Label-Based Trajectory Clustering in Complex Road Networks

Document Type

Article

Publication Date

10-1-2020

Abstract

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.

Identifier

85092580267 (Scopus)

Publication Title

IEEE Transactions on Intelligent Transportation Systems

External Full Text Location

https://doi.org/10.1109/TITS.2019.2937882

e-ISSN

15580016

ISSN

15249050

First Page

4098

Last Page

4110

Issue

10

Volume

21

Grant

2015SCYYCX06

Fund Ref

University of Electronic Science and Technology of China

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