Ensuring a shared bike is readily available can reduce demand for less climate-friendly transportation options. Explore how to model the relationship between bike usage and points of interest (POIs) to identify the best locations for new shared-bike stands.
Author(s):
- Felix Wagner, Technische Universität Berlin & Mercator Research Institute on Global Commons and Climate Change, wagner@mcc-berlin.net
- Florian Nachtigall, Technische Universität Berlin & Mercator Research Institute on Global Commons and Climate Change, nachtigall@tu-berlin.de
- Shafat Rahman, Climate Change AI, shafatrahman@climatechange.ai
- Konstantin Klemmer, Microsoft Research & Climate Change AI, konstantin@climatechange.ai
Originally presented at Climate Change AI Summer School 2023.
We recommend executing this notebook in a Colab environment to gain access to GPUs and to manage all necessary dependencies.
We estimate that this tutorial will take around 20 minutes to execute from end-to-end.
Please refer to these GitHub instructions to open a pull request via the "fork and pull request" workflow.
Pull requests will be reviewed by members of the Climate Change AI Tutorials team for relevance, accuracy, and conciseness.
Check out the tutorials page on our website for a full list of tutorials demonstrating how AI can be used to tackle problems related to climate change.
Usage of this tutorial is subject to the MIT License.
Wagner, F., Nachtigall, F., Rahman, S., & Klemmer, K. (2024). Predicting Mobility Demand from Urban Features [Tutorial]. In Climate Change AI Summer School. Climate Change AI. https://doi.org/10.5281/zenodo.11619223
@misc{wagner2024predicting,
title={Predicting Mobility Demand from Urban Features},
author={Wagner, Felix and Nachtigall, Florian and Rahman, Shafat and Klemmer, Konstantin},
year={2024},
organization={Climate Change AI},
type={Tutorial},
doi={https://doi.org/10.5281/zenodo.11619223},
booktitle={Climate Change AI Summer School},
howpublished={\url{https://github.com/climatechange-ai-tutorials/mobility-demand}}
}