Journal article

An effective and versatile distance measure for spatiotemporal trajectories

Somayeh Naderivesal, Lars Kulik, James Bailey

DATA MINING AND KNOWLEDGE DISCOVERY | SPRINGER | Published : 2019

Abstract

The analysis of large-scale trajectory data has tremendous benefits for applications ranging from transportation planning to traffic management. A fundamental building block for the analysis of such data is the computation of similarity between trajectories. Existing work for similarity computation focuses mainly on the spatial aspects of trajectories, but more rarely takes into account time in conjunction with space. A key challenge when considering time is how to handle trajectories that are sampled asynchronously or at variable rates, which can lead to uncertainty. To tackle this problem, we quantify trajectory similarity as an interval, rather than a single value, to capture the uncertai..

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