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V2V‐based method for the detection of road traffic congestion.

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Title: V2V‐based method for the detection of road traffic congestion.
Authors: Wang, Runmin1 (AUTHOR), Xu, Zhigang1 (AUTHOR) xzgmail@qq.com, Zhao, Xiangmo1 (AUTHOR), Hu, Jinchao1 (AUTHOR)
Source: IET Intelligent Transport Systems (Wiley-Blackwell). May2019, Vol. 13 Issue 5, p880-885. 6p.
Subjects: Traffic monitoring, Traffic congestion, Traffic density, Traffic safety, Telecommunication systems
Abstract: The traffic congestion detection based on the internet of vehicles is gaining enormous research interest. A vehicle‐to‐vehicle (V2V)‐based method for the detection of road traffic congestion is proposed. Firstly, a fuzzy controller was constructed based on the vehicle speed, traffic density, and traffic congestion rating system, and the level of local traffic congestion was evaluated. Then, the level of local traffic congestion of neighbouring vehicles was queried based on V2V communication, and the level of regional traffic congestion was obtained based on a large sub‐sample hypothesis test. Finally, a simulation test platform was built based on vehicles in network simulation, and the back‐off time slots and received packets of vehicle nodes were calculated. The accuracy of the proposed method for detecting road traffic congestion was compared to the cooperative traffic congestion detection (CoTEC) method and the geomagnetic coil method. The results show that the detection accuracy of the proposed method increased by 5.5 and 7.5%, respectively, compared to the geomagnetic coil method and CoTEC method. The V2V communication network overhead of the proposed traffic congestion detection method is reduced by 90.8% compared to the adopted CoTEC method. The communication overhead of the vehicle node using the proposed method is significantly decreased when there is no traffic congestion. [ABSTRACT FROM AUTHOR]
Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 183824913
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
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  Data: V2V‐based method for the detection of road traffic congestion.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Runmin%22">Wang, Runmin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Xu%2C+Zhigang%22">Xu, Zhigang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> xzgmail@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Xiangmo%22">Zhao, Xiangmo</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hu%2C+Jinchao%22">Hu, Jinchao</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22IET+Intelligent+Transport+Systems+%28Wiley-Blackwell%29%22">IET Intelligent Transport Systems (Wiley-Blackwell)</searchLink>. May2019, Vol. 13 Issue 5, p880-885. 6p.
– Name: Subject
  Label: Subjects
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  Data: <searchLink fieldCode="DE" term="%22Traffic+monitoring%22">Traffic monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+congestion%22">Traffic congestion</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+density%22">Traffic density</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+safety%22">Traffic safety</searchLink><br /><searchLink fieldCode="DE" term="%22Telecommunication+systems%22">Telecommunication systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The traffic congestion detection based on the internet of vehicles is gaining enormous research interest. A vehicle‐to‐vehicle (V2V)‐based method for the detection of road traffic congestion is proposed. Firstly, a fuzzy controller was constructed based on the vehicle speed, traffic density, and traffic congestion rating system, and the level of local traffic congestion was evaluated. Then, the level of local traffic congestion of neighbouring vehicles was queried based on V2V communication, and the level of regional traffic congestion was obtained based on a large sub‐sample hypothesis test. Finally, a simulation test platform was built based on vehicles in network simulation, and the back‐off time slots and received packets of vehicle nodes were calculated. The accuracy of the proposed method for detecting road traffic congestion was compared to the cooperative traffic congestion detection (CoTEC) method and the geomagnetic coil method. The results show that the detection accuracy of the proposed method increased by 5.5 and 7.5%, respectively, compared to the geomagnetic coil method and CoTEC method. The V2V communication network overhead of the proposed traffic congestion detection method is reduced by 90.8% compared to the adopted CoTEC method. The communication overhead of the vehicle node using the proposed method is significantly decreased when there is no traffic congestion. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of IET Intelligent Transport Systems (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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    Identifiers:
      – Type: doi
        Value: 10.1049/iet-its.2018.5177
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 6
        StartPage: 880
    Subjects:
      – SubjectFull: Traffic monitoring
        Type: general
      – SubjectFull: Traffic congestion
        Type: general
      – SubjectFull: Traffic density
        Type: general
      – SubjectFull: Traffic safety
        Type: general
      – SubjectFull: Telecommunication systems
        Type: general
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      – TitleFull: V2V‐based method for the detection of road traffic congestion.
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            NameFull: Wang, Runmin
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            NameFull: Xu, Zhigang
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            NameFull: Zhao, Xiangmo
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            NameFull: Hu, Jinchao
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            – D: 01
              M: 05
              Text: May2019
              Type: published
              Y: 2019
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