Equilibrium-Traffic-Networks

This repository contains three DGL datasets generated for the study "A hybrid deep-learning-metaheuristic framework for bi-level network design problems" by Bahman Madadi and Gonçalo H. de Almeida Correia, published in Expert Systems with Applications. The datasets are generated and used to train and evaluate models for solving the User Equilibrium (UE) problem on three transportation networks (Sioux-Falls, Eastern-Massachusetts, and Anaheim) from the well-known "transport networks for research" repository.
Detailed information can be found in the "Metadata.md" file.

Note: This dataset is maintained at Figshare (https://doi.org/10.6084/m9.figshare.27889251).

References
Article: A hybrid deep-learning-metaheuristic framework for bi-level network design problems (https://doi.org/10.1016/j.eswa.2023.122814)
GitHub Repository: HDLMF_GIN-GA (https://github.com/bahmanmdd/HDLMF_GIN-GA)
Primary Figshare data repository: https://doi.org/10.6084/m9.figshare.27889251

本仓库收录了由 Bahman Madadi 和 Gonçalo H. de Almeida Correia 共同撰写的、发表于《专家系统与应用》杂志的论文《一种用于双层网络设计问题的混合深度学习-元启发式框架》（A hybrid deep-learning-metaheuristic framework for bi-level network design problems）所生成与使用的三个 DGL 数据集。这些数据集经过生成，并用于训练与评估模型，旨在解决三个知名交通网络（Sioux-Falls、Eastern-Massachusetts 和 Anaheim）中的用户均衡（UE）问题。详细资料可查阅“Metadata.md”文件。

备注：本数据集由 Figshare 维护（https://doi.org/10.6084/m9.figshare.27889251）。

参考文献

文章：一种用于双层网络设计问题的混合深度学习-元启发式框架（A hybrid deep-learning-metaheuristic framework for bi-level network design problems）（https://doi.org/10.1016/j.eswa.2023.122814）

GitHub 仓库：HDLMF_GIN-GA（https://github.com/bahmanmdd/HDLMF_GIN-GA）

主要 Figshare 数据仓库：https://doi.org/10.6084/m9.figshare.27889251