More tools

BikeNetLib is the core utility package underpinning the BikeNetKit family of tools — shared input/output, network-analysis utilities, and visualization helpers built on OSMnx and GeoPandas, so GrowBikeNet, LinkBikeNet, FixBikeNet and the others don't each reimplement the same bicycle-network plumbing. It also provides general tools useful for anybody working with bicycle networks. BikeNetLib is developer/researcher-facing infrastructure rather than a standalone analysis: there is no visualization or dataset of its own, only a growing, documented API for anyone working with bicycle network data or building on top of the family's tools.

MoveBikeNet uses computer vision and computational desire line analysis to understand actual cycling movement patterns. By analyzing observed trajectories from a video, it reveals where cyclists want to ride versus where infrastructure currently exists. The approach was originally demonstrated at the Dybbølsbro intersection in Copenhagen, one of Europe's busiest cycling junctions, showing how desire lines can inform intersection redesign and network planning.

BikeDNA is a tool for bicycle infrastructure data and network assessment. It compares OpenStreetMap bicycle data against official reference datasets at both global and local (grid cell) levels, measuring completeness, accuracy, and structural quality. The analysis produces interactive maps and reports. A "BIG" variant handles large-scale datasets. BikeDNA helps planners understand how reliable their data is before making infrastructure decisions.

StretchBikeNet addresses the challenge of designing cohesive urban bicycle infrastructure by modeling the city as a multilayer transport network. Using optimal transport routing, it identifies which corridors should be widened or upgraded to maximize cycling utility while accounting for interactions with other transport modes. The approach balances the trade-off between bicycle and car infrastructure, producing investment plans that improve the overall coherence of the cycling network.

BikeNodePlanner is a decision-support tool for planning bicycle node networks — the numbered-signpost wayfinding systems that guide recreational cyclists along existing infrastructure. It runs in QGIS and Python and works for any study area. Planners feed it a draft network plan plus curated local geodata — land use, amenities, points of interest, elevation — and it returns a network analysis and a customizable evaluation against best-practice design criteria, both explorable interactively in QGIS. The result highlights the stretches that are too steep, too remote from services, or too poorly connected, so the plan can be adjusted before anything is signposted. It was built for users with minimal GIS experience, in a collaboration between Dansk Kyst- og Naturturisme and the IT University of Copenhagen to develop a country-wide bicycle node network for Denmark.

LoopBikeNet assesses the quality of a bicycle node network through loop census analysis. Rather than measuring the network segment by segment, it enumerates the closed round trips the network offers and characterizes each by length, gradient, and the features along the way — the quantity that actually matters to a recreational cyclist choosing a day out. Applied to Denmark's 28,215 km country-wide node network, it revealed high heterogeneity and local clusters in node density, loop lengths, gradients and feature-rich areas: experienced cyclists have an abundance of options, while families with small children are sharply constrained by loop length and hilliness — a gap that e-bikes could close. The analysis runs on networks prepared with BikeNodePlanner.

HoodBikeNet extends bicycle network growth strategies to incorporate Low Traffic Neighborhoods (LTNs). It combines modal filtering — restricting through-traffic in residential areas — with strategic bicycle network expansion, prioritizing connections between LTNs and existing cycling infrastructure. The tool helps planners maximize safe cycling coverage with minimal investment by leveraging the traffic-calmed environment that LTNs already provide.