinary least squares (OLS) and expectation-maximization approaches were either biased or inefficient. A nonlinear least squares (NLS) estimator that eliminated model specification error was introduced, and it performed better in terms of statistical efficiency and lack of bias. This implies that accurate O-D estimation may require an accurate traffic flow model and that actual implementation may require joint estimation of O-D patterns and traffic flow model parameters. On the other hand, a constrained approximate maximum likelihood estimator performed better than OLS but somewhat worse than NLS, showing some potential for providing a simple and yet plausibly accurate approach. Language: en |
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Keywords |
Automation; Intersections; Mathematical models; Highway traffic control; Control systems; Estimation; Computer simulation; Highway systems; Probability; Least squares approximations; Monte Carlo methods; Nonlinear equations; Matrix algebra |