Label-Based Trajectory Clustering in Complex Road Networks

In the data mining of road networks, trajectory clustering of moving objects is of particular interest for its practical importance in many applications. Most of the existing approaches to this problem are based on distance measurement, and suffer from several performance limitations including inaccurate clustering, expensive computation, and incompetency to handle high dimensional trajectory data. This paper investigates the complex network theory and explores its application to trajectory clustering in road networks to address these issues. Specifically, we model a road network as a dual graph, which facilitates an effective transformation of the clustering problem from sub-trajectories in the road network to nodes in the complex network. Based on this model, we design a label-based trajectory clustering algorithm, referred to as LBTC, to capture and characterize the essence of similarity between nodes. For the evaluation of clustering performance, we establish a clustering criterion based on the classical Davies-Bouldin Index (DB), Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC) to maximize inter-cluster separation and intra-cluster homogeneity. The clustering accuracy and performance superiority of the proposed algorithm are illustrated by extensive simulations on both synthetic and real-world dataset in comparison with existing algorithms.