For analysing, understanding and predicting the track/train-dynamics in order to develop comfortable and sustainable vehicles a sufficient description of the track course and conditions are key requirements. Not only the track irregularities but also the horizontal curvature affects the vehicle dynamics strongly. Nearly all cities owning light rail systems have gradually established the light rail traffic. By reasons of building density, road transportation infrastructure, and progress of the overall urban planning, the light rail infrastructure was constrained to the pre-existing environment. Thus, the track course is mainly optimized for efficient space use but not for best possible vehicle dynamics. To be able to analyse the track layout of tram networks at a bigger scale, an appropriate methodology that allows acquiring track course data is needed, which is the main objective of this paper. For this purpose, an open data approach was developed by the authors utilizing OpenStreetMap (OSM) to derive the horizontal track curvature based on geodata. This groundbreaking approach improves the state of the art since professional geodetic measurements of light rail tracks are generally rarely publicly available, cost-intensive and their preprocessing can potentially be time consuming. The outcome is a simple, robust, and fast approach that was validated using already existing reference track data which was available to the authors. Additionally, an error estimation of the methodology was carried out. Using a quadratic error function, the median standard deviation of the curvature can be determined and used to rate the exactness of the estimated curvature depending on its magnitude. In this approach, the curvature estimations exactness is generally high for small curve radii and decreases for bigger radii. Therefore it can be concluded that the field of application is especially promising for light rail infrastructure. But also for mainline tracks the new method can be used as a rough estimate, if no curvature data is at hand.

为了分析、理解和预测轨道/列车动力学以开发舒适和可持续的车辆，对轨道路线和条件的充分描述是关键要求。不仅轨道不平顺，水平曲率也对车辆动力学有很大影响。几乎所有拥有轻轨系统的城市都逐步建立了轻轨交通。由于建筑密度、道路交通基础设施和城市总体规划的推进，轻轨基础设施受限于原有环境。因此，赛道路线主要针对有效空间利用进行了优化，但并未针对可能的最佳车辆动力学进行优化。为了能够更大规模地分析有轨电车网络的轨道布局，需要一种允许获取赛道数据的适当方法，这是本文的主要目标。为此，作者开发了一种开放数据方法，利用 OpenStreetMap (OSM) 根据地理数据推导出水平轨道曲率。这种开创性的方法提高了最先进的技术水平，因为轻轨轨道的专业大地测量通常很少公开获得，成本密集，而且它们的预处理可能很耗时。结果是一种简单、稳健且快速的方法，该方法使用作者可用的现有参考轨道数据进行了验证。此外，对该方法进行了误差估计。使用二次误差函数，可以确定曲率的中值标准偏差，并根据其大小来评估估计曲率的准确性。在这种方法中，曲率估计的准确性通常对于较小的曲线半径较高，而对于较大的半径则较低。因此可以得出结论，轻轨基础设施的应用领域特别有前景。但对于主线轨道，如果手头没有曲率数据，新方法也可以用作粗略估计。