A survey on trajectory clustering analysis

TL;DR

This survey reviews the development of trajectory clustering methods, highlighting their applications in security, traffic, and behavior analysis, and discusses challenges and future research directions.

Contribution

It provides a comprehensive overview and analysis of trajectory clustering techniques, categorizing methods and identifying key challenges and future directions.

Findings

Trajectory clustering methods are categorized into unsupervised, supervised, and semi-supervised.

Current methods face challenges due to complex application scenarios and high data dimensions.

The survey highlights promising future research directions in trajectory clustering.

Abstract

This paper comprehensively surveys the development of trajectory clustering. Considering the critical role of trajectory data mining in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior analysis, and traffic control, trajectory clustering has attracted growing attention. Existing trajectory clustering methods can be grouped into three categories: unsupervised, supervised and semi-supervised algorithms. In spite of achieving a certain level of development, trajectory clustering is limited in its success by complex conditions such as application scenarios and data dimensions. This paper provides a holistic understanding and deep insight into trajectory clustering, and presents a comprehensive analysis of representative methods and promising future directions.