Bysykkel Oslo

Analyse the trip data from the bicycle sharing scheme in Oslo and create a machine-learning-based net demand predictor.

Prerequisites

The code has been developed under Windows using the Anaconda Python distribution, with Python version 3.6. Package dependencies for Anaconda and pip can be found in the requirements_*.txt files in the repository.

Data sources and processing pipeline

Preparations:

1. Download the bicycle trip data as csv files from https://developer.oslobysykkel.no/data
2. Get a client ID for the Norwegian Meteorological office's Frost API, see the instructions here; assign the string to the variable client_id in the file frost_account.py in the src/data directory.

data directory:	raw (10_get_data.py)	interim (20_process_raw2interim.py)	processed (30_process_interim2processed.py)
bike	csv.gz files dowloaded manually	trips.feather: cleaned negative time trips; duration limit; sorted	obs_netflow.csv/feather: net flow data
meteorological	downloaded from API	blindern_interim.csv/feather: joined with days.csv+solar elevation	(included in the above)
calendar	-	days.csv with delta time; hour is local time; no non-service hours	(included in the above)
stations	-		stations.csv: first day/time each station is used (written by 31_station_start2processed)

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements_conda.txt <- The requirements files for reproducing the environment,
├── requirements_pip.txt   <- conda is leading.
│
├── setup.py           <- makes project pip installable (pip install -e .) so src can be imported
└── src                <- Source code for use in this project.
    ├── __init__.py    <- Makes src a Python module
    │
    ├── data           <- Scripts to download or generate data
    │   └── make_dataset.py
    │
    ├── models         <- Scripts to train models and then use trained models to make
    │   │                 predictions
    │   ├── predict_model.py
    │   └── train_model.py
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations
        └── visualize.py

---