ABSTRACT Global Positioning System (GPS) data have become ubiquitous in many areas of transportation planning and research. The usefulness of GPS data often depends on the points being matched to the true sequence of edges on the underlying street network – a process known as ‘map matching.’ This paper presents a new map-matching algorithm that is designed for use with poor-quality GPS traces in urban environments, where drivers may circle for parking and GPS quality may be affected by underground parking and tall buildings. The paper is accompanied by open-source Python code that is designed to work with a PostGIS spatial database. In a test dataset that includes many poor-quality traces, our new algorithm accurately matches about one-third more traces than a widely available alternative. Our algorithm also provides a ‘match score’ that evaluates the likelihood that the match for an individual trace is correct, reducing the need for manual inspection.

中文翻译：

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在城市环境中匹配劣质 GPS 数据的地图：pgMapMatch 包

摘要 全球定位系统 (GPS) 数据在交通规划和研究的许多领域已经无处不在。GPS 数据的有用性通常取决于与底层街道网络上真实边缘序列匹配的点——这一过程称为“地图匹配”。本文提出了一种新的地图匹配算法，该算法专为在城市环境中使用质量较差的 GPS 轨迹而设计，在这种环境中，驾驶员可能会绕圈停车，而 GPS 质量可能会受到地下停车场和高层建筑的影响。该论文附有旨在与 PostGIS 空间数据库配合使用的开源 Python 代码。在包含许多劣质轨迹的测试数据集中，我们的新算法准确匹配的轨迹比广泛可用的替代方法多三分之一。