Data underlying the publication: Optimising fleet sizing and management of shared automated vehicle (SAV) services: A mixed-integer programming approach integrating endogenous demand, congestion effects, and accept/reject mechanism impacts

This dataset supports the research project titled <em>"Optimising Fleet Sizing and Management of Shared Automated Vehicle (SAV) Services: A Mixed-Integer Programming Approach Integrating Endogenous Demand, Congestion Effects, and Accept/Reject Mechanism Impacts."</em> The study explores optimization strategies for fleet sizing and management of SAVs while accounting for endogenous demand, traffic congestion, and accept/reject mechanisms. The mixed-integer programming model integrates these elements to provide insights into fleet operations and system efficiency. 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>This dataset includes:Delft Network and Mobility Data.Toy Network and Mobility Data.Experimental Results.<br><br><br>

本数据集可支撑题为《共享自动驾驶汽车（Shared Automated Vehicle, SAV）服务的车队规模优化与管理：整合内生需求、拥堵效应与接纳/拒绝机制影响的混合整数规划方法》的研究项目。该研究围绕共享自动驾驶汽车车队的规模优化与运营管理策略展开探索，同时考量内生需求、交通拥堵以及接纳/拒绝机制的影响。本研究所构建的混合整数规划模型整合了上述各类要素，可为车队运营与系统效率优化提供科学洞察与决策参考。代尔夫特（Delft）案例研究的原始数据集已公开发布，可通过以下数字对象标识符（DOI）获取：https://doi.org/10.13140/RG.2.2.11097.83043。
本数据集包含：代尔夫特交通网络与出行数据、玩具级交通网络与出行数据、实验结果。