Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

Mohamed Khalil El Mahrsi (1) , Fabrice Rossi (2)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 SAMM - Statistique, Analyse et Modélisation Multidisciplinaire (SAmos-Marin Mersenne)

Even though clustering trajectory data attracted considerable attention in the last few years, most of prior work assumed that moving objects can move freely in an euclidean space and did not consider the eventual presence of an underlying road network and its influence on evaluating the similarity between trajectories. In this paper, we present two approaches to clustering network-constrained trajectory data. The first approach discovers clusters of trajectories that traveled along the same parts of the road network. The second approach is segment-oriented and aims to group together road segments based on trajectories that they have in common. Both approaches use a graph model to depict the interactions between observations w.r.t. their similarity and cluster this similarity graph using a community detection algorithm. We also present experimental results obtained on synthetic data to showcase our propositions.

Mots clés: similarity; clustering; moving objects; trajectories; road network

Domaines: Apprentissage [cs.LG]; Machine Learning [stat.ML]

Mohamed Khalil El Mahrsi, Fabrice Rossi. Graph-Based Approaches to Clustering Network-Constrained Trajectory Data. Workshop on New Frontiers in Mining Complex Patterns (NFMCP 2012), held at ECML-PKDD 2012, Sep 2012, Bristol, United Kingdom. pp.184-195. ⟨hal-00737457⟩