An efficient online direction-preserving compression approach for trajectory streaming data

Deng, Ze; Han, Wei; Wang, Lizhe; Ranjan, Rajiv; Zomaya, Albert Y.; Jie, Wei

doi:10.1016/j.future.2016.09.019

Cited by 26 publications

(16 citation statements)

References 16 publications

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“…There is also quite a lot of work has been done to represent trajectory using less data [13], [15], [22], [30]. Trajectory compression algorithms can be roughly categorized into two groups, i.e., line-simplification-based and map-matching-based, according to the utilization of the road network [9], [16]. The latter group is more relevant to our work.…”

Section: B Trajectory Compressionmentioning

confidence: 99%

VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Chen

Ding

Wang

et al. 2020

IEEE Systems Journal

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Vehicles can be easily tracked due to the proliferation of vehicle-mounted global positioning system (GPS) devices. V T racer is a cost-effective mobile system for online trajectory compression and tracing vehicles, taking the streaming GPS data as inputs. Online trajectory compression, which seeks a concise and (near) spatial-lossless data representation before revealing the next vehicle's GPS position, is gradually becoming a promising way to alleviate burdens such as communication bandwidth, storing, and cloud computing. In general, an accurate online mapmatcher is a prerequisite. This two-phase approach is nontrivial because we need to overcome the essential contradiction caused by the resource-constrained GPS devices and the heavy computation tasks. V T racer meets the challenge by leveraging the idea of mobile edge computing. More specifically, we offload the heavy computation tasks to the nearby smartphones of drivers (i.e., smartphones play the role of cloudlets), which are almost idle during driving. More importantly, they have relatively more powerful computing capacity. We have implemented V T racer on the Android platform and evaluate it based on a real driving trace dataset generated in the city of Chongqing, China. Experimental results demonstrate that V T racer achieves the excellent performance in terms of matching accuracy, compression ratio, and it also costs the acceptable memory, energy, and app size. Index Terms-Global positioning system (GPS) devices, mobile edge computing, resource-constrained, trajectory mapping, trajectory compression. I. INTRODUCTION T HE wide proliferation of various global positioning system (GPS) devices and mobile Internet in daily life has made many kinds of trajectory data easily available at a large scale,

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Section: B Trajectory Compressionmentioning

confidence: 99%

VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Chen

Ding

Wang

et al. 2020

IEEE Systems Journal

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“…Long et al [20] proposed error measurement for direction preservation that calculates the angular delta before adding artificial arcs to a graph and using the (SP) approach to simplify the trajectory. A streaming version of [20], additionally, has been proposed in [21]. The latest one, the Sunshine algorithm [22], which is variant of the shortest path (SP) algorithm with additional requirements of sunshine duration error.…”

Section: Trajectory-simplification Problemmentioning

confidence: 99%

Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

Pulshashi

Bae

Choi

et al. 2019

IJGI

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Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

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“…There are also a number of studies that compress trajectories from the aspect of direction preservation, and representative algorithms include those by Potamias et al (2006), Long et al (2013; 2015), Ke et al (2017), Wu et al (2017a) and Deng et al (2017). Potamias et al (2006) defined two safe areas where one is formed by the previous two practical points, the other is formed by the previous two sample points, and only points that are within both safe areas will be deleted.…”

Section: Trajectory Simplificationmentioning

confidence: 99%

Vessel Trajectory Online Multi-Dimensional Simplification Algorithm

Zhang

Shi

et al. 2019

J. Navigation

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Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course.

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An efficient online direction-preserving compression approach for trajectory streaming data

Cited by 26 publications

References 16 publications

VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

VTracer: When Online Vehicle Trajectory Compression Meets Mobile Edge Computing

Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

Vessel Trajectory Online Multi-Dimensional Simplification Algorithm

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