Origin-destination matrix estimation with a conditionally binomial model

Pirkko Kuusela; Ilkka Norros; Jorma Kilpi; Tomi Räty

doi:10.1186/s12544-020-00433-7

Origin-destination matrix estimation with a conditionally binomial model

Pirkko Kuusela^*
, Ilkka Norros
, Jorma Kilpi
, Tomi Räty

^*Corresponding author for this work

BA6401 Connectivity solutions

University of Helsinki

Research output: Contribution to journal › Article › Scientific › peer-review

1 Citation (Scopus)

179 Downloads (Pure)

Abstract

A doubly stochastic, conditionally binomial model is proposed to describe volumes of vehicular origin-destination flows in regular vehicular traffic, such as morning rush hours. The statistical properties of this model are motivated by the data obtained from inductive loop traffic counts. The model parameters can be expressed as rational functions of the first and second order moments of the observed link counts. Challenges arising from the inaccuracy of moment estimates are studied. A real origin-destination traffic problem of Tampere city is solved by optimisation methods and the accuracy of the solution is examined.

Original language	English
Article number	43
Number of pages	12
Journal	European Transport Research Review
Volume	12
Issue number	1
DOIs	https://doi.org/10.1186/s12544-020-00433-7
Publication status	Published - 17 Jun 2020
MoE publication type	A1 Journal article-refereed

Funding

This research work was funded by the Academy af Finland project 294763 Stomograph. Refining resuls and publishing was funded by EU ECSEL project 737494 MegaMart2 and Business Finland project 123137 RAGE, and VTT’s internal funding.

Keywords

Doubly stochastic
Method of moments
Optimisation
Origin-destination flow
Traffic matrix
Vehicular traffic

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10.1186/s12544-020-00433-7

s12544-020-00433-7Final published version, 1.56 MB

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title = "Origin-destination matrix estimation with a conditionally binomial model",

abstract = "A doubly stochastic, conditionally binomial model is proposed to describe volumes of vehicular origin-destination flows in regular vehicular traffic, such as morning rush hours. The statistical properties of this model are motivated by the data obtained from inductive loop traffic counts. The model parameters can be expressed as rational functions of the first and second order moments of the observed link counts. Challenges arising from the inaccuracy of moment estimates are studied. A real origin-destination traffic problem of Tampere city is solved by optimisation methods and the accuracy of the solution is examined.",

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AU - Kuusela, Pirkko

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AU - Kilpi, Jorma

AU - Räty, Tomi

PY - 2020/6/17

Y1 - 2020/6/17

N2 - A doubly stochastic, conditionally binomial model is proposed to describe volumes of vehicular origin-destination flows in regular vehicular traffic, such as morning rush hours. The statistical properties of this model are motivated by the data obtained from inductive loop traffic counts. The model parameters can be expressed as rational functions of the first and second order moments of the observed link counts. Challenges arising from the inaccuracy of moment estimates are studied. A real origin-destination traffic problem of Tampere city is solved by optimisation methods and the accuracy of the solution is examined.

AB - A doubly stochastic, conditionally binomial model is proposed to describe volumes of vehicular origin-destination flows in regular vehicular traffic, such as morning rush hours. The statistical properties of this model are motivated by the data obtained from inductive loop traffic counts. The model parameters can be expressed as rational functions of the first and second order moments of the observed link counts. Challenges arising from the inaccuracy of moment estimates are studied. A real origin-destination traffic problem of Tampere city is solved by optimisation methods and the accuracy of the solution is examined.

KW - Doubly stochastic

KW - Method of moments

KW - Optimisation

KW - Origin-destination flow

KW - Traffic matrix

KW - Vehicular traffic

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ER -

Origin-destination matrix estimation with a conditionally binomial model

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Keywords

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