Meuse: An origin-destination matrix estimator that exploits structure

Micahel Bierlaire; Philippe Toint

Meuse: An origin-destination matrix estimator that exploits structure

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Abstract

This article proposes an improvement of existing methods of origin-destination matrix estimation by an explicit use of data describing the structure of the matrix. These data can be obtained from parking surveys. The new model is applied on both illustrative and real examples, and the results are discussed. Comparisons with the results obtained with SATURN/ME2 and the generalized least-squares method are also presented. © 1994.

Original language	English
Pages (from-to)	47-60
Number of pages	14
Journal	Transportation Research Part B : Methodological
Volume	29
Issue number	1
Publication status	Published - 1 Feb 1995

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title = "Meuse: An origin-destination matrix estimator that exploits structure",

abstract = "This article proposes an improvement of existing methods of origin-destination matrix estimation by an explicit use of data describing the structure of the matrix. These data can be obtained from parking surveys. The new model is applied on both illustrative and real examples, and the results are discussed. Comparisons with the results obtained with SATURN/ME2 and the generalized least-squares method are also presented. {\textcopyright} 1994.",

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AB - This article proposes an improvement of existing methods of origin-destination matrix estimation by an explicit use of data describing the structure of the matrix. These data can be obtained from parking surveys. The new model is applied on both illustrative and real examples, and the results are discussed. Comparisons with the results obtained with SATURN/ME2 and the generalized least-squares method are also presented. © 1994.

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Meuse: An origin-destination matrix estimator that exploits structure

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Urban Crossroads Optimization

Optimization for random utility models

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Meuse: An origin-destination matrix estimator that exploits structure

Abstract

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Urban Crossroads Optimization

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Optimization for random utility models

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