Similarity-Based Compression of GPS Trajectory Data (original) (raw)

Algorithms for compressing GPS trajectory data: An empirical evaluation

2010

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of real-world data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including execution times and the errors caused by compressing spatio-temporal information, across numerous real-world datasets and various error metrics.

Compression of trajectory data: a comprehensive evaluation and new approach

GeoInformatica, 2013

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts of spatial and temporal information that traces a moving object's path. The exponential increase in the amount of such trajectory data has caused three major problems. First, transmission of large amounts of data is expensive and time-consuming. Second, queries on large amounts of trajectory data require computationally expensive operations to extract useful patterns and information. Third, GPS trajectories often contain large amounts of redundant data that waste storage and cause increased disk I/O time. These issues can be addressed by algorithms that reduce the size of trajectory data. A key requirement for these algorithms is to minimize the loss of information essential to locationbased applications. This paper presents a new compression method called SQUISH-E (Spatial QUalIty Simplification Heuristic-Extended) that provides improved runtime performance and usability. A comprehensive comparison of SQUISH-E with

Compression of GPS Trajectories Using Optimized Approximation

A large number of GPS trajectories, which include users' spatial and temporal information, are collected by geo-positioning mobile phones in recent years. The massive volumes of trajectory data bring about heavy burdens for both network transmission and data storage. To overcome these difficulties, GPS trajectory compression algorithm (GTC) was proposed recently that optimizes both the data reduction by trajectory simplification and the coding procedure using the quantized data. In this paper, instead of using greedy solution in GTC algorithm, the approximation process is optimized jointly with the encoding step via dynamic programming. In addition, Bayes' theorem is applied to improve the robustness of probability estimation for encoded values. The proposed solution has the same time complexity with GTC algorithm in the decoding procedure and experimental results show that its bitrate is around 80% comparing with GTC algorithm.

Compression and Mining of GPS Trace Data: New Techniques and Applications

2011

The massive volumes of trajectory data generated by inexpensive GPS devices have led to difficulties in processing, querying, transmitting and storing such data. To overcome these difficulties, a number of algorithms for compressing trajectory data have been proposed. These algorithms try to reduce the size of trajectory data, while preserving the quality of the information. We present results from a comprehensive empirical evaluation of many compression algorithms including Douglas-Peucker Algorithm, Bellman's Algorithm, STTrace Algorithm and Opening Window Algorithms. Our empirical study uses different types of realworld data such as pedestrian, vehicle and multimodal trajectories. The algorithms are compared using several criteria including how well they preserve the spatio-temporal information across numerous real-world datasets, execution times and various error metrics. Such comparisons are useful in identifying the most effective algorithms for various situations. We also...

A New Trajectory Similarity Measure for GPS Data

Proceedings of the 6th ACM SIGSPATIAL International Workshop on GeoStreaming, 2015

We present a new algorithm for measuring the similarity between trajectories, and in particular between GPS traces. We call this new similarity measure the Merge Distance (MD). Our approach is robust against subsampling and supersampling. We perform experiments to compare this new similarity measure with the two main approaches that have been used so far: Dynamic Time Warping (DTW) and the Euclidean distance.

Compressing Trajectories Using Inter-Frame Coding

With the advances in wireless communications and GPS technology, there is increasing interest in the field of location-aware services. Because of the proliferation of GPS-enabled devices and applications, in this study, we address the scalability issue in trajectory data management. Specifically, we propose a scheme called Inter-Frame Coding (IFC) for lossless compression of trajectory data, and implement two classical database queries based on the scheme. Evaluations of the IFC scheme using real trajectory datasets show that it can achieve a compression ratio of 58%. Moreover, it can reduce the computational complexity of range queries by a factor of 0.45, while maintaining an acceptable execution time in k-nearest neighbor searches. The IFC scheme is simple, efficient, and lossless; thus, it has the potential to facilitate trajectory-based data storage, compression, and computation.

TS2-tree - an efficient similarity based organization for trajectory data

Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems - GIS '07, 2007

The increasingly popular GPS technology and the growing amount of trajectory data it generates create the need for developing applications that efficiently store and query trajectories of moving objects. In this paper we introduce TS2 tree, a novel indexing structure for organizing trajectory data based on similarity between trajectories. TS2 tree provides lower and upper bounds on distance between trajectories, based on which we propose a general framework for effectively answering a wide range of similarity-based trajectory queries such as similarity threshold (ST) query and similarity best fit (SBF) query. The multifold reduction in query computation times and the number of I/O operations is demonstrated through an extensive experimental evaluation.

SQUISH: an online approach for GPS trajectory compression

2011

GPS-equipped mobile devices such as smart phones and in-car navigation units are collecting enormous amounts spatial and temporal information that traces a moving object's path. The popularity of these devices has led to an exponential increase in the amount of GPS trajectory data ...

Trajectory Compression under Network Constraints

Lecture Notes in Computer Science, 2009

The wide usage of location aware devices, such as GPS-enabled cellphones or PDAs, generates vast volumes of spatiotemporal streams modeling objects movements, raising management challenges, such as efficient storage and querying. Therefore, compression techniques are inevitable also in the field of moving object databases. Moreover, due to erroneous measurements from GPS devices, the problem of matching the location recordings with the underlying traffic network has recently gained the attention of the research community. So far, the proposed compression techniques are not designed for network constrained moving objects, while map matching algorithms do not consider compression issues. In this paper, we propose solutions tackling the combined, map matched trajectory compression problem, the efficiency of which is demonstrated through an experimental evaluation using a real trajectory dataset.

A Framework for Efficient and Convenient Evaluation of Trajectory Compression Algorithms

2013 Fourth International Conference on Computing for Geospatial Research and Application, 2013

Trajectory compression algorithms eliminate redundant information in the history of a moving object. Such compression enables efficient transmission, storage, and processing of trajectory data. Although a number of compression algorithms have been proposed in the literature, no common benchmarking platform for evaluating their effectiveness exists. This paper presents a benchmarking framework for efficiently, conveniently, and accurately comparing trajectory compression algorithms. This framework supports various compression algorithms and metrics defined in the literature, as well as three synthetic trajectory generators that have different trade-offs. It also has a highly extensible architecture that facilitates the incorporation of new compression algorithms, evaluation metrics, and trajectory data generators. This paper provides a comprehensive overview of trajectory compression algorithms, evaluation metrics and data generators in conjunction with detailed discussions on their unique benefits and relevant application scenarios. Furthermore, this paper describes challenges that arise in the design and implementation of the above framework and our approaches to tackling these challenges. Finally, this paper presents evaluation results that demonstrate the utility of the benchmarking framework.