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.

翻译：自行车节点网络是一种区域自行车网络，配备有编号节点的导航系统，旨在方便休闲骑行。它们促进可持续的自行车旅游、经济支出和当地文化。由于覆盖全国范围，实施自行车节点网络需要相当大的努力和投资。尽管投入了这些资源，规划仍然是一个遵循通用设计原则的手动临时过程，缺乏考虑人类骑行体验的明确绩效指标。本文分析了一个覆盖丹麦、全长28,215公里的自行车节点网络，开发并研究了此类指标。首先，对几何和拓扑属性的空间分析揭示了节点密度、面循环长度、坡度以及富含特色区域的高度异质性和局部集聚性。接着，从休闲骑行者的视角出发，我们创建了一个环路普查，列出了网络中所有长度不超过一日行程的环路。环路普查识别了网络中可行一日游的出发点，并量化了往返行程选择的数量，揭示了不同人口群体面临的不同选择水平。虽然长距离骑行者可以到达全国大部分地区，并且通常有过多选择，但受限于骑行长度和坡度的群体（如带小孩的家庭）则无法实现，而电动自行车可能克服这一限制。我们的开源分析方法为自行车节点网络规划提供了数据驱动决策支持，具有推动乡村骑行和自行车旅游发展的潜力。