In an era where complex routing solutions often dominate the landscape, a lightweight Python library called US Routing has emerged as a refreshing alternative for developers seeking simpler solutions for routing within the United States. The library, which bootstraps from the North American Roads dataset, has sparked discussions among developers about its practical applications and potential optimizations.
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| A screenshot of the GitHub repository for the US Routing project, showcasing its code and version management |
Lightweight Alternative to Complex Routing Solutions
US Routing positions itself as a lightweight alternative to more comprehensive routing engines like Graphhopper and OSRM. The library enables fast local routing across the United States when approximations are acceptable, making it particularly useful for simpler tasks where heavier solutions might be overkill.
Nice. Very light-weight compared to proper local routers like Graphhopper, OSRM, etc., which can be overkill for simple tasks.
The core functionality allows users to calculate routes between cities, zip codes, or coordinates, providing details such as total distance, duration, and states traversed. This simplicity has resonated with developers looking for straightforward routing solutions without the complexity of more robust systems.
Technical Implementation and Optimization Opportunities
The library's routing implementation is based on NetworkX's shortest_path function, which uses Dijkstra's algorithm. While functional, community members have pointed out potential performance optimizations. Suggestions include implementing bidirectional Dijkstra, A* algorithms, or contraction hierarchies, which could significantly improve performance for a fixed-weight edge graph.
The developer clarified that the approximations mentioned in the documentation refer not to the shortest path calculation itself (which is guaranteed to be optimal with Dijkstra's algorithm) but rather to the distance and time estimations. These approximations occur when routing between points that don't exist as nodes in the graph, requiring the system to find the closest available node.
Data Source and Accessibility
US Routing relies on the North American Roads dataset, which is a work of the United States government and available for unrestricted public use. The library includes functionality to download and process this data, though some users reported challenges accessing the raw dataset due to its format.
One community member discovered a 282MB SQLite database linked to the project but encountered compatibility issues with the VSRS module, which appears to be a proprietary Esri component. The developer and other commenters helpfully pointed out alternative download options for the dataset, including CSV and shapefile formats.
Python Version Requirements and Installation Challenges
A notable discussion point among users centered around the library's Python version requirements. US Routing requires Python 3.11 specifically, which created installation challenges for users running different versions. This sparked a helpful exchange about using tools like uv and pyenv to manage Python environments without disrupting system-wide installations.
Several community members offered detailed guidance on creating virtual environments with specific Python versions:
uv venv --python 3.11
This command creates a virtual environment using Python 3.11 without requiring changes to the global Python installation, allowing users to run the library alongside existing projects that might use different Python versions.
Coverage and Potential Extensions
When questioned about the library's coverage for shorter distances within cities, the developer explained that the current implementation covers road classes up to level 3, which includes freeways, primary provincial/territorial highways, and secondary provincial/territorial highways/municipal arterials. The developer noted that the library could be extended with the proper dataset to handle more granular routing needs.
The community's engagement with US Routing highlights a continuing interest in specialized, lightweight tools that solve specific problems efficiently, even in a landscape dominated by comprehensive solutions and AI-powered alternatives.
Reference: US Routing