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                <identifier>oai:data.mendeley.com/nw7tpmr23w.1</identifier>
                <datestamp>2026-09-09T07:14:38Z</datestamp>
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    <dc:creator>heidari, sadegh</dc:creator>
    <dc:title>Datasets and Implementation Code for Resilient Real-Time Metro Rerouting Using Adaptive Bidirectional Heuristics and Graph Neural Networks</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This repository contains the dataset, source codes, trained graph models, and experimental outputs used in the research article entitled:

&quot;Resilient Real-Time Metro Rerouting with Adaptive Bidirectional Heuristics and Graph Neural Network-Based Edge Prediction&quot;

The provided materials support the implementation and evaluation of the proposed Adaptive Bidirectional Heuristic (ABH) routing framework integrated with Graph Neural Network (GNN)-based edge weight prediction.

The repository includes metro network data, congestion information, routing algorithm implementations, GNN training codes, trained graph models, and experimental evaluation scripts.

The provided files enable reproduction of the reported experiments and verification of the obtained results.</dc:description>
    <dc:subject>Computer Science</dc:subject>
    <dc:subject>Artificial Intelligence</dc:subject>
    <dc:subject>Smart Transportation</dc:subject>
    <dc:contributor id="https://orcid.org/0000-0002-5868-6100">Yaeghoobi, Kaebeh</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/nw7tpmr23w.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/nw7tpmr23w.1</dc:identifier>
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    <dc:date>2026-09-09T07:14:38Z</dc:date>
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