Quality assessment of a country-wide bicycle node network with loop census analysis

TL;DR

This paper analyzes a large-scale Danish bicycle node network, developing metrics and a loop census to evaluate its topological features and cycling experience, aiding future planning and tourism development.

Contribution

It introduces a novel spatial and loop census analysis framework for assessing bicycle node networks, addressing the lack of performance metrics for human cycling experience.

Findings

High heterogeneity in node density and face loop lengths.

Identified feasible points for day trips and cycling choices.

Long-range cyclists have abundant options, while families face limitations.

Abstract

Bicycle node networks are regional bicycle networks equipped with a wayfinding system of numbered nodes to ease recreational cycling. They spur sustainable bicycle tourism, economic spending, and local culture. Due to their country-wide scale, implementing bicycle node networks is a considerable effort and investment. Despite this investment, planning is a manual ad-hoc process that follows general design principles, but without clear performance metrics that account for the human cycling experience. Here we analyze a 28,215 km long bicycle node network spanning Denmark, developing and studying such metrics. First, a spatial analysis of geometric and topological properties reveals high heterogeneity and local clusters of node density, face loop lengths, gradients, and feature-rich areas. Next, taking the perspective of a recreational cyclist starting at any node on the network, we create a loop census that lists all loops in the network up to day-trip length. The loop census identifies the feasible points on the network from which to take a day trip and quantifies the number of round trip choices, unveiling different levels of choice depending on the considered demographic group. While long-range cyclists can access most of the country with often overabundant choices, cyclists with stronger length and gradient limitations like families with small children can not - which could be overcome by e-bikes. Our open-source analysis methods provide data-driven decision support for bicycle node network planning with the potential to boost the development of rural cycling and cycling tourism.