Instances for "Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models"

Subject to MIT License

Parameter values and error terms for all twenty instances in each of the five datasets discussed in "Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models". Instances are designed to be loaded using one of the methods provided at https://github.com/StevenLamontagne/EVChargingStationModel/. However, instances are in valid JSON format (the .pickle files must be unpickled first via, e.g. , the standard pickle library in Python). Documentation is included which explains the notation and usage of each key in the resulting dictionaries. The .pickle files include error terms, and are suitable for either maximum covering models, maximum capture problems, or bilevel models. The .json files precompute the coverage of each station, and are thus only suitable for maximum covering models (however, they load significantly faster for such problems). As the data are synthetic, the spatial and temporal coverage are too, but they are based closely on data from the Trois-Rivières area of Québec, Canada, electric vehicle charging station data from 2014 to 2020. Associated pre-print: Lamontagne, S., Carvalho, M., Frejinger, E., Gendron, B., Anjos, M. F., Atallah, R. (2022). Optimising Electric Vehicle Charging Station Placement using Advanced Discrete Choice Models. arXiv preprint arXiv:2206.11165.