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. T...
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2020-06-01
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Online Access: | http://link.springer.com/article/10.1186/s12544-020-00433-7 |
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doaj-d5ed0c5c5f774d59bbac20ff83b4b0a02020-11-25T02:58:49ZengSpringerOpenEuropean Transport Research Review1867-07171866-88872020-06-0112111210.1186/s12544-020-00433-7Origin-destination matrix estimation with a conditionally binomial modelPirkko Kuusela0Ilkka Norros1Jorma Kilpi2Tomi Räty3Techical Research Centre af Finland, VTT Ltd.University af Helsinki, Department af Mathematics and StatisticsTechical Research Centre af Finland, VTT Ltd.Techical Research Centre af Finland, VTT Ltd.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.http://link.springer.com/article/10.1186/s12544-020-00433-7Vehicular trafficOrigin-destination flowTraffic matrixDoubly stochasticMethod of momentsOptimisation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Pirkko Kuusela Ilkka Norros Jorma Kilpi Tomi Räty |
spellingShingle |
Pirkko Kuusela Ilkka Norros Jorma Kilpi Tomi Räty Origin-destination matrix estimation with a conditionally binomial model European Transport Research Review Vehicular traffic Origin-destination flow Traffic matrix Doubly stochastic Method of moments Optimisation |
author_facet |
Pirkko Kuusela Ilkka Norros Jorma Kilpi Tomi Räty |
author_sort |
Pirkko Kuusela |
title |
Origin-destination matrix estimation with a conditionally binomial model |
title_short |
Origin-destination matrix estimation with a conditionally binomial model |
title_full |
Origin-destination matrix estimation with a conditionally binomial model |
title_fullStr |
Origin-destination matrix estimation with a conditionally binomial model |
title_full_unstemmed |
Origin-destination matrix estimation with a conditionally binomial model |
title_sort |
origin-destination matrix estimation with a conditionally binomial model |
publisher |
SpringerOpen |
series |
European Transport Research Review |
issn |
1867-0717 1866-8887 |
publishDate |
2020-06-01 |
description |
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. |
topic |
Vehicular traffic Origin-destination flow Traffic matrix Doubly stochastic Method of moments Optimisation |
url |
http://link.springer.com/article/10.1186/s12544-020-00433-7 |
work_keys_str_mv |
AT pirkkokuusela origindestinationmatrixestimationwithaconditionallybinomialmodel AT ilkkanorros origindestinationmatrixestimationwithaconditionallybinomialmodel AT jormakilpi origindestinationmatrixestimationwithaconditionallybinomialmodel AT tomiraty origindestinationmatrixestimationwithaconditionallybinomialmodel |
_version_ |
1724705023708889088 |