US Routing is a Python library designed for fast route calculations within the United States. It utilizes a pre-built graph of US roads, stored efficiently in memory, allowing for rapid queries without external dependencies or API calls. This offline capability makes it suitable for applications needing quick routing solutions, such as logistics or mapping tools, where network latency or cost is a concern. The project is open-source and available on GitHub.
A new Python library, named "US Routing," has been introduced with a focus on providing rapid and efficient routing calculations within the United States. This library leverages pre-calculated routing data specifically optimized for the US road network, enabling significantly faster query responses compared to traditional online routing services or general-purpose routing engines. Instead of relying on external APIs or computationally intensive on-the-fly calculations, US Routing utilizes a local dataset, minimizing latency and dependencies.
The project is open-source and hosted on GitHub, offering users the flexibility to integrate it directly into their Python applications. The core functionality revolves around providing driving routes and associated information, such as travel time and distance, between specified locations within the United States. This is achieved by leveraging a pre-built graph representation of the US road network, stored locally, allowing for rapid traversal and route computation. While the exact details of the underlying data and algorithms aren't fully elaborated in the announcement, the emphasis is on speed and efficiency for US-centric routing needs. This makes it potentially suitable for applications requiring numerous routing queries or scenarios where low latency is crucial, such as logistics, delivery optimization, or interactive mapping applications. The project aims to offer a practical alternative to relying on external routing services, particularly for applications focused specifically on the US geography.
Summary of Comments ( 26 )
https://news.ycombinator.com/item?id=43921653
HN users generally praised the project for its speed, simplicity, and use of OpenStreetMap data. Several commenters appreciated the clear documentation and the straightforward Python interface. Some questioned the licensing implications of using Valhalla's routing engine, specifically whether the non-commercial clause of the Valhalla license affects the US Routing library. Others suggested alternative approaches like GraphHopper or OSRM, and discussed the tradeoffs between local routing engines and cloud-based solutions. A few users mentioned potential use cases like delivery route optimization and logistics planning. The performance comparison with other routing libraries generated considerable interest, with some expressing skepticism and asking for more detailed benchmarks.
The Hacker News post for "Show HN: US Routing – Python library for fast local routing in the US" has several comments discussing the project and related topics.
Some users expressed interest in the project and its potential applications. One commenter questioned the licensing implications of using OpenStreetMap data, specifically mentioning the requirement to credit OpenStreetMap and its contributors. They further inquired about how this credit is displayed within the library's usage.
Another user highlighted the importance of the Valhalla project, suggesting that it's often overlooked despite its significant contributions to routing solutions. They posit that Valhalla's prominence is somewhat overshadowed by other more widely recognized projects like OSRM and GraphHopper. This commenter seems to imply the project being showcased might benefit from learning from or integrating aspects of Valhalla.
One commenter pointed out that the project uses a pre-calculated grid for routing, raising a concern about how this grid handles areas with varying road densities. They suggested that a uniform grid might be inefficient or inaccurate in areas with vastly different road network complexities, advocating for a more adaptive approach.
A different user mentioned their personal use of pgRouting, a PostgreSQL extension for geospatial routing, on a custom dataset of US roads. They didn't elaborate on the specifics of their application but implied satisfaction with this setup.
Another commenter asked a clarifying question about whether the library handles routing for all vehicle types or focuses specifically on cars. This suggests an interest in the library's versatility and potential application to different transportation modes.
Finally, a comment discussed the challenges inherent in routing, especially considering real-world scenarios like traffic, road closures, and turn restrictions. This brought a practical perspective to the discussion, highlighting that static routing solutions, while valuable, often need to be coupled with dynamic data for real-time applications.