Review on trajectory similarity measures

Magdy, Nehal; Sakr, Mahmoud; Mostafa, T.; El-Bahnasy, Khaled

doi:10.1109/intelcis.2015.7397286

Cited by 79 publications

(65 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…EDR and LCSS present poorer robustness than the DTW and SDTW. The conclusion that the robustness of the LCSS and EDR algorithms is better than the DTW when the deviation degree is greater than ε from [4] is not correct. The above conclusion is similar with [23].…”

Section: Noise Effect Analysismentioning

confidence: 96%

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

Mao

Zhong

Xiao

et al. 2017

Preprint

View full text Add to dashboard Cite

Abstract:With the rapid spread of built-in GPS handheld smart devices, the trajectory data from GPS sensors has grown explosively. Trajectory data has spatio-temporal characteristics and rich information. Using trajectory data processing techniques can mine the patterns of human activities and the moving patterns of vehicles in the intelligent transportation systems. A trajectory similarity measure is one of the most important issues in trajectory data mining (clustering, classification, frequent pattern mining, etc.). Unfortunately, the main similarity measure algorithms with the trajectory data have been found to be inaccurate, highly sensitive of sampling methods, and have low robustness for the noise data. To solve the above problems, three distances and their corresponding computation methods are proposed in this paper. The point-segment distance can decrease the sensitivity of the point sampling methods. The prediction distance optimizes the temporal distance with the features of trajectory data. The segment-segment distance introduces the trajectory shape factor into the similarity measurement to improve the accuracy. The three kinds of distance are integrated with the traditional dynamic time warping algorithm (DTW) algorithm to propose a new segment-based dynamic time warping algorithm (SDTW). The experimental results show that the SDTW algorithm can exhibit about 57%, 86%, and 31% better accuracy than the longest common subsequence algorithm (LCSS), and edit distance on real sequence algorithm (EDR) , and DTW, respectively, and that the sensitivity to the noise data is lower than that those algorithms.

show abstract

Section: Noise Effect Analysismentioning

confidence: 96%

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

Mao

Zhong

Xiao

et al. 2017

Preprint

View full text Add to dashboard Cite

show abstract

“…Namely, the structure of the nodes inside the trajectory is taken into consideration in the computation process, which can more accurately describe the similarity between the trajectories. [4,5] For example, when there exists backward direction, ring or crisscross in a trajectory, the Fréchet distance value shows no distortion than other distance measurements. Hence,the Fréchet distance metric is more descriptive and more suitable as a measure of the similarity between trajectories.…”

Section: Research Statusmentioning

confidence: 98%

“…Trajectory data differs from simple line data in the strict order of interior nodes, so there are many schemes for the measurement of the trajectory distance, including the maximum distance, the minimum distance, the average distance, Hausdorff distance, Fréchet distance, Dynamic Time Warping (DTW), Longest Common Subsequence (LCSS) and Time Warp Edit Distance (TWED), etc. [4,5].…”

Section: Research Statusmentioning

confidence: 99%

An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold

Guo

Xiong

et al. 2017

IJGI

View full text Add to dashboard Cite

Abstract:The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms.

show abstract

“…Scientists have formulated various similarity measures according to the needs of specific domain applications [4]. Typical similarity measures include Euclidean Distance, Dynamic Time Warping and Hausdorff and Fréchet Distance [5].…”

Section: Introductionmentioning

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

View full text Add to dashboard Cite

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.

show abstract

Review on trajectory similarity measures

Cited by 79 publications

References 18 publications

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

A Segment-Based Trajectory Similarity Measure in the Urban Transportation Systems

An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold

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

Contact Info

Product

Resources

About