TY - DATA
T1 - 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
PY - 2024/12/09
AU - Qiaochu Fan
AU - J. Theresia van Essen
AU - Gonçalo Homem de Almeida Correia
UR - 
DO - 10.4121/cf19bfc7-d032-47f6-9828-fe20f8f38f96.v1
KW - Fleet sizing
KW - Shared automated vehicles
KW - Non-linear demand
KW - Mode choice
KW - Traffic congestion
N2 - <p>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: <a href="https://doi.org/10.13140/RG.2.2.11097.83043" target="_blank">https://doi.org/10.13140/RG.2.2.11097.83043</a>.</p><p><br></p><p>This dataset includes:</p><ul><li>Delft Network and Mobility Data.</li><li>Toy Network and Mobility Data.</li><li>Experimental Results.</li></ul><p><br></p><p><br></p><p><br></p>
ER -