Group:

• Erika Gutierrez
• Javier Roset Cardona

In this project we create station level hourly demand forecasting models for BiciMAD, Madrid's bikeshare system. We first aggregated data from multiple open source online data portals into a cohesive dataset. The dataset contains hourly level entries from January 1st, 2019 to December 31st, 2022 for all stations in the bikeshare system resulting in 8.1 million rows. We then test the performance of an XGBOOST and a Graph Convolutional Neural Network on this dataset to generate bike demand estimates.

A summary of our project can be found here.

We have three main folders: data, models, and processing

In each of these folders there is a development folder that contains the notebooks we used to explore the data and test code that would be used in our actual ML pipeline.

data

• The folder downloading contains the code we use to download data from their respective online sources in their raw form.
• The folder processing contains the code we use to take this raw data, clean it, and combine it.

processing

• The folder Feature Engineering contains the code we used to create the final dataframe of our features and targets. Also contains the script to collect the data used to populate our adjancency matrix.

models

• contains the various scripts used for creating, training, testing, and assessing our models. The baseline_modeling.py contains the code for the baseline and XGBOOST model and GNN_Main.py contains the code for the GCNN model.