自行车网络中缺失链路的自动化检测
Automated Detection of Missing Links in Bicycle Networks
AI总结：
针对城市自行车网络建设零散、缺口多的问题，提出基于OpenStreetMap数据的IPDC流程识别优先级缺失链路，在哥本哈根验证与官方规划高度重叠，可为全市自行车网络规划提供低成本数据支持。
摘要：
骑行是提升城市交通可持续性的有效解决方案。然而，自行车网络通常以缓慢、零散的过程建设，即便在哥本哈根这类骑行基础设施发达的城市，也存在大量缺口。本文提出IPDC流程（识别、优先级排序、去聚类、分类，即Identify、Prioritize、Decluster、Classify），利用OpenStreetMap（开放街道地图）数据查找城市自行车网络中最重要的缺失链路。该流程首先采用多层网络方法识别所有可能的缺口，基于流量指标对其进行优先级排序，对出现的缺口集群进行去聚类处理，并人工分类缺口类型。我们将IPDC流程应用于哥本哈根，列出了105个最高优先级缺口。为评估效果，我们将这些缺口与该市最新的《自行车道优先级规划》（Cycle Path Prioritization Plan）对比，发现两者存在大量重叠。研究结果表明，数据需求极低的网络分析可作为自行车网络规划的高性价比支持工具。通过统筹全市网络来整合城市自行车基础设施，本数据驱动框架可补充本地化的人工规划流程，助力更高效的全市范围决策。
英文摘要：
Cycling is an effective solution for making urban transport more sustainable. However, bicycle networks are typically developed in a slow, piecewise process that leaves open a large number of gaps, even in well developed cycling cities like Copenhagen. Here, we develop the IPDC procedure (Identify, Prioritize, Decluster, Classify) for finding the most important missing links in urban bicycle networks, using data from OpenStreetMap. In this procedure we first identify all possible gaps following a multiplex network approach, prioritize them according to a flow-based metric, decluster emerging gap clusters, and manually classify the types of gaps. We apply the IPDC procedure to Copenhagen and report the 105 top priority gaps. For evaluation, we compare these gaps with the city's most recent Cycle Path Prioritization Plan and find considerable overlaps. Our results show how network analysis with minimal data requirements can serve as a cost-efficient support tool for bicycle network planning. By taking into account the whole city network for consolidating urban bicycle infrastructure, our data-driven framework can complement localized, manual planning processes for more effective, city-wide decision-making.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
