Supporting data for "PDE-constrained traffic assignment optimization for air quality improvement with surrogate models"
收藏DataCite Commons2024-09-12 更新2025-04-16 收录
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https://datahub.hku.hk/articles/dataset/Supporting_data_for_PDE-constrained_traffic_assignment_optimization_for_air_quality_improvement_with_surrogate_models_/26886361/1
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资源简介：
The codes and data correspond to the paper "Mei, Di, and Chun-Ho Liu. "Bi-objective optimization of traffic assignment with air quality consideration via CFD-based surrogate model." Sustainable Cities and Society 91 (2023): 104425." All the research works in my thesis are based on this coding framework.The code conducst bi-objective optimization to minimize both travel time and CO concentration for a urban traffic network. The CO concentration is predicted via the surrogate model, Gaussian process regression, which is extablished from CFD simulations on a given dataset of decision variables. In the filefolder, *.npy indicates the files of data (e.g., sampled CO concentration), .pynb represents the optimization algorithm writen by python.<br><br>
提供机构：
HKU Data Repository
创建时间：
2024-09-12
