Utility Route Planner

This repository shares research on software for automatic placement of electricity cables using a combination of geo-information and graph theory.

The utility network needs to be expanded due to the energy transition. Finding a location for new infrastructure is no easy feat considering the amount of involved design criteria. This research includes the creation of a software package for automatic placement of utility network using a combination of geo-information and graph theory.

This research is being carried out at Alliander, a Dutch DSO, as part of Jelmar Versleijen's PhD with Wagening University. Read more about research at Alliander.

Citing

If you use or refer to this software in your research, please cite the following publication:

Versleijen, J., de Bruin, S., Dirckx, D. et al. An open-source benchmark of the state-of-the-art in electrical cable routing. Energy Syst (2026). https://doi.org/10.1007/s12667-026-00827-x

Goals for sharing

Our goal for sharing this software is to encourage research on utility route planning for distribution system operators. Researchers or education can use the real-life use cases presented in this repository to test their own algorithms. The software is not intended for production use.

Remaining challenges solve are:

• Constraining the route to a maximum length.
• Giving alternative routes which are similar in costs (like seen in modern navigation systems).
• Realistic road crossings. Road crossings are typically done at a 90 degree angle through a process called pipe ramming.
• Alignment to existing infrastructure, resulting in a more "human-like" route.

Installation

To install the utility-route-designer package, use Python 3.12 with uv package manager:

uv sync

Usage

Running the main file will create utility routes for the five included cases in the data/examples folder. Optionally edit the configuration file mcda_presets.py to change the weights of the environmental criteria.

uv run python main.py

The results are placed in the data/processed folder:

• mcda_output.gpkg: contains the environmental criteria as vectors used in creating the suitability raster
• benchmark_suitability_raster.vrt: the suitability raster / cost-surface used for the least-cost path analysis
• lcpa_results.gpkg: contains the generated route as linestring

View them in QGIS or similar GIS GUI:

Run tests using pytest:

uv run python pytest

For quick experimentation, weights of existing criteria can be changed. This can be done by editing the mcda_presets.py file.

Adding new criteria can be done by:

1. Add vector data to the case study geopackage in the data/examples folder.
2. Adding a new entry to mcda_presets.py according to the pydantic model RasterPresetCriteria.
3. Adding a new class to the utility_route_planner/mcda/vector_preprocessing folder.
4. Implementing the specific_preprocess method. This commonly consists out reclassifying a dominant attribute.

Support

If you have trouble installing, building, or using utility-route-planner, but you don't think you've encountered a genuine bug, you can ask a question in the Issues tab of the repository. If you have an idea for a new feature or a recommendation for existing features or documentation, you can also propose it in the Issues tab.

How to report a bug or a security vulnerability

This project manages bug and enhancement using the GitHub issue tracker.

Contributing

Please read CODE_OF_CONDUCT, CONTRIBUTING and PROJECT GOVERNANCE for details on the process for submitting pull requests to us.

License

This project is licensed under the Apache-2.0 - see LICENSE for details.

Licenses data

This project is largely dependent on data. Data is incorporated in the example folder and is licensed under their own respective Open-Source licenses:

• BGT: CC PDM 1.0 downloaded from PDOK
• Natura2000: CC PDM 1.0 downloaded from PDOK
• Alliander asset information: CC BY 4.0 downloaded from ArcGIS Online: gas, electricity