ABSTRACT
Several methods for trajectory classification build models exploring trajectory global features, such as the average and the standard deviation of speed and acceleration, but for some applications these features may not be the best to determine the class. Other works explore local features, applying trajectory partition and discretization, that lose important movement information that could discriminate the class. In this work we propose a new method, called Movelets, to discover relevant subtrajectories without the need of a predefined criteria for either trajectory partition or discretization. We extend the concept of time series shapelets for trajectories, and to the best of our knowledge, this work is the first to use shapelets in the trajectory domain. We evaluated the proposed approach with several categories of datasets, including hurricanes, vehicles, animals, and transportation means, and show with extensive experiments that our method largely outperformed state of the art works, indicating that Movelets is very promising for trajectory classification.
- Lucas Andre de Alencar, Luis Otavio Alvares, Chiara Renso, Alessandra Raffaeta, and Vania Bogorny. 2015. A Rule-based Method for Discovering Trajectory Profiles. In Proceedings of the 27th International Conference on Software Engineering and Knowledge Engineering (SEKE). Pittsburgh, USA, 244--249.Google ScholarCross Ref
- Somayeh Dodge, Robert Weibel, and Ehsan Forootan. 2009. Revealing the physics of movement: Comparing the similarity of movement characteristics of different types of moving objects. Computers, Environment and Urban Systems 33, 6 (2009), 419--434.Google ScholarCross Ref
- Salvador García, Alberto Fernández, Julián Luengo, and Francisco Herrera. 2010. Advanced nonparametric tests for multiple comparisons in the design of experiments in computational intelligence and data mining: Experimental analysis of power. Information Sciences 180, 10 (2010), 2044--2064. Google ScholarDigital Library
- Jon Hills, Jason Lines, Edgaras Baranauskas, James Mapp, and Anthony Bagnall. 2014. Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery 28, 4 (2014), 851--881. Google ScholarDigital Library
- Jae-Gil Lee, Jiawei Han, Xiaolei Li, and Hector Gonzalez. 2008. TraClass: Trajectory Classification Using Hierarchical Region-based and Trajectory-based Clustering. Proceedings of the Very Large Data Base (VDLB) Endowment 1, 1 (2008), 1081--1094. Google ScholarDigital Library
- Abdullah Mueen, Eamonn Keogh, and Neal Young. 2011. Logical-shapelets: an expressive primitive for time series classification. In Proceedings of the 17th ACM International Conference on Knowledge Discovery and Data Mining (SIGKDD). ACM, New York, NY, USA, 1154--1162. Google ScholarDigital Library
- Andrey Tietbohl Palma, Vania Bogorny, Bart Kuijpers, and Luis Otavio Alvares. 2008. A clustering-based approach for discovering interesting places in trajectories. In Proceedings of the 23rd ACM Symposium On Applied Computing (SAC). ACM, Fortaleza, Ceara, Brazil, 863--868. Google ScholarDigital Library
- Dhaval Patel, Chang Sheng, Wynne Hsu, and Mong Li Lee. 2012. Incorporating Duration Information for Trajectory Classification. In Proceedings of the 28th IEEE International Conference on Data Engineering (ICDE). IEEE, Washington, DC, USA, 1132--1143. Google ScholarDigital Library
- Ian H Witten, Eibe Frank, Mark A Hall, and Christopher J Pal. 2016. Data Mining: Practical machine learning tools and techniques (4 ed.). Morgan Kaufmann. Google ScholarDigital Library
- Zhibin Xiao, Yang Wang, Kun Fu, and Fan Wu. 2017. Identifying Different Transportation Modes from Trajectory Data Using Tree-Based Ensemble Classifiers. ISPRS International Journal of Geo-Information 6, 2 (2017), 1--22.Google ScholarCross Ref
- Lexiang Ye and Eamonn J. Keogh. 2011. Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Mining Knowledge Discovery 22, 1--2 (2011), 149--182. Google ScholarDigital Library
- Willian Zalewski, Fabiano Silva, A.G. Maletzke, and C.A. Ferrero. 2016. Exploring Shapelet Transformation for Time Series Classification in Decision Trees. Knowledge Based Systems 112, C (Nov. 2016), 80--91. Google ScholarDigital Library
- Yu Zheng, Yukun Chen, Quannan Li, Xing Xie, and Wei-Ying Ma. 2010. Understanding transportation modes based on GPS data for web applications. ACM Transactions on the Web (TWEB) 4, 1 (2010), 1--36. Google ScholarDigital Library
Index Terms
- MOVELETS: exploring relevant subtrajectories for robust trajectory classification
Recommendations
Exploring frequency-based approaches for efficient trajectory classification
SAC '20: Proceedings of the 35th Annual ACM Symposium on Applied ComputingIn the last few years, several trajectory classification methods have been proposed for mobility data collected from GPS devices. Most of them only use information derived from the physical movement of the object, as speed, acceleration, and direction ...
Trajectory Data Classification: A Review
Survey Papers and Regular PapersThis article comprehensively surveys the development of trajectory data classification. Considering the critical role of trajectory data classification in modern intelligent systems for surveillance security, abnormal behavior detection, crowd behavior ...
Trajectory Data Mining: An Overview
Survey Paper, Regular Papers and Special Section on Participatory Sensing and Crowd IntelligenceThe advances in location-acquisition and mobile computing techniques have generated massive spatial trajectory data, which represent the mobility of a diversity of moving objects, such as people, vehicles, and animals. Many techniques have been proposed ...
Comments