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Dynamic Estimators of Origin-Destination Matrices Using Traffic Counts

Ennio Cascetta, Domenico Inaudi, Gérald Marquis

Open publisher page 370 citations

Abstract

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

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What this paper is about

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

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Available abstract

This paper proposes different “dynamic” estimators using time-varying traffic counts to obtain (discrete) time-varying OD flows or average OD flows. All the estimators can combine counts with other available information, e.g., out-dated matrices and surveys, on a general network and can be formulated as optimization problems. In the case of time-varying OD flows, two types of estimators are proposed. Simultaneous estimators produce joint estimates of the whole set of OD matrices, one for each time slice, whereas sequential estimators produce a sequence of OD estimates for successive time slices. A particular type of simultaneous and sequential estimator (namely Generalized Least Squares) was tested on the Italian Brescia–Padua motorway for which “true” OD flows over a whole day were available. Results were generally satisfactory, showing that also in the “no a priori information” case significant estimates could be obtained.

Key concepts: Estimator, Mathematics, A priori and a posteriori, Set (abstract data type), Sequence (biology), Traffic flow (computer networking), Least-squares function approximation, Statistics

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