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== Abstract ==
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The paper focuses on the problem of traffic congestion at intersection based on the mechanism of risk identification. The main goal of this study is to explore a new methodology for identifying and predicting the intersection congestion. Considering all the factors influencing the traffic status of intersection congestion, an integrated evaluation index system is constructed. Then, a detailed dynamic decision model is proposed for identifying the risk degree of the traffic congestion and predicting its influence on future traffic flow, which combines the traffic flow intrinsic properties with the basic model of the Risking Dynamic Multi-Attribute Decision-Making theory. A case study based on a real-world road network in Baoji, China, is implemented to test the efficiency and applicability of the proposed modeling. The evaluation result is in accord with the actual condition and shows that the approach proposed can determine the likelihood and risk degree of the traffic congestion occurring in the intersection, which can be used as a tool to help transport managers make some traffic control measures in advance.
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Document type: Article
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== Full document ==
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<pdf>Media:Draft_Content_658098389-beopen876-3954-document.pdf</pdf>
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== Original document ==
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The different versions of the original document can be found in:
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* [https://www.mdpi.com/2071-1050/12/15/5923/pdf https://www.mdpi.com/2071-1050/12/15/5923/pdf] under the license https://creativecommons.org/licenses/by
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* [https://www.mdpi.com/2071-1050/12/15/5923 https://www.mdpi.com/2071-1050/12/15/5923],
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: [https://academic.microsoft.com/#/detail/3045066881 https://academic.microsoft.com/#/detail/3045066881] under the license cc-by
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* [https://www.mdpi.com/2071-1050/12/15/5923 https://www.mdpi.com/2071-1050/12/15/5923],
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: [https://doaj.org/toc/2071-1050 https://doaj.org/toc/2071-1050]
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* [https://www.mdpi.com/2071-1050/12/15/5923/pdf https://www.mdpi.com/2071-1050/12/15/5923/pdf],
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: [http://dx.doi.org/10.3390/su12155923 http://dx.doi.org/10.3390/su12155923]
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 under the license https://creativecommons.org/licenses/by/4.0/
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Document information

Published on 01/01/2020

Volume 2020, 2020
DOI: 10.3390/su12155923
Licence: Other

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