Improve urban passenger transport management by rationally forecasting traffic congestion probability

A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Feng, Xuesong [verfasserIn] Saito, Mitsuru Liu, Yi

Format:	Artikel
Sprache:	Englisch

Erschienen:	2016

Rechteinformationen:	Nutzungsrecht: © 2015 Informa UK Limited, trading as Taylor & Francis Group 2015

Schlagwörter:	urban transport operation management probabilistic forecast modelling traffic congestion probability Bayesian network policy application effect analysis Traffic congestion Probability Roads & highways

Übergeordnetes Werk:	Enthalten in: International journal of production research - London : Taylor & Francis, 1961, 54(2016), 12, Seite 3465-10
Übergeordnetes Werk:	volume:54 ; year:2016 ; number:12 ; pages:3465-10

Links:	Volltext Link aufrufen Link aufrufen

DOI / URN:	10.1080/00207543.2015.1062570

Katalog-ID:	OLC1977020771

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author	Feng, Xuesong
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title	Improve urban passenger transport management by rationally forecasting traffic congestion probability
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title_full	Improve urban passenger transport management by rationally forecasting traffic congestion probability
author_sort	Feng, Xuesong
journal	International journal of production research
journalStr	International journal of production research
lang_code	eng
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author_browse	Feng, Xuesong
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author-letter	Feng, Xuesong
doi_str_mv	10.1080/00207543.2015.1062570
dewey-full	600 620 330
title_sort	improve urban passenger transport management by rationally forecasting traffic congestion probability
title_auth	Improve urban passenger transport management by rationally forecasting traffic congestion probability
abstract	A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.
abstractGer	A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.
abstract_unstemmed	A Bayesian network (BN) approach is proposed in this study to analyse the overall traffic congestion probability of an urban road network in consideration of the influence of applying various transport policies. The continually expanding urbanised region of Beijing has been chosen as the study area because of its rapid expansion and motorisation, which lead to the severe traffic congestion occurring nearly every day. It is demonstrated that the proposed BN approach is able to rationally predict the probability of the overall traffic congestion that will take place given a certain transport policy. It is also proven that increasing the number of buses providing convenient passenger transport service in the urbanised region of Beijing will most effectively reduce the probability of the traffic congestion in this area, especially when the newly constructed roads in the same region are put into use.
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container_issue	12
title_short	Improve urban passenger transport management by rationally forecasting traffic congestion probability
url	http://dx.doi.org/10.1080/00207543.2015.1062570 http://www.tandfonline.com/doi/abs/10.1080/00207543.2015.1062570 http://search.proquest.com/docview/1780983824
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author2	Saito, Mitsuru Liu, Yi
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doi_str	10.1080/00207543.2015.1062570
up_date	2024-07-03T17:10:05.314Z
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