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.
AschauerFGausterJHartwigL, et al. (2021). Guidelines for sustainable bicycle tourism. Technical report, Universität für Bodenkultur Wien, Publisher: Zenodo Version Number: Version 1.1. https://zenodo.org/record/4812801
CaggianiLCamporealeRBinettiM, et al. (2019) An urban bikeway network design model for inclusive and equitable transport policies. Transportation Research Procedia37: 59–66. DOI: 10.1016/j.trpro.2018.12.166. https://www.sciencedirect.com/science/article/pii/S2352146518305210
4.
CaspersenOPræstholmS. (2019) Rekreative stier og ruter - Erfaringer fra Tyskland, Holland, Schweiz, UK og Irland. Technical report, BARK Rådgivning A/S, Copenhagen.
KambALundbergELarssonJ, et al. (2021) Potentials for reducing climate impact from tourism transport behavior. Journal of Sustainable Tourism29(8): 1365–1382. DOI: 10.1080/09669582.2020.1855436.
13.
KimMJMichael HallC (2022) Does active transport create a win-win situation for environmental and human health? The moderating effect of leisure and tourism activity. Journal of Hospitality and Tourism Management52: 487–498. DOI: 10.1016/j.jhtm.2022.08.007. URL. https://www.sciencedirect.com/science/article/pii/S1447677022001516
14.
KircherKForwardSWallén WarnerH (2022) Cycling in rural areas : an overview of national and international literature. Statens väg- och transportforskningsinstitut, URL. https://urn.kb.se/resolve?urn=urn:nbn:se:vti:diva-18557
15.
MalucelliFGiovanniniANonatoM (2015) Designing single origin-destination itineraries for several classes of cycle-tourists. Transportation Research Procedia10: 413–422. DOI: 10.1016/j.trpro.2015.09.091. URL. https://www.sciencedirect.com/science/article/pii/S2352146515002781
McAndrewsCTabatabaieSLittJS (2018) Motivations and strategies for bicycle planning in rural, suburban, and low-density communities: the need for new best practices. Journal of the American Planning Association84(2): 99–111. DOI: 10.1080/01944363.2018.1438849.
OlmosLETadeoMSVlachogiannisD, et al. (2020) A data science framework for planning the growth of bicycle infrastructures. Transportation Research Part C: Emerging Technologies115: 102640. DOI: 10.1016/j.trc.2020.102640. URL. https://linkinghub.elsevier.com/retrieve/pii/S0968090X19306436
21.
OspinaJPDuqueJCBotero-FernándezV, et al. (2022) The maximal covering bicycle network design problem. Transportation Research Part A: Policy and Practice159: 222–236. DOI: 10.1016/j.tra.2022.02.004. URL. https://www.sciencedirect.com/science/article/pii/S0965856422000325
22.
ParkHLeeYJShinHC, et al. (2011) Analyzing the time frame for the transition from leisure-cyclist to commuter-cyclist. Transportation38(2): 305–319. DOI: 10.1007/s11116-010-9299-4.
ScappiniBZuccaVMeloniI, et al. (2022) The regional cycle network of Sardinia: upgrading the accessibility of rural areas through a comprehensive island-wide cycle network. European Transport Research Review14(1): 10. DOI: 10.1186/s12544-022-00533-6.
25.
SchmidtTTop Klein-WengelTChristiansenLB, et al. (2024) Identifying the potential for increasing cycling in Denmark: factors associated with short-distance and long-distance commuter cycling. Journal of Transport & Health38: 101870. DOI: 10.1016/j.jth.2024.101870. URL. https://www.sciencedirect.com/science/article/pii/S2214140524001166
26.
SteinackerCStorchD-MTimmeM, et al. (2022) Demand-driven design of bicycle infrastructure networks for improved urban bikeability. Nature Computational Science2: 1–10. DOI: 10.1038/s43588-022-00318-w. URL. https://www.nature.com/articles/s43588-022-00318-w
