An Approach to Spatiotemporal Trajectory Clustering Based on Community Detection

Xin Wang,Jiahui Zhu,Xinzheng Niu,Zuoyan Liu,Zhipeng Cai

doi:10.1155/2021/5582341

Abstract

Nowadays, large volumes of multimodal data have been collected for analysis. An important type of data is trajectory data, which contains both time and space information. Trajectory analysis and clustering are essential to learn the pattern of moving objects. Computing trajectory similarity is a key aspect of trajectory analysis, but it is very time consuming. To address this issue, this paper presents an improved branch and bound strategy based on time slice segmentation, which reduces the time to obtain the similarity matrix by decreasing the number of distance calculations required to compute similarity. Then, the similarity matrix is transformed into a trajectory graph and a community detection algorithm is applied on it for clustering. Extensive experiments were done to compare the proposed algorithms with existing similarity measures and clustering algorithms. Results show that the proposed method can effectively mine the trajectory cluster information from the spatiotemporal trajectories.

Highlights

Nowadays, a huge amount of data is collected and it is important to develop tools to analyze data to extract useful knowledge
Based on the above advantages and limitations, we propose an approach to spatiotemporal trajectory clustering based on community detection (STTC-CD)
LCSS, EDR, Clue-Aware Trajectory Similarity (CATS), and Multidimensional Similarity Measure (MSM) fall all in the ε-threshold-based strategy, and the computation of similarity score is based on the point matching of two trajectories

Summary

Introduction

A huge amount of data is collected and it is important to develop tools to analyze data to extract useful knowledge. An emerging type of data that is playing a key role in multimodal data analysis is trajectory data [7] It consists of spatial and temporal information about moving objects. A complex network is suitable for revealing important relationships in trajectory data visually and can provide global information as time series data. (i) An improved similarity calculation method is designed, which matches pairs of trajectory points and applies a pruning strategy based on time slicing to reduce the time complexity (ii) A method is proposed to convert trajectories into a suitable data format to apply many types of techniques for trajectory data mining. A community detection algorithm is applied to cluster trajectories, which captures global relationships among trajectories from a graph-based perspective (iii) Experiments have been conducted to evaluate the proposed algorithm on several datasets to verify the influence of multiple factors.

Related Work

Problem Statement

The Proposed STTC-CD Algorithm

Performance Evaluation

Conclusion