Fast largescale trajectory clustering

Abstract

In this paper, we study the problem of large-scale trajectory data clustering, k-paths, which aims to effciently identify k \representative“ paths in a road network. Unlike traditional clustering approaches that require multiple data-dependent hyperparameters, k-paths can be used for visual exploration in applications such as traffc monitoring, public transit planning, and site selection. By combining map matching with an effcient intermediate representation of trajectories and a novel edge-based distance (EBD) measure, we present a scalable clustering method to solve k-paths. Experiments verify that we can cluster millions of taxi trajectories in less than one minute, achieving improvements..