Temporal Network of bicycle usage in Wroclaw (PL)

Application website

Github Large File Storage

For large file storeage we need to use Github LFS. Please follow the instruction and install it on your machine.

Preprocessing

1. To preprocess the dataset, you need to execute the following notebook preprocessing/preprocessing.ipynb

2. To execute the notebook, you need:

   • dataset (directory data with monthly rentals)
   • pickle with Wroclaw stations (preprocessing/stations.pkl)

3. Preprocessed data with csv extension will be saved in data_preprocessed directory.

   • File with map from id to station name will be saved in data_preprocessed/nodes.csv file.

4. Output sample:

bike_number, start_time, end_time, rental_place, return_place
57114, 2019-03-15 10:21:55, 2019-03-15 10:28:18, 195, 195 

where rental_place and return_place are id from map from file data_preprocessed/nodes.csv

Example nodes.csv:

value, name
0, Aleja Bielany

Groupby Intervals

Prepcosessed files can be used for generation of the dataset of 15 minutes intervals. For each interval we count number of trips between two places A and B. If a trip lasts more then 15 minutes it will be reordered in more then one interval (depending on its duration).

We trips in intervals as rows of a dataframe where each row describes interval start, its end, the number of trips made in this interval, rental place and return place

Example record:

interval_start, interval_end, number_of_trips, rental_place, return_place
2019-06-30, 15:30:00, 2019-06-30 15:45:00, 1, 97, 154

More details about this process in 15 Minutes Interval Parser Notebook where we present generation of gropedby intervals for an example month

To generate gropedby intervals for every month you need to preprocess every month (See Preprocessing). If you generate this files now you only need to execute generate_groupby_intervals_datasets.py which will create new folder called groupedby_intervals in with intervals for each month.

Convert Groupby Intervals to JSON format

When u generenate groupedby_interval files for each month, you can convert them into jsons so they can be easily used in the webapp. In order to do that execute the convert_bikes_usage_to_json.py file. Generated jasons will have a fomrat explained in Json-coding-edges section of a readme.

More details about this process in Convert 15 Minutes Interval To Json.ipynb where we presrent generation of gropedby intervals jsons for an example month

Metrics

List of metrics per station:

• Degree
• In Degree
• Out Degree
• PageRank

Station metrics are stored in ./metrics_json/historia_przejazdow_*.json and in ./metrics/historia_przejazdow_*.csv.

Network usage metrics:

• Bikes in use
• Total bikes available in current month
• Bike usage in percentage

Network usage is stored in ./bike_usage_json/historia_przejazdow_*.json and in ./bike_usage/historia_przejazdow_*.csv.

Path generation

To generate csv file with paths you need to have ./network/nodes_locations.csv file already available.

Execute notebook (watch out, it's memory consuming, and takes ages ~7h):

OSMNX - path generation.ipynb

File with paths should be separate files depends on accuracy:

./application/app_data/paths_reduced-e7.json
./application/app_data/paths_reduced-e8.json
./application/app_data/paths_reduced-e9.json
./application/app_data/paths_reduced-e10.json
./application/app_data/paths_reduced-e11.json
./application/app_data/paths_reduced-e12.json

Additionally, it will create file with nodes:

./application/public/nodes_list.json

WebApp installation

cd application
npm install
npm run start

Deployment

npm run deploy

Mapped network nodes on Wroclaw map

Json coding - edges

{"rental_place":"o","return_place":"d","number_of_trips":"c"}

{
    <day>: {
           <start_minute>: [
                    {
                            "o": <int>,
                            "d": <int>,
                            "c": <int>,
                    }
             ]
     }
}

Example:

{
   1: {
           375: [
                    {
                            "o": 1,
                            "d": 2,
                            "c": 15,
                    }
             ]
     }
}

Json coding - nodes metrics

{"node": "o", "degree": "k", "in_degree": "ik", "out_degree": "ok", "pagerank": "p"}

{
    <day>: {
           <start_minute>: [
                    {
                            "o": <int>,
                            "k": <int>,
                            "ik": <int>,
                            "ok": <int>,
                            "p": <float>,
                    }
             ]
     }
}

Example:

{
   "1": {
           "375": [
                    {
                            "o": 1,
                            "k": 2,
                            "ik": 1,
                            "ok": 1,
                            "p": 0.03103880199262298,
                    }
             ]
     }
}

Json coding - bikes usage

{"bikes_in_use": "bu", "bikes_total": "bt", "bikes_percentage": "bp"}

{
    <day>: {
           <start_minute>: [
                    {
                           "bu": <int>,
                           "bt": <int>,
                           "bp": <float>,
                    }
             ]
     }
}

Example:

{
   "1": {
           "375": [
                    {
                           "bu": 366,
                           "bt": 1027,
                           "bp": 0.3563777994157741,
                    }
             ]
     }
}

Contributors ✨

Thanks goes to these wonderful people (emoji key):

Kemal Erdem 🎨 💻 📖 👀

Piotr Mazurek 💻 📖 👀

dkunc 💻 📖 👀

NorbertRop 💻 📖 👀

This project follows the all-contributors specification. Contributions of any kind welcome!