The promotion / demotion algorithm of moving objects with large velocity differences in time-parameterized spatio-temporal index

2020 ◽  
Vol 107 ◽  
pp. 645-658
Author(s):  
Chumsu Kim ◽  
Bonghee Hong ◽  
Jiwan Lee
Keyword(s):  
Moving Objects ◽  
Temporal Index ◽  
Large Velocity ◽  
Spatio Temporal

Reverse Nearest Neighbors query of moving objects based on HRNN-tree

2019 ◽  
Vol 13 ◽  
Author(s):  
Miao Wang ◽  
Xiaotong Wang ◽  
Songyang Li ◽  
Xiaodong Liu ◽  
Song Li
Keyword(s):  
Quantitative Analysis ◽  
Spatial Analysis ◽  
Experimental Evaluation ◽  
Moving Objects ◽  
Nearest Neighbors ◽  
Spatial Processing ◽  
Temporal Database ◽  
Temporal Index ◽  
Spatio Temporal

Reverse nearest neighbors query as an indispensable part of spatial retrieve plays an important role in spatial analysis and spatial processing. In this paper, we target the problem of reverse nearest neighbors query for moving objects. We devised a spatio-temporal index suitable for this problem firstly and then an algorithm for reverse nearest neighbors search is proposed based on this index. The results of experimental evaluation demonstrate that this algorithm outperforms others for reverse nearest neighbors queries of moving objects. This work can better improve and enhance the power of quantitative analysis and process for spatio-temporal database.

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

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