TY - JOUR
T1 - Label-Based Trajectory Clustering in Complex Road Networks
AU - Niu, Xinzheng
AU - Chen, Ting
AU - Wu, Chase Q.
AU - Niu, Jiajun
AU - Li, Yuran
N1 - Funding Information:
Manuscript received February 22, 2018; revised August 2, 2018, March 24, 2019, and August 3, 2019; accepted August 7, 2019. Date of publication September 13, 2019; date of current version October 2, 2020. This work was supported in part by the Scientific Research Project of Sichuan Provincial Public Security Department under Grant 2015SCYYCX06, in part by the Science and Technology Planning Project of Sichuan Province under Grant 2017FZ0094, and in part by the Fundamental Research Funds for the Central Universities Project with the University of Electronic Science and Technology of China. The Associate Editor for this article was S. A. Birrell. (Corresponding author: Xinzheng Niu.) X. Niu, T. Chen, J. Niu, and Y. Li are with the School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China (e-mail: xinzhengniu@uestc.edu.cn).
Publisher Copyright:
© 2000-2011 IEEE.
PY - 2020/10
Y1 - 2020/10
N2 - 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.
AB - 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.
KW - Vehicle trajectory
KW - clustering
KW - dual graph
KW - road networks
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U2 - 10.1109/TITS.2019.2937882
DO - 10.1109/TITS.2019.2937882
M3 - Article
AN - SCOPUS:85092580267
SN - 1524-9050
VL - 21
SP - 4098
EP - 4110
JO - IEEE Transactions on Intelligent Transportation Systems
JF - IEEE Transactions on Intelligent Transportation Systems
IS - 10
M1 - 8836635
ER -