Co-Clustering Network-Constrained Trajectory Data
This addresses a domain-specific problem for traffic analysis, but it is incremental as it adapts existing clustering methods to network-constrained data.
The paper tackles the problem of clustering vehicle trajectories constrained by a road network, modeling them as a bipartite graph to cluster vertices, and demonstrates the approach on synthetic data to infer flow dynamics and driver behavior.
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.