The project aims at elaborating the parking data, creating the necessary datasets, visualizations, and services for historical, actual, and forecasted parking data.

The data management consists of the following activities

• download the historical data and create the dataset on the platform Datalake
• aggregate the data according by the calendar day and parking for further analysis, saving the dataset in the Datalake and in the database
• continuously download and update the latest data to have it available in the DB for exposure as API and visualizations
• process data in order to create forecast models for the parkings and continously predict the occupation rate for the next 24 hours

The data management operations are captured by the two procedures:

• "extract-parkings-latest": download the latest data and store it in datalake and PostgreSQL DB. The operation is scheduled eacvh 10 minutes.
• "data-update-pipeline": a pipeline that downloads the storical data, aggregates it, extract the basic parking info, creates the predicition models and applies them to forecast next day data. The created data (parkings, aggregated occupation, forecast) is stored in the datalake and DB. The pipeline is then scheduled for execution every day.

To run / schedule the procedures it is possible to follow the "parcheggi" Jupyter notebook. Please note that to work with the associated source code it is necessary to provide a set of secrets, specifically

• the GIT_TOKEN to access the private repo to clone the code (necessary also for executions)
• the DB_USERNAME and DB_PASSWORD to write the data to the database.

It is possible to expose APIs for the collected data using PostgREST operator. In this way the DB tables may be accessed using the corresponding APIs and parameters.

Please note, that in this scenario the data is written to the public schema of the digitalhub database. It is recommended to expose explicitly only the specific datasets via dedicated schema and views. For this the following stepls should be performed:

• create a new api schema for your database (it is possible to use e.g., SQLPad workspace from Coder).

create schema if not exists api;

• create views for the stored tables (e.g., using the SQLPad tool as before)

create or replace view api.parkings as select * from public.parkings;
create or replace view api.parking_data_aggregated as select * from public.parking_data_aggregated;
create or replace view api.parkings_latest as select * from public.parkings_latest;
create or replace view api.parkings_prediction as select * from public.parkings_prediction;

• create a PostgreSQL instance using the KRM UI. Specify api as exposed schema and an appropriate user (e.g., default read-only user for the digitalhub database).

• expose the service using API Gateway in KRM). To access the data represented in the specific view, use the calls <host>/<view_name>. Please take into account that the propagation of the DNS name may require some time.

The folder dashboards contains the dashboards that can be used for the visualization of the parking data. In particular

• grafana_parkings.json defines a Grafana dashboard with the parking data. Import it in the grafana instance of interest and replace the UID of the digitalhub datasource with the one configured in your instance.

Based on the historical data collected the project allows for training a parking occupancy prediction model and expose it as a service. The training code is managed by the train-multimodel operation (see src/train_multimodel.py for details) and creates a NBEATS DL global model using darts framework. The model is then deployed using Nuclio serverless platform with a custom service for this model to make predictions. The "parcheggi" contains the necessary details for declaring a serving functions, adding the trained model and deploying it on the platform using the custom server implementation (see predictor_serving.py for further details).