The ML-airport-data-services software is developed to provide common code used throughout the ML-airport suite of software. The software is built in python and leverages open-source libraries kedro, scikitlearn, MLFlow, and others. The software provides useful functions for development of pipelines including data query and save, data engineering, and data science.
ML-airport-data-services provides general-purpose code to several ML models that are tied together by the Airport Surface Model Orchestrator shown below.
The Airport Surface Model Orchestrator runs at fixed intervals and is driven from real-time Fused FAA System Wide Information Management (SWIM) data feeds and also pulls in additional D-ATIS airport configuration data and weather data. The data input manager within the Orchestrator prepares data for each ML service and data is exchanged between the Orchestrator and the ML services via API interfaces.
The input data sources for the individual ML prediction services are shown in the below diagram which also illustrates dependencies between the individual ML services.
The ML Airport Surface Model forms the building blocks of a cloud based predictive engine that alerts flight operators to pre-departure Trajectory Option Set (TOS) reroute opportunities within the terminal airspace. The ML Airport Surface Model was designed to be a scalable replacement for the capabilities provided by NASA's Surface Trajectory Based Operations (STBO) subsystem, which is a component of the fielded ATD2 Phase 3 System in the North Texas Metroplex. The STBO subsystem relies heavily upon detailed adaptation, which defines the physical constraints and encodes Subject Matter Expert knowledge within decision trees, and creates a costly bottleneck to scaling the pre-departure TOS digital reroute capability across the National Airspace System.
Data Services is part of a suite of softwares designed to model the airport surface:
- ML-airport Airport Configuration Model
- ML-airport Arrival Runway Model
- ML-airport Departure Runway Model
- ML-airport Taxi-In Model
- ML-airport Taxi-Out Model
- ML-airport Estimated-On-Time Model
- ML-airport Data Services
This repository does not contain a standalone project. Code in this repository has been packaged to facilitate usage in environments for development and deployment of predictive models. To use functionality in this repository in another project, it can be installed in the project's anaconda environment via a pip install of the packaged code available in the appropriate git repository.
Corresponding lines can also be added to the anaconda environment specification file.
For example, see the conda.yaml file in the ML-airport-taxi-in repository.
This repository also contains some general MLflow and Kedro functionality.
The kedro_extensions/io/sqlfile_dataset.py file contains code defining the SQLQueryFileDataSet and SQLQueryFileChunkedDataSet data set types for use in Kedro data catalogs.
These enable users to specify a SQL file for executing parameterized queries and for breaking queries that cover long time periods in to time "chunks" that are less burdensome on the database responding to the queries.
The mlflow_utils/mlflow_utils.py file contains some utility functions for working with MLflow and the MLflow server.
For example, some of these use the MLflow API to get the most recently registered model of a certain type.
Several files contain classes that extend the scikit-learn base Pipeline or BaseEstimator or TransformerMixin classes and can be used when creating scikit-learn ColumnTransformer or Pipeline objects that incorporate fairly sophisticated feature calculations along with prediction models.
The FilterPipeline.py file implements the FilterPipeline class, a noteworthy child class of sklearn.pipeline.Pipeline.
It is a wrapper class that applies consistent filtering criteria for rows when doing model fitting and prediction.
The build_swim_eta function in swim_based_eta.py contains rules to select the "best" ETA from candidate ETAs available in SWIM (i.e., TFMS ETA, TBFM ETA, TBFM STA).
The time_series_sampling.py file contains some functions that are useful for time series data.
For example, the _count_in_ts function computes a time series representation of expected counts, given frequently-updated predictions of some future event.
The data_services_utils.py file contains some functions that are useful for lining up the data to match what the live nas-fuser/nas-model system does. For example, there is an add_flight_timeout function that times out a flight after not receiving a final time after a pre-configured amount of time.
The compute_surface_counts.py file computes arrival, departure, and total counts of flights on the surface of the airport at the time of landing for each arrival in the dataset.
The gate_occupied_at_landing_proxy.py file computes whether or not the assigned gate is occupied at the time of landing.
The ML-airport-data-services code is released under the NASA Open Source Agreement Version 1.3 license
The “ATD-2 Machine Learning Airport Surface Model: Data Services” software also makes use of the following 3rd party Open Source software:
- scp - under GNU Library or Lesser General Public License (LGPL) - Copyright (C) 2007 Free Software Foundation, Inc. https://fsf.org/
- paramiko - under GNU Library or Lesser General Public License (LGPL) - Copyright (C) 2007 Free Software Foundation, Inc. https://fsf.org/
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