Compression of GPS Trajectories Using Optimized Approximation (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 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...

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

Compressing GPS data on mobile devices

2006

In context-aware mobile systems, data on past user behaviour or use of a device can give critical information. The scale of this data may be large, and it must be quickly searched and retrieved. Compression is a powerful tool for both storing and indexing data. For text documents powerful algorithms using structured storage achieve high compression and rapid search and retrieval. Byte-stream techniques provide higher compression, but lack indexation and have slow retrieval.

Similarity-Based Compression of GPS Trajectory Data

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

The recent increase in the use of GPS-enabled devices has introduced a new demand for efficiently storing trajectory data. In this paper, we present a new technique that has a higher compression ratio for trajectory data than existing solutions. This technique splits trajectories into sub-trajectories according to the similarities among them. For each collection of similar sub-trajectories, our technique stores only one subtrajectory's spatial data. Each sub-trajectory is then expressed as a mapping between itself and a previous sub-trajectory. In general, these mappings can be highly compressed due to a strong correlation between the time values of trajectories. This paper presents evaluation results that show the superiority of our technique over previous solutions.

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 ...

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.

An Online Compression Algorithm for Positioning Data Acquisition

Informatica, 2014

Positioning data are usually acquired periodically and uploaded to the server via wireless network in the location data acquisition systems. Huge communication overheads between the terminal and the server and heavy loads of storage space are needed when a large number of data points are uploaded. To this end, an online compression algorithm for positioning data acquisition is proposed, which compresses data by reducing the number of uploaded positioning points. Error threshold can be set according to users' needs. Feature points are extracted to upload real-timely by considering the changes of direction and speed. If necessary, an approximation trajectory can be obtained by using the proposed recovery algorithm based on the feature points on the server. Positioning data in three different travel modes, including walk, non-walk and mixed mode, are acquired to validate the efficiency of the algorithm. The experimental results show that the proposed algorithm can get appropriate compression rate in various road conditions and travel modes, and has better adaptability. Povzetek: Predstavljen je nov algoritem za zajemanje podatkov o realnem času, uporaben za sisteme za določanje položaja.

Compact Representation of GPS Trajectories over Vectorial Road Networks

Lecture Notes in Computer Science, 2013

Many devices nowadays record traveling routes, of users, as sequences of GPS locations. With the growing popularity of smartphones, millions of such routes are generated each day, and many routes have to be stored locally on the device or transmitted to a remote database. It is, thus, essential to encode the sequences, to decrease the volume of the stored or transmitted data. In this paper we study the problem of coding routes over a vectorial road network (map), where GPS locations can be associated with vertices or with road segments. We consider a three-step process of dilution, map-matching and coding. We present two methods to code routes. The first method represents the given route as a sequence of greedy paths. We provide two algorithms to generate a greedy-path code for a sequence of n vertices on the map. The first algorithm has O(n) time complexity, and the second one has O(n 2 ) time complexity, but it is optimal, meaning that it generates the shortest possible greedypath code. Decoding a greedy-path code can be done in O(n) time. The second method codes a route as a sequence of shortest paths. We provide a simple algorithm to generate a shortest-path code in O(kn 2 log n) time, where k is the length of the produced code, and we prove that this code is optimal. Decoding a shortest-path code also requires O(kn 2 log n) time. Our experimental evaluation shows that shortest-path codes are more compact than greedy-path codes, justifying the larger time complexity.

On Predicting and Compressing Vehicular GPS Traces

2010

Many vehicular safety applications rely on vehicles periodically broadcasting their position information and location trace. In very dense networks, such safety messaging can lead to offered traffic loads that saturate the shared wireless medium. One approach to address this problem is to reduce the frequency of location update messages when the movements of a vehicle can be predicted by nearby vehicles. In this paper, we study how predictable vehicular locations are, given a Global Positioning System trace of a vehicles recent path. We empirically evaluate the performance of linear and higher degree polynomial prediction algorithms using about 2500 vehicle traces collected under urban and highway driving conditions. We find that linear polynomial prediction using the two most recent known locations performs best. Also, traces with a time granularity of 0.2s are highly predictable in low speed urban environments, and a location update rate of 1Hz may suffice to represent urban vehicular movements. Lastly, the paper also evaluates compression of different time-granularity traces using line simplification and polynomial interpolation techniques to reduce message sizes.