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Online traffic flow assignment method based on structure matching selection of multilevel road network.

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Τίτλος: Online traffic flow assignment method based on structure matching selection of multilevel road network.
Συγγραφείς: Di, Zhang
Πηγή: International Journal of Computers & Applications; Feb2021, Vol. 43 Issue 2, p171-175, 5p
Θεματικοί όροι: Traffic flow measurement, Artificial intelligence, Regression analysis data processing, Neural computers, Artificial neural networks, Sensitivity analysis
Περίληψη: The main purpose of the paper is to propose a traffic flow assignment method with matched selection of a multi-level road traffic network structure. Firstly, road connection 'design' parameters of traffic pricing are introduced, equilibrium conditions of users by traffic flow assignment are improved, design/pricing problem models of traffic network are constructed and surrounding semi-differentiable conditions of the reference point are considered and sensibility analysis methods of traffic equilibrium are designed. Then, sensitivity analysis is implemented combined with traffic flow assignment (TAPAS) output based on paired selection, it is shown how to use TAPAS to find a very precise equilibrium solution effectively and use the directional derivative. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Computers & Applications is the property of Taylor & Francis Ltd 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.)
Βάση Δεδομένων: Complementary Index
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  Data: Online traffic flow assignment method based on structure matching selection of multilevel road network.
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  Data: <searchLink fieldCode="AR" term="%22Di%2C+Zhang%22">Di, Zhang</searchLink>
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  Data: International Journal of Computers & Applications; Feb2021, Vol. 43 Issue 2, p171-175, 5p
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  Data: <searchLink fieldCode="DE" term="%22Traffic+flow+measurement%22">Traffic flow measurement</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Regression+analysis+data+processing%22">Regression analysis data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Neural+computers%22">Neural computers</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Sensitivity+analysis%22">Sensitivity analysis</searchLink>
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  Label: Abstract
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  Data: The main purpose of the paper is to propose a traffic flow assignment method with matched selection of a multi-level road traffic network structure. Firstly, road connection 'design' parameters of traffic pricing are introduced, equilibrium conditions of users by traffic flow assignment are improved, design/pricing problem models of traffic network are constructed and surrounding semi-differentiable conditions of the reference point are considered and sensibility analysis methods of traffic equilibrium are designed. Then, sensitivity analysis is implemented combined with traffic flow assignment (TAPAS) output based on paired selection, it is shown how to use TAPAS to find a very precise equilibrium solution effectively and use the directional derivative. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of International Journal of Computers & Applications is the property of Taylor & Francis Ltd 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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        Value: 10.1080/1206212X.2018.1536393
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      – Code: eng
        Text: English
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        StartPage: 171
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      – SubjectFull: Traffic flow measurement
        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Regression analysis data processing
        Type: general
      – SubjectFull: Neural computers
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Sensitivity analysis
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      – TitleFull: Online traffic flow assignment method based on structure matching selection of multilevel road network.
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              Text: Feb2021
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              Y: 2021
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