Transportation Research Part C

This dataset contains information about ride-sourcing rides from 06-02-2016 through 04-13-2017 provided by Ride Austin through the website https://data.world/ride-austin/ride-austin-june-6-april-13

The attached files include the R code that executes the analysis in the paper and the subset of the data used in the paper that is public. With the public data only, the code will execute some of the analysis fully and produce error messages where non-public data are needed. Proprietary data used in the analysis may be purchased from IHS/Polk (https://ishmarkit.com/products/products/automotive-market-data-analysis.html) and Ward's Automotive (https://subscribers.wardsintelligence.com/data-center) to run the full analysis.

This repository contains the code developed for determining the optimal size of charging infrastructure for a network of airports, to service a fleet of electric aircraft operating with a battery swapping system. It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

This repository contains the code developed for scheduling a fleet of electric towing vehicles at an airport. It accounts for the uncertainty in flight arrival/departure times by both anticipating to it (using delay probability densities) and reacting to it when it occurs. It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

This repository contains the code developed for scheduling simulations for electric towing vehicles (ETVs). It is applied to Amsterdam Airport Schiphol. This code was developed at Delft University of Technology, as part of Simon van Oosterom's PhD Thesis project (2025). It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

This repository contains the code developed for determining the optimal size of charging infrastructure for a network of airports, to service a fleet of electric aircraft operating with a battery swapping system. It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

This repository contains the code developed for scheduling a fleet of electric towing vehicles at an airport. It accounts for the uncertainty in flight arrival/departure times by both anticipating to it (using delay probability densities) and reacting to it when it occurs. It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

This repository contains the code developed for scheduling simulations for electric towing vehicles (ETVs). It is applied to Amsterdam Airport Schiphol. This code was developed at Delft University of Technology, as part of Simon van Oosterom's PhD Thesis project (2025). It is being made public both to act as supplementary data for publications and the PhD thesis of Simon van Oosterom, and in order for other researchers to use this repository in their own work.

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.

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.