Predictive analytics over mobility data is of great importance since it can assist an analyst to predict events, such as collisions, encounters, traffic jams, etc. A typical example is anticipated location prediction, where the goal is to predict the future location of a moving object, given a look-ahead time. What is even more challenging is to be able to accurately predict collective behavioural patterns of movement, such as co-movement patterns as well as their course over time. In this paper, we address the problem of Online Prediction of Co-movement Patterns. Furthermore, in order to be able to calculate the accuracy of our solution, we propose a co-movement pattern similarity measure, which facilitates the comparison between the predicted clusters and the actual ones. Finally, we calculate the clusters’ evolution through time (survive, split, etc.) and compare the cluster evolution predicted by our framework with the actual one. Our experimental study uses two real-world mobility datasets from the maritime and urban domain, respectively, and demonstrates the effectiveness of the proposed framework.

移动数据的预测分析非常重要，因为它可以帮助分析师预测事件，例如碰撞、遭遇、交通拥堵等。一个典型的例子是预期位置预测，其目标是预测移动的未来位置对象，给定一个前瞻时间。更具挑战性的是能够准确地预测运动的集体行为模式，例如共同运动模式以及它们随着时间的推移的过程。在本文中，我们解决了协同运动模式的在线预测问题. 此外，为了能够计算我们解决方案的准确性，我们提出了一种协同运动模式相似性度量，它有助于预测聚类与实际聚类之间的比较。最后，我们计算集群随时间的进化（生存、分裂等），并将我们的框架预测的集群进化与实际进化进行比较。我们的实验研究分别使用了来自海洋和城市领域的两个真实世界的移动数据集，并证明了所提出框架的有效性。