Co-Clustering Network-Constrained Trajectory Data

TL;DR

This paper introduces a novel method for clustering vehicle trajectories constrained by road networks using bipartite graph modeling, aiding in understanding traffic flow and driver behavior.

Contribution

It presents a new approach to co-cluster network-constrained trajectories, extending traditional free-space clustering methods to road network data.

Findings

Effective clustering demonstrated on synthetic data

Revealed insights into traffic flow dynamics

Potential for analyzing driver behavior

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.