Data underlying the publication: A bi-level framework for heterogeneous fleet sizing of ride-hailing services considering an approximated mixed equilibrium between automated and non-automated traffic

This dataset supports the research project titled <em>"A Bi-Level Framework for Heterogeneous Fleet Sizing of Ride-Hailing Services Considering an Approximated Mixed Equilibrium Between Automated and Non-Automated Traffic."</em> The dataset focuses on optimizing fleet sizing in mixed-traffic environments and incorporates a bi-level modelling approach to analyze strategic and operational interactions between automated and non-automated vehicles. The original dataset for the Delft case study has been published and is accessible via the DOI: https://doi.org/10.13140/RG.2.2.11097.83043.<br>Contents of the DatasetThis dataset includes:Delft network and mobility data.Toy network and mobility data.Experimental results.<br><br>本数据集支撑题为《考虑自动驾驶与非自动驾驶交通近似混合均衡的网约车服务异构车队规模优化双层框架（Bi-Level Framework）》的研究项目。本数据集聚焦混合交通环境下的车队规模优化问题，采用双层框架建模方法，分析自动驾驶与非自动驾驶车辆间的战略与运营交互关系。代尔夫特（Delft）案例研究的原始数据集已公开发布，可通过DOI链接https://doi.org/10.13140/RG.2.2.11097.83043获取。数据集包含以下内容：代尔夫特路网与出行数据、小型测试路网（Toy network）与出行数据、实验结果。