Equilibrium-Traffic-Networks

This repository contains three graph 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.It is recommended to use the pytorch geometric (pyg) datasets (added in the latest update) to avoid potential compatibility issues with different versions of dgl. The underlying data is the same and the existing code base works with new datasets. You only need to make sure the dataset names mathc the names in the config files.Detailed information can be found in the "Metadata.md" file.ReferencesA hybrid deep-learning-metaheuristic framework for bi-level network design problemsGitHub Repository: HDLMF_GIN-GA

本仓库包含为Bahman Madadi与Gonçalo H. de Almeida Correia发表于《专家系统及其应用》（原刊名：Expert Systems with Applications）的研究《双层网络设计问题的混合深度学习-元启发式框架》（原标题：A hybrid deep-learning-metaheuristic framework for bi-level network design problems）所生成的3个图数据集。这些数据集用于训练和评估模型，以求解来自知名"科研用交通网络"仓库的3个交通网络（苏福尔斯、马萨诸塞州东部、阿纳海姆）上的用户均衡（User Equilibrium，UE）问题。建议使用最新更新中新增的PyTorch几何（PyTorch Geometric，pyg）数据集，以避免与不同版本的dgl产生潜在兼容性问题。二者底层数据完全一致，现有代码库可兼容新增数据集，仅需确保数据集名称与配置文件中的名称匹配即可。详细信息可参阅"Metadata.md"文件。
参考文献
《双层网络设计问题的混合深度学习-元启发式框架》（原标题：A hybrid deep-learning-metaheuristic framework for bi-level network design problems）
GitHub仓库：HDLMF_GIN-GA