Co-Clustering Network-Constrained Trajectory Data
Machine Learning
2015-11-05 v1 Databases
Machine Learning
Abstract
Recently, clustering moving object trajectories kept gaining interest from both the data mining and machine learning communities. This problem, however, was studied mainly and extensively in the setting where moving objects can move freely on the euclidean space. In this paper, we study the problem of clustering trajectories of vehicles whose movement is restricted by the underlying road network. We model relations between these trajectories and road segments as a bipartite graph and we try to cluster its vertices. We demonstrate our approaches on synthetic data and show how it could be useful in inferring knowledge about the flow dynamics and the behavior of the drivers using the road network.
Cite
@article{arxiv.1511.01281,
title = {Co-Clustering Network-Constrained Trajectory Data},
author = {Mohamed Khalil El Mahrsi and Romain Guigourès and Fabrice Rossi and Marc Boullé},
journal= {arXiv preprint arXiv:1511.01281},
year = {2015}
}