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Multi-agent deep reinforcement learning-based decentralized adaptive traffic signal control for corridor-level safety optimization.

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Bibliographic Details
Title: Multi-agent deep reinforcement learning-based decentralized adaptive traffic signal control for corridor-level safety optimization.
Authors: Reyad, Passant1 (AUTHOR) passant.ali.ahmed@gmail.com, Sayed, Tarek1 (AUTHOR)
Source: Canadian Journal of Civil Engineering. 1/9/2026, Vol. 53, p1-19. 19p.
Database: Environment Complete
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Header DbId: eih
DbLabel: Environment Complete
An: 190982125
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Multi-agent deep reinforcement learning-based decentralized adaptive traffic signal control for corridor-level safety optimization.
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  Data: <searchLink fieldCode="AR" term="%22Reyad%2C+Passant%22">Reyad, Passant</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> passant.ali.ahmed@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Sayed%2C+Tarek%22">Sayed, Tarek</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Canadian+Journal+of+Civil+Engineering%22">Canadian Journal of Civil Engineering</searchLink>. 1/9/2026, Vol. 53, p1-19. 19p.
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1139/cjce-2025-0202
    Languages:
      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1
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      – TitleFull: Multi-agent deep reinforcement learning-based decentralized adaptive traffic signal control for corridor-level safety optimization.
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            NameFull: Reyad, Passant
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            NameFull: Sayed, Tarek
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            – D: 09
              M: 01
              Text: 1/9/2026
              Type: published
              Y: 2026
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              Value: 53
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            – TitleFull: Canadian Journal of Civil Engineering
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