# A survey on trajectory clustering analysis

arXiv: https://arxiv.org/abs/1802.06971 · HTML: https://mlsanity.com/paper/1802.06971/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2018.

## Built on by (most cited first, 12 of 12)

- [Trajectory Data Management and Mining: A Survey from Deep Learning to the LLM Era](https://mlsanity.com/paper/2403.14151/) (2024)
- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)
- [Clustering in Recurrent Neural Networks for Micro-Segmentation using Spending Personality](https://mlsanity.com/paper/2109.09425/) (2021)
- [Traffic Scene Similarity: a Graph-based Contrastive Learning Approach](https://mlsanity.com/paper/2309.09720/) (2023)
- [Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration](https://mlsanity.com/paper/2301.04525/) (2023)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [A Simplified Framework for Air Route Clustering Based on ADS-B Data](https://mlsanity.com/paper/2107.12869/) (2021)
- [BiC-MPPI: Goal-Pursuing, Sampling-Based Bidirectional Rollout Clustering Path Integral for Trajectory Optimization](https://mlsanity.com/paper/2410.06493/) (2024)
- [A Dual Perspective on Synthetic Trajectory Generators: Utility Framework and Privacy Vulnerabilities](https://mlsanity.com/paper/2604.19653/) (2026)
- [Trajectory Clustering Performance Evaluation: If we know the answer, it's not clustering](https://mlsanity.com/paper/2112.01570/) (2021)
- [An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction](https://mlsanity.com/paper/2211.00817/) (2022)
- [Context-free Self-Conditioned GAN for Trajectory Forecasting](https://mlsanity.com/paper/2603.08658/) (2026)

## Abstract (arXiv)

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.