RouteE-Transit provides a complete pipeline for predicting the energy consumption of transit bus trips based on GTFS data and a RouteE-Powertrain model. The package matches GTFS shapes to the OpenStreetMap road network, aggregates speed, distance, and grade estimates at the OSM road link level, and then uses a trained RouteE-Powertrain model to predict energy consumption.
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
)
# Run analysis 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
)See the documentation for more examples and details.
Install Pixi following the instructions in its documentation.
Once you have Pixi installed, from the root directory (routee-transit), install the environment with pixi install. If you encounter GDAL-related errors when installing on a Mac, try brew install gdal and then pixi install again.
This will create a virtual environment based on the dependencies described in pyproject.toml. To execute code in this virtual environment, use pixi run <my_file.py>, or pixi shell to run all subsequent commands in that environment. To use the development environment, add the -e dev flag, e.g., pixi shell -e dev.
You can also set up your environment using pip, if preferred. Create a new virtual environment using the tool of your choice (e.g., conda create -n routee-transit / conda activate routee-transit if using conda). In your virtual environment, run pip install . from the root directory. To include development dependencies, use pip install ".[dev]".
- Simple API: Use the
run()method for most workflows, or call individual methods for fine-grained control - HVAC Energy: Includes seasonal HVAC energy impacts for battery-electric buses
- Deadhead Trips: Models both mid-block deadhead (between trips) and depot deadhead (pull-out/pull-in)
- Multiple Models: Predict for multiple vehicle types in a single workflow
scripts/single_agency_full_analysis.py shows how to use RouteE-Transit to predict energy consumption for some or all trips in an agency's GTFS feed. See also docs/examples/Utah_Transit_Agency_example.py for a documented example.
To build the documentation, you must have the dev dependencies installed.
If you are using pixi, there is a pixi task defined in pyproject.toml you can use:
pixi run docsIf you installed RouteE-Transit with pip, you can build the docs with:
python docs/examples/_convert_examples.py # create .ipynb file from example for jupyter-book
jupyter-book build docs # build the docs pagesBe aware that jupyter-book will run all of the documentation examples when the docs are built, which can take a few minutes. To build the documentation faster, you can skip the examples by simply commenting them out in the table of contents (docs/_toc.yml).