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
doi: 10.4121/cf19bfc7-d032-47f6-9828-fe20f8f38f96
This dataset supports the research project titled "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." 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.
This dataset includes:
- Delft Network and Mobility Data.
- Toy Network and Mobility Data.
- Experimental Results.
- 2024-12-09 first online, published, posted
TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft Institute of Applied Mathematics
DATA
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README.md - 445,285 bytesMD5:
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