roster-generator 0.3.6

Realistic flight schedule generation from empirical operational records.

ROSTER - Realistic Operational Schedules Through Empirical Records

ROSTER is a Python package for generating realistic flight schedules from historical data. It is designed for ATM researchers that require synthetic schedules (with or without modification) for simulation purposes.

The goal of ROSTER is to provide a transparent and reproducible pipeline for building these synthetic schedules, which preserve important operational structure: airline and wake-category mixes, airport and route usage, scheduled flight times and turnarounds, and fleet initial conditions.

Installation

ROSTER requires Python 3.12 or newer. Install the latest released package with:

python -m pip install roster-generator

The installed import package is named roster_generator:

import roster_generator

For local development from a source checkout:

cd roster
python -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"

Runtime dependencies are declared in pyproject.toml and include pandas, numpy, airportsdata, aircraft-list, and pytz. Development and release tools, including pytest, build, and twine, are available through the optional dev extra.

Input Data

The main ROSTER pipeline expects a cleaned flight schedule with this normalized schema:

DEP_ICAO, ARR_ICAO, STD_REFTZ, STA_REFTZ, ATD_REFTZ, ATA_REFTZ,
AC_OPER, AC_REG, AC_WAKE

Any dataset with those columns can be used directly as schedule_file in PipelineConfig. The built-in cleaning step is optional: it is a convenience utility for converting EUROCONTROL-style flight records into the normalized ROSTER input format.

For that optional cleaner, the raw EUROCONTROL-style file must include, at minimum, the following source columns:

ADEP, ADES, FILED OFF BLOCK TIME, FILED ARRIVAL TIME,
ACTUAL OFF BLOCK TIME, ACTUAL ARRIVAL TIME,
AC Type, AC Operator, AC Registration

The cleaner validates airport ICAO codes, parses timestamps, adds ICAO wake turbulence categories from aircraft type data, and writes the normalized schema above.

Using BTS Data

ROSTER can also clean BTS on-time performance downloads into the same normalized schema. Download the BTS source data yourself, extract the CSV files into BTS/, and run the BTS cleaning tutorial:

mkdir -p BTS
# Put the extracted BTS on-time schedule CSV and aircraft inventory CSV in BTS/.
# The tutorial auto-detects BTS/on_time.csv, BTS/aircraft.csv, or the default
# extracted BTS filenames shown below.
python tutorials/tutorial_bts_cleaning.py
python main.py --schedule-file input/bts_clean.csv --seed 42 --suffix bts

You can also call the cleaner directly by passing both extracted CSV paths:

python -m roster_generator.data_cleaning.clean_bts \
  BTS/On_Time_Reporting_Carrier_On_Time_Performance_\(1987_present\)_2024_12.csv \
  BTS/T_F41SCHEDULE_B43.csv \
  --output input/bts_clean.csv

For example, from a user workspace such as /home/josu/Escritorio/main_pc/roster-workspace, the command above expects the extracted CSV files under /home/josu/Escritorio/main_pc/roster-workspace/BTS.

The two required BTS CSV inputs are:

Need	BTS table	Where to get it	Filename
Required	Reporting Carrier On-Time Performance (1987-present) schedule	https://transtats.bts.gov/PREZIP/	Extracted CSV, for example On_Time_Reporting_Carrier_On_Time_Performance_(1987_present)_2024_12.csv
Required	Schedule B-43 aircraft inventory	https://www.transtats.bts.gov/DL_SelectFields.aspx?QO_fu146_anzr=Nv4+Pn44vr4+Sv0n0pvny&gnoyr_VQ=GEH	Extracted CSV, for example T_F41SCHEDULE_B43.csv

BTS on-time field definitions are documented at https://transtats.bts.gov/Fields.asp?gnoyr_VQ=FGJ. BTS Schedule B-43 fields are documented at https://www.transtats.bts.gov/Fields.asp?gnoyr_VQ=GEH.

ROSTER does not download, cache, or read zipped BTS support data. Both the on-time schedule CSV and the Schedule B-43 aircraft inventory CSV are mandatory.

BTS stores its scheduled and actual clock times as local hhmm values. The BTS cleaner localizes those times using each airport timezone, converts them into the normalized ROSTER timestamp convention, and writes the standard cleaned columns shown above.

Quick Start

Run the full tutorial pipeline from a source checkout:

python tutorials/tutorial_pipeline.py --seed 42 --suffix demo

The seed controls stochastic sampling. The optional suffix is appended to generated files, so --suffix demo produces outputs such as schedule_demo.csv.

Pipeline

The standard tutorial workflow runs the following stages:

1. Optionally clean historical flight records into the normalized ROSTER schema.
2. Build empirical Markov transition tables and sample fleet initial conditions.
3. Analyze scheduled turnaround and flight-time distributions.
4. Generate auxiliary simulator input files for airlines, airports, fleet, and routes.
5. Generate a synthetic schedule through greedy forward construction with airport capacity checks.

Intermediate analysis files are written under computed/, including initial_conditions, markov, scheduled flight-time distributions, and turnaround profiles. Simulator-facing outputs are written under output/, including airlines, airports, fleet, routes, phys_ta, and schedule.

Configuration

Runtime window behavior can be configured in tutorials/params.yaml:

REFTZ: UTC
WINDOW_START: "00:00"
WINDOW_LENGTH_HOURS: 24
ACTUAL_TIMES: false

REFTZ defines the reference timezone used for time-of-day and day-boundary logic. WINDOW_START and WINDOW_LENGTH_HOURS define the simulated operating window. ACTUAL_TIMES controls whether actual timestamp columns are required and used by stages that support them.

Programmatic workflows use roster_generator.PipelineConfig to define input paths, output paths, random seed, suffix, time-window settings, and optional manipulation callbacks.

Schedule manipulation

ROSTER supports controlled scenario manipulation without editing the generated analysis tables by hand:

• manipulation_fn modifies scalar distribution parameters at runtime, such as turnaround distributions, fleet-size parameters, route durations, physical turnaround minima, and prior-day probabilities.
• markov_manipulation_fn receives a MarkovContext and reweights existing destination probabilities before each Markov row is normalized.

See tutorials/tutorial_manipulation.py for a worked example of both hooks.

Some Features Of ROSTER

• Written in Python 3 and installable with pip.
• Reproducible stochastic generation through explicit random seeds.
• Empirical Markov transition models for aircraft continuation behavior.
• Synthetic fleet initial-condition sampling from historical records.
• Reference-timezone operating windows with configurable start time and length.
• Wake-category handling based on aircraft type data.
• Scheduled turnaround and flight-time distribution analysis.
• Airport capacity-aware schedule construction.
• CSV outputs suitable for downstream simulation workflows.
• Unit tests covering cleaning, configuration, distributions, Markov models, auxiliary files, and schedule generation.

Testing

Run the test suite from the project root:

python -m pytest

Contributions

Contributions are welcome from researchers and developers. Please keep changes focused, add or update tests for behavioural changes.

License

ROSTER is distributed under the GNU General Public License v3. See LICENSE for the full license text.