Welcome 👋
💡 Background
A renowned German car company is in the process of establishing a platform for ridehailing mobility services. With this project, we contribute new insights to the manufacturers lack of operational know-how. More specifically, we analyze taxi data in chigaco, forecast demand in various temporal and spatial resolutions and ultimately formulate and solve an optimization problem for an optimal allocation of charging stations.
🚀 Usage
Make sure to configure your environment using the environment.yml
Just run the following command:
conda env create -f environment.yml
Furthermore, the creation of a data folder is necessary for the usage of all notebooks. For GitHub capacity reasons, this folder must be created manually.
Run the following steps:
- create a folder with the name "data" on the same level as the notebook folder is located.
- create a folder with the name "input" inside the data folder.
- copy the files "chicago-community-areas.geojson" as well as "holidays_illinois" from the repository to the "data" folder
- download the 2017 cab dataset from the following URL: "https://data.cityofchicago.org/Transportation/Taxi-Trips/wrvz-psew#column-menu"
- copy the downloaded cab data to the folder /data/input
🤝 Contributing
Contributions, issues and feature requests are welcome. Feel free to check issues page if you want to contribute. Check the contributing guide.
👤 Authors
Breyer, Roman - Ghoffrani, Davis - Mindl, Felix - Neumüller, Nikita - Zimmermann, Leon
Github: @romanbreyer, @dghoffra, @Freelix123 , @INikson, @Leon Zimmermann
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