Direction-preserving trajectory simplification

Long, Cheng; Wong, Raymond Chi-Wing; Jagadish, H. V.

doi:10.14778/2536206.2536221

Cited by 90 publications

(88 citation statements)

References 18 publications

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“…Some methods [31,32] compress trajectories beyond the use of road networks, and further make use of other user specified domain knowledge, such as places of interests along the trajectories [31]. There are also compression algorithms preserving the direction of the trajectory [18].…”

Section: Semantics Based Methodsmentioning

confidence: 99%

“…Transmitting and storing raw trajectory data consumes too much network bandwidth and storage capacity [2, 5, 15-17, 20, 22-24, 27, 34]. It is known that these issues can be resolved or greatly alleviated by trajectory compression techniques via removing redundant data points of trajectories [2,4,5,7,10,12,[15][16][17][18]20,23,24,27], among which the piece-wise line simplification technique is widely used [2, 4, 5, 7, 15-17, 20, 23], due to its distinct advantages: (a) simple and easy to implement, (b) no need of extra knowledge and suitable for freely moving objects, and (c) bounded errors with good compression ratios [15,27].…”

Section: Introductionmentioning

confidence: 99%

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One-pass trajectory simplification using the synchronous Euclidean distance

Lin

Jiang

et al. 2019

The VLDB Journal

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Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network band and computing resource. To attack this issue, one-pass line simplification (LS) algorithms have are been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (SED), and cannot support spatio-temporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (CISED-S and CISED-W) using SED, based on a novel spatio-temporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms CISED-S and CISED-W are on average 3 times faster than SQUISH-E (the most efficient existing LS algorithm using SED). In terms of compression ratios, algorithms CISED-S and CISED-W are comparable with and 19.6% better than DPSED (the most effective existing LS algorithm using SED) on average, respectively, and are 21.1% and 42.4% better than SQUISH-E on average, respectively.

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Section: Semantics Based Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

One-pass trajectory simplification using the synchronous Euclidean distance

Lin

Jiang

et al. 2019

The VLDB Journal

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“…Segmentation has been studied extensively in the context of trajectory representation. Segmentation partitions a trajectory into a series of segments or sub-trajectories and each segment is homogenous according to the segmentation criterion (e.g., direction) [22] of the segments to evaluate the overall patterns [23]. In this research, we utilize the concept of segmentation to build progressive MBRs for filtering.…”

Section: Mbr and Mindist For Efficient Searchingmentioning

confidence: 99%

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Wang

Zhang

et al. 2018

IJGI

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Performing similarity analysis on trajectories consisting of massive numbers of tracking points is computationally challenging. We introduce a progressive minimum bounding rectangle (MBR) and minimum distance (MINDIST) approach to process the K Best Connected Trajectory (K-BCT) query, which aims to find the top K similarity trajectories to a given query trajectory. Our approach has three unique features to speed up the query. First, the approach builds a series of progressive MBRs from the query trajectory to determine the order of reference trajectories to identify the target top K reference trajectories at an earlier stage. Second, this method introduces a grid-based search method to speed up the matched point detection between two trajectories for similarity measures. Third, this approach further leverages the many-core computing power of Graphical Processing Unit (GPU) devices to perform the query in a parallel manner. We have conducted tests with ship tracking data and human movement data using GPU instances from Amazon Web Services. Preliminary results indicate that (a) parallel computing has greatly improved the efficiency of the query, and (b) our optimized approach can further speedup the computation compared to parallel implementations.

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“…As expected, the pruning rules can reduce the number of visited nodes. For example, when K is set to 8,197 nodes are visited with pruning rules while 361 nodes are visited without pruning rules. It shows that with two pruning rules, about 45% of the nodes can be eliminated during the search process.…”

Section: Kds-tree Performance Pruning Effectiveness and Query Efficiencymentioning

confidence: 99%

“…Thirdly, taxi trajectories are big temporal and spatial datasets. For example, the size of daily trajectories of a taxi with 5 second interval could be around 4 GB [8]. Therefore, how to make recommendation based on the big trajectory data is still challenging.…”

Section: Introductionmentioning

confidence: 99%

Recommending Profitable Taxi Travel Routes Based on Big Taxi Trajectories Data

Yang

Wang

Rahimi

et al. 2015

Advances in Knowledge Discovery and Data Mining

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Abstract. Recommending routes with the shortest cruising distance based on big taxi trajectories is an active research topic. In this paper, we first introduce a temporal probability grid network generated from the taxi trajectories, then a profitable route recommendation algorithm called Adaptive Shortest Expected Cruising Route (ASECR) algorithm is proposed. ASECR recommends profitable routes based on assigned potential profitable grids and updates the profitable route constantly based on taxis' movements as well as utilizing the temporal probability grid network dynamically. To handle the big trajectory data and improve the efficiency of updating route constantly, a data structure kdS-tree is proposed and implemented for ASECR. The experiments on two real taxi trajectory datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

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Direction-preserving trajectory simplification

Cited by 90 publications

References 18 publications

One-pass trajectory simplification using the synchronous Euclidean distance

One-pass trajectory simplification using the synchronous Euclidean distance

Efficient Parallel K Best Connected Trajectory (K-BCT) Query with GPGPU: A Combinatorial Min-Distance and Progressive Bounding Box Approach

Recommending Profitable Taxi Travel Routes Based on Big Taxi Trajectories Data

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