Graph-Based Approaches to Clustering Network-Constrained Trajectory Data
This work addresses a domain-specific problem for researchers and practitioners in transportation or urban planning by focusing on network-constrained trajectories, but it is incremental as it adapts existing graph-based methods to a new context.
The paper tackled the problem of clustering trajectory data constrained by road networks, which prior work often ignored, by modeling segment interactions as a weighted graph and applying community detection to discover groups of road segments frequently traveled together, with results demonstrated on synthetic datasets.
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 an approach to clustering such network-constrained trajectory data. More precisely we aim at discovering groups of road segments that are often travelled by the same trajectories. To achieve this end, we model the interactions between segments w.r.t. their similarity as a weighted graph to which we apply a community detection algorithm to discover meaningful clusters. We showcase our proposition through experimental results obtained on synthetic datasets.