Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data. In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed). We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers. This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation. We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio. Analysis and preliminary results show the applicability of approach for finding significant driving patterns.

中文翻译：

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通过轨迹分割发现驾驶模式

由于在驾驶过程中收集数据用于不同目的的设备无处不在，例如基于使用的保险 (UBI)、车队管理、联网车辆导航等，远程信息处理数据变得越来越可用。 因此，各种数据分析应用程序从数据中提取有价值的见解变得可行。在本文中，我们解决了仅从外部可观察到的现象（例如车辆速度）中发现基于行为的驾驶模式这一特别具有挑战性的问题。我们提出了一种轨迹分割方法，能够根据驾驶员的行为将驾驶模式发现为单独的段。这种分割方法包括一种新颖的轨迹变换以及一种用于分割的动态编程方法。我们将分割方法应用于真实词，俄亥俄州哥伦布市的一家大型保险公司提供的丰富的个人汽车轨迹数据集。分析和初步结果显示了寻找重要驾驶模式的方法的适用性。