BikeNodePlanner
A data-driven decision support tool for bicycle node network planning
Bibliographic Data
| ID | 21247173 |
|---|---|
| Authors | Anastassia Vybornova (0000-0001-6915-2561, University of Copenhagen, corresponding author), Ane Rahbek Vierø (0000-0002-2404-7271, Roskilde University), Kirsten Krogh Hansen (Danish Nature Agency), Michael Szell (0000-0003-3022-2483, Complexity Science Hub Vienna) |
| Year | 2025 |
| Volume | 52 |
| Issue | 7 |
| Pages | 1771-1780 |
| Publication date | 2025-09-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Environment and Planning B Urban Analytics and City Science (JOURNAL) |
| Journal identifiers | ISSN: 2399-8083 • E-ISSN: 2399-8091 |
| Publisher | SAGE Publications (PUBLISHER • US) |
| DOI | 10.1177/23998083251355999 |
| OpenAlex | W4411826254 |
| Language | EN |
| References cited | 21 |
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
Computer network · Data mining · Decision support system · Network planning and design · Computer Science · Data Management and Algorithms · Engineering · Human Mobility and Location-Based Analysis · Transportation Planning and Optimization
| Highly cited | No |
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