BikeNodePlanner: a data-driven decision support tool for bicycle node network planning

TL;DR

BikeNodePlanner provides a data-driven, open-source workflow for planning bicycle node networks within a GIS framework. By encoding DKNT design criteria into modular PyQGIS evaluations of edge lengths, loop lengths, connectivity, POI accessibility, landscape variation, and elevation, it enables reproducible assessment of proposed networks. The tool supports visualization and identification of missing links to guide regional planning and cycling tourism development, with Denmark as a primary use case. While lacking live iterative feedback, it lays groundwork for automated proposal generation and broader applicability beyond the Danish context.

Abstract

A bicycle node network is a wayfinding system targeted at recreational cyclists, consisting of numbered signposts placed alongside already existing infrastructure. Bicycle node networks are becoming increasingly popular as they encourage sustainable tourism and rural cycling, while also being flexible and cost-effective to implement. However, the lack of a formalized methodology and data-driven tools for the planning of such networks is a hindrance to their adaptation on a larger scale. To address this need, we present the BikeNodePlanner: a fully open-source decision support tool, consisting of modular Python scripts to be run in the free and open-source geographic information system QGIS. The BikeNodePlanner allows the user to evaluate and compare bicycle node network plans through a wide range of metrics, such as land use, proximity to points of interest, and elevation across the network. The BikeNodePlanner provides data-driven decision support for bicycle node network planning, and can hence be of great use for regional planning, cycling tourism, and the promotion of rural cycling.

Figures (3)

• Figure 1: Left: Subset of concept bicycle node network. Contrary to a route going straight from A to B, a bicycle node network contains many loops (i.e., possible roundtrips, or "simple cycles" in graph theory) and allows for customization of the route. Right: Example of signage for node in the network. The sign points towards the nearest other nodes in the network.
• Figure 2: Overview of BikeNodePlanner features; interactive QGIS legends not shown. a) Classification of network edge length. Black: too short; green: ideal length; yellow: above ideal length; red: too long. b) Classification of loop lengths. Black: too short; green: ideal length; red: too long. c) Components in the network. Each color represents a disconnected component. d) Network accessibility, point data. Large points represent facilities within reach, small points represent facilities outside of reach, based on the distance threshold.) (e) Landscape variation, polygon data. Highlights where the network goes through areas of cultural interest. f) Network slope. Darker shades of red indicate steeper slopes.
• Figure 3: The BikeNodePlanner workflow.