This New Software Maps Out a Better Bike Network for Your City

In most urban environments, bicycle infrastructure is often rolled out in disconnected fragments, leaving riders unable to travel safely across entire cities. A new suite of open-source software tools aims to solve this fragmentation by automatically identifying gaps in existing networks and suggesting complete, functional routes. The project, known as BikeNetKit, is based on eight years of research conducted at Denmark's IT University of Copenhagen. Led by associate professor Michael Szell, the tools utilize network science methods to view the city from a "big picture" perspective rather than focusing solely on individual street segments. One specific tool, GrowBikeNet, is designed to build networks from scratch, a capability particularly useful for U.S. cities where bike lanes are currently sparse. The algorithm generates a grid of "seed points" and connects them to any existing infrastructure, creating a map of suggested routes that ensures strong connections are in place from the outset. Users can further customize these plans by uploading data on past traffic crashes or daily travel patterns. The software also offers options to plan lanes around schools and suggests areas for car-free neighborhoods through a tool called Superblockify. Another component, LinkBikeNet, targets cities with otherwise well-connected networks by identifying specific gaps that need repair, while FixBikeNet prioritizes which gaps are most critical to address first. Importantly, the tools do not dictate the physical appearance of the infrastructure, allowing planners to choose between standard lanes or dedicated spaces for cyclists and pedestrians. The software is free and open-source, with code available on GitHub. It currently includes hundreds of analyses of European cities, ranging from growing a new network in Kyiv, Ukraine, to fixing gaps in Copenhagen and planning car-free zones in Berlin. However, the project notes that getting started requires technical knowledge, specifically the ability to use Python to install and run the algorithms. While the tools offer a data-driven approach to urban planning, they represent a shift away from the traditional model where cycling is treated as an afterthought to car traffic. By automating the identification of connectivity issues, the software empowers planners, advocates, and citizens to design more cohesive transportation systems. #NexSouk #AIForGood #EthicalAI #UrbanPlanning #CyclingInfrastructure