Graph-Based Approaches to Clustering Network-Constrained Trajectory Data

TL;DR

This paper introduces two graph-based methods for clustering trajectory data constrained by road networks, focusing on grouping similar routes and road segments, with experimental validation on synthetic datasets.

Contribution

It proposes novel graph-based clustering approaches tailored for network-constrained trajectories, considering the influence of road networks on similarity measures.

Findings

Effective clustering of trajectories along the same road segments

Segmentation-based grouping of road segments by shared trajectories

Validation results on synthetic data demonstrate approach viability

Abstract

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.