论文标题
无监督的多标准轨迹细分和驱动优先采矿
Scalable Unsupervised Multi-Criteria Trajectory Segmentation and Driving Preference Mining
论文作者
论文摘要
我们为大型轨迹数据集提供了分析技术，旨在提供对超出它们超越它们的轨迹的语义理解。提出的技术使用驾驶偏好模型W.R.T.路段遍历成本，例如旅行时间和距离，以分析和解释轨迹。 特别是，我们提出了轨迹挖掘技术，可以（a）在指示的轨迹中找到有趣的点，例如，通过所选轨迹恢复驾驶员的驾驶偏好。我们使用超过100万辆汽车轨迹的数据集评估了有关识别和个性化路由的任务，该技术在3年内在整个丹麦收集了100万个车辆轨迹。我们的技术可以有效地实施，并且高度可行，从而使它们可以扩展到数百万或数十亿的轨迹。
We present analysis techniques for large trajectory data sets that aim to provide a semantic understanding of trajectories reaching beyond them being point sequences in time and space. The presented techniques use a driving preference model w.r.t. road segment traversal costs, e.g., travel time and distance, to analyze and explain trajectories. In particular, we present trajectory mining techniques that can (a) find interesting points within a trajectory indicating, e.g., a via-point, and (b) recover the driving preferences of a driver based on their chosen trajectory. We evaluate our techniques on the tasks of via-point identification and personalized routing using a data set of more than 1 million vehicle trajectories collected throughout Denmark during a 3-year period. Our techniques can be implemented efficiently and are highly parallelizable, allowing them to scale to millions or billions of trajectories.
