标题： 基于图的聚类网络约束轨迹数据方法 显示英文标题

标题： Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

摘要： 尽管聚类轨迹数据在近几年引起了相当多的关注，但大多数先前的工作假设移动对象可以在欧几里得空间中自由移动，并未考虑潜在道路网络的存在及其对评估轨迹之间相似性的影响。 在本文中，我们提出了两种聚类网络约束轨迹数据的方法。 第一种方法发现了沿着道路网络相同部分行驶的轨迹聚类。 第二种方法是面向路段的，旨在根据它们共有的轨迹将道路路段分组在一起。 这两种方法都使用图模型来描述观察结果之间的相互作用，根据它们的相似性，并使用社区检测算法对这种相似性图进行聚类。 我们还在合成数据上展示了实验结果，以展示我们的建议。 显示英文摘要

摘要： Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present two approaches to clustering network-constrained trajectory data. The first approach discovers clusters of trajectories that traveled along the same parts of the road network. The second approach is segment-oriented and aims to group together road segments based on trajectories that they have in common. Both approaches use a graph model to depict the interactions between observations w.r.t. their similarity and cluster this similarity graph using a community detection algorithm. We also present experimental results obtained on synthetic data to showcase our propositions.