MANTRA: A Scalable Approach to Mining Temporally Anomalous Sub-trajectories

In this paper, we study the problem of mining temporally anomalous sub-trajectory patterns from an input trajectory in a scalable manner. Given the prevailing road conditions, a sub-trajectory is temporally anomalous if its travel time deviates significantly from the expected time. Mining these patterns requires us to delve into the sub-trajectory space, which is not scalable for real-time analytics. To overcome this scalability challenge, we design a technique called MANTRA. We study the properties unique to anomalous sub-trajectories and utilize them in MANTRA to iteratively refine the search space into a disjoint set of sub-trajectory islands. The expensive enumeration of all possible sub-trajectories is performed only on the islands to compute the answer set of maximal anomalous sub-trajectories. Extensive experiments on both real and synthetic datasets establish MANTRA as more than 3 orders of magnitude faster than baseline techniques. Moreover, through trajectory classification and segmentation, we demonstrate that the proposed model conforms to human intuition.