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Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach.

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Title: Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach.
Authors: Ying, Chengshuo1,2, Chow, Andy H.F.1,3, andychow@cityu.edu.hk, Yan, Yimo4, Kuo, Yong-Hong4, Wang, Shouyang2,5,6
Source: Transportation Research Part B: Methodological; Oct2024, Vol. 188, pN.PAG-N.PAG, 1p
Database: Applied Science & Technology Source
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Header DbId: aci
DbLabel: Applied Science & Technology Source
An: 179602893
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: <searchLink fieldCode="JN" term="%22Transportation+Research+Part+B%3A+Methodological%22">Transportation Research Part B: Methodological</searchLink>; Oct2024, Vol. 188, pN.PAG-N.PAG, 1p
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=aci&AN=179602893
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      – Type: doi
        Value: 10.1016/j.trb.2024.103067
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      – Code: eng
        Text: English
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      – TitleFull: Adaptive rescheduling of rail transit services with short-turnings under disruptions via a multi-agent deep reinforcement learning approach.
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            NameFull: Ying, Chengshuo
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            NameFull: Chow, Andy H.F.
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              M: 10
              Text: Oct2024
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              Y: 2024
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              Value: 188
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