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Demand prediction

While we provide the results for our demand prediction in the file demad_grid.csv, we detail here the procedure that was followed to generate such files:

  1. Download the historical trip data from Bluebikes and save it in a folder ‘bluebikes_data’ within the Preprocessing folder

  2. Rscript training_data.R (input bluebikes_data → outputs training_data.csv)

  3. python3 gccn_ddgf.py (input training_data.csv → outputs testing_data.csv)

  4. Rscript testing_data.R (input testing_data.csv → outputs demand_grid.csv)