RouteE-Transit

RouteE-Transit#

RouteE-Transit is a Python package that provides comprehensive tools for predicting energy consumption of transit bus systems. Built on top of NREL's RouteE-Powertrain package, RouteE-Transit focuses on transit bus applications, predicting energy consumption for buses based on GTFS data.

The package enables users to work with pre-trained transit bus energy models or their own RouteE-Powertrain models based on real-world telematics data or simulation outputs. RouteE-Transit models predict vehicle energy consumption for transit trips based on factors such as road grade, estimated vehicle speed, and distance.

Key Features#

  • GTFS Integration: Works with General Transit Feed Specification (GTFS) data to analyze entire transit networks

  • Powertrain Agnostic: Support for various vehicle types including diesel, hybrid, and battery-electric buses

  • Fleet-wide Analysis: Predict energy consumption for individual trips, complete bus blocks, or entire bus fleets

Quickstart#

To install RouteE-Transit, see Installation.

from routee.transit import GTFSEnergyPredictor

# Create predictor and run complete pipeline
predictor = GTFSEnergyPredictor(
    gtfs_path="path/to/gtfs",
    # depot_path is optional - defaults to NTD depot locations from:
    # https://data.transportation.gov/stories/s/gd62-jzra
)

# Run the complete workflow with a single method call
trip_results = predictor.run(
    vehicle_models="Transit_Bus_Battery_Electric",
    date="2023/08/02",  # Optional, filter to specific date
    routes=["205"],     # Optional, filter to specific routes
    add_hvac=True,      # Include HVAC energy impacts
    output_dir="reports/output",
)

For a full example, see Utah Transit Agency Example. That example can also be run as a script with python scripts/single_agency_full_analysis.py.

Alternative: Step-by-Step Processing#

For more control over the workflow, you can invoke each processing step individually:

predictor = GTFSEnergyPredictor(gtfs_path="path/to/gtfs")

# Load and process GTFS data
predictor.load_gtfs_data()
predictor.filter_trips(date="2023/08/02", routes=["205"])

# Add deadhead trips (optional)
predictor.add_mid_block_deadhead()
predictor.add_depot_deadhead()

# Match to road network and add grade
predictor.match_shapes_to_network()
predictor.add_road_grade()

# Predict energy consumption
predictor.predict_energy(
    vehicle_models=["Transit_Bus_Battery_Electric", "Transit_Bus_Diesel"],
    add_hvac=True,
)

# Access results
trip_results = predictor.get_trip_predictions()
link_results = predictor.get_link_predictions()

Available Models#

Pretrained transit bus models are included in the RouteE Powertrain package. You can list all available models (including transit buses and other vehicles) with:

import nrel.routee.powertrain as pt

# List all available pre-trained models
print(pt.list_available_models())

Each model includes multiple estimators that account for different combinations of features such as speed, road grade, and stop frequency.

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