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

2024-12-28

AI Summary

Current bicycle node network planning lacks standardized methodologies and data-driven decision-support tools, hindering the development of green tourism and rural cycling infrastructure. Method: This study proposes a systematic framework for constructing numbered-node guidance networks tailored to recreational cyclists, leveraging existing infrastructure. We develop an open-source, modular QGIS-integrated tool combining Python scripting with spatial analysis techniques to enable multi-dimensional quantitative evaluation—including land-use classification, proximity to attractions, and elevation distribution—and implement a multi-criteria evaluation model for automated network alternative comparison and optimization. Contribution/Results: The framework significantly enhances the scientific rigor, reproducibility, and efficiency of regional cycling tourism planning. The tool is publicly released under an open-source license and has been successfully applied in sustainable rural cycling initiatives.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Knowledge Representation and Reasoning: Qualitative Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Web Mining and Content Analysis: Web data visualization

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.

Problem

• Bicycle Network Planning
• Green Tourism
• Rural Cycling Promotion

Innovation

• BikeNodePlanner
• Data-driven Approach
• Green Mobility