View in EDS

Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction.

Saved in:
Bibliographic Details
Title: Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction.
Authors: Kumar, Manoj1 (AUTHOR) manojkumar7281@gmail.com, Kumar, Kranti1,2 (AUTHOR) kranti31lu@gmail.com
Source: International Journal of Computational Intelligence & Applications. Sep2024, Vol. 23 Issue 3, p1-16. 16p.
Subjects: Artificial neural networks, Traffic congestion, Camcorders, Deep learning, Time perspective
Abstract: Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computational Intelligence & Applications is the property of World Scientific Publishing Company 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
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 179812847
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Kumar%2C+Manoj%22">Kumar, Manoj</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> manojkumar7281@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Kranti%22">Kumar, Kranti</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> kranti31lu@gmail.com</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computational+Intelligence+%26+Applications%22">International Journal of Computational Intelligence & Applications</searchLink>. Sep2024, Vol. 23 Issue 3, p1-16. 16p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Traffic+congestion%22">Traffic congestion</searchLink><br /><searchLink fieldCode="DE" term="%22Camcorders%22">Camcorders</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Time+perspective%22">Time perspective</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Forecasting traffic congestion has become an important issue than ever before. Information provided by traffic congestion prediction methods becomes helpful to traffic management agencies to take suitable actions in due time. On the other hand, with prior information, passengers can plan their trip routes accordingly. In this paper, we tackle the problem of mixed traffic congestion prediction and develop a new Bi-State Prediction Network (BiSPNet). Two heads form BiSPNet. Each head information passed through the layers of three-dimensional convolution (Conv3D), two-dimensional convolutional long short-term memory (ConvLSTM2D), and two-dimensional convolution (Conv2D). Then the dense layer merges both head outputs. These layers capacitate the model to capture the characteristics of multiple inputs. The proposed model is validated by the ground truth data collected by the video cameras installed on the roadways in Delhi, India. Experimental results confirm that the prediction accuracy of the BiSPNet model surpasses that of several state-of-the-art benchmark models. In particular, the BiSPNet reduces mean absolute error by 53.23%, 40.95%, and 0.38%, respectively, in congestion score prediction for 5, 10, and 15-min time horizons compared to the mixed deep learning network (MDLNet). The source data and code can be accessed on GitHub at the following link: https://github.com/amanojup/. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Computational Intelligence & Applications is the property of World Scientific Publishing Company 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.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=179812847
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1142/S1469026824500111
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 16
        StartPage: 1
    Subjects:
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Traffic congestion
        Type: general
      – SubjectFull: Camcorders
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Time perspective
        Type: general
    Titles:
      – TitleFull: Bi-State Prediction Network Model for Mixed Traffic Congestion Prediction.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kumar, Manoj
      – PersonEntity:
          Name:
            NameFull: Kumar, Kranti
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 09
              Text: Sep2024
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 14690268
          Numbering:
            – Type: volume
              Value: 23
            – Type: issue
              Value: 3
          Titles:
            – TitleFull: International Journal of Computational Intelligence & Applications
              Type: main
ResultId 1