Map matching is a widely used technology for mapping tracks to road networks. Typically, tracks are recorded using publicly available Global Navigation Satellite Systems, and road networks are derived from the publicly available OpenStreetMap project. The challenge lies in resolving the discrepancies between the spatial location of the tracks and the underlying road network of the map. Map matching is a combination of defined models, algorithms, and metrics for resolving these differences that result from measurement and map errors. The goal is to find routes within the road network that best represent the given tracks. These matches allow further analysis since they are freed from the noise of the original track, they accurately overlap with the road network, and they are corrected for impossible detours and gaps that were present in the original track. Given the ongoing need for map matching in mobility research, in this work, we present a novel map matching method based on Markov decision processes with Reinforcement Learning algorithms. We introduce the new Candidate Adoption feature, which allows our model to dynamically resolve outliers and noise clusters. We also incorporate an improved Trajectory Simplification preprocessing algorithm for further improving our performance. In addition, we introduce a new map matching metric that evaluates direction changes in the routes, which effectively reduces detours and round trips in the results. We provide our map matching implementation as Open Source Software (OSS) and compare our new approach with multiple existing OSS solutions on several public data sets. Our novel method is more robust to noise and outliers than existing methods and it outperforms them in terms of accuracy and computational speed.

中文翻译：

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与马尔可夫决策过程匹配的开源地图：一种新方法和现有方法的详细基准

地图匹配是一种广泛使用的技术，用于将轨迹映射到道路网络。通常，轨迹是使用公开的全球导航卫星系统记录的，道路网络则源自公开的 OpenStreetMap 项目。挑战在于解决轨道的空间位置与地图的基础道路网络之间的差异。地图匹配是定义的模型、算法和指标的组合，用于解决由于测量和地图错误而导致的差异。目标是在道路网络中找到最能代表给定轨道的路线。这些匹配可以进行进一步的分析，因为它们没有原始轨道的噪音，它们与道路网络准确重叠，并且它们针对原始轨道中存在的不可能的绕路和间隙进行了纠正。鉴于移动性研究中对地图匹配的持续需求，在这项工作中，我们提出了一种基于马尔可夫决策过程和强化学习算法的新型地图匹配方法。我们引入了新的候选采用功能，该功能使我们的模型能够动态解决异常值和噪声簇。我们还采用了改进的轨迹简化预处理算法，以进一步提高我们的性能。此外，我们引入了一种新的地图匹配指标，可以评估路线的方向变化，从而有效减少结果中的弯路和往返。我们以开源软件 (OSS) 的形式提供地图匹配实现，并将我们的新方法与多个公共数据集上的多个现有 OSS 解决方案进行比较。我们的新方法比现有方法对噪声和异常值更稳健，并且在准确性和计算速度方面优于现有方法。