scholarly journals Design of intelligent acquisition system for moving object trajectory data under cloud computing

2021 ◽  
Vol 30 (1) ◽  
pp. 763-773
Author(s):  
Yang Zhang ◽  
Abhinav Asthana ◽  
Sudeep Asthana ◽  
Shaweta Khanna ◽  
Ioan-Cosmin Mihai
Keyword(s):  
Cloud Computing ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Hot Spot ◽  
Moving Object ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Spot Area ◽  
Time Period ◽  
Moving Object Trajectory

Abstract In order to study the intelligent collection system of moving object trajectory data under cloud computing, information useful to passengers and taxi drivers is collected from massive trajectory data. This paper uses cloud computing technology, through clustering algorithm and density-based DBSCAN algorithm combined with Map Reduce programming model and design trajectory clustering algorithm. The results show that based on the 8-day data of 15,000 taxis in Shenzhen, the characteristic time period is determined. The passenger hot spot area is obtained by clustering the passenger load points in each time period, which verifies the feasibility of the passenger load point recommendation application based on trajectory clustering. Therefore, in the absence of holidays, the number of passenger hotspots tends to be stable. It is reliable to perform cluster analysis. The recommended application has been demonstrated through experiments, and the implementation results show the rationality of the recommended application design and the feasibility of practice.

Mining Partners in Trajectories

2020 ◽  
Vol 16 (1) ◽  
pp. 22-38
Author(s):  
Diego Vilela Monteiro ◽  
Rafael Duarte Coelho dos Santos ◽  
Karine Reis Ferreira
Keyword(s):  
Moving Objects ◽  
R Package ◽  
Moving Object ◽  
Spatiotemporal Data ◽  
Data Preparation ◽  
Trajectory Data ◽  
Gps Satellites ◽  
Object Trajectories ◽  
Moving Object Trajectory ◽  
Moving Object Trajectories

Spatiotemporal data is everywhere, being gathered from different devices such as Earth Observation and GPS satellites, sensor networks and mobile gadgets. Spatiotemporal data collected from moving objects is of particular interest for a broad range of applications. In the last years, such applications have motivated many pieces of research on moving object trajectory data mining. In this article, it is proposed an efficient method to discover partners in moving object trajectories. Such a method identifies pairs of trajectories whose objects stay together during certain periods, based on distance time series analysis. It presents two case studies using the proposed algorithm. This article also describes an R package, called TrajDataMining, that contains algorithms for trajectory data preparation, such as filtering, compressing and clustering, as well as the proposed method Partner.

An efficient trajectory-clustering algorithm based on an index tree

2011 ◽  
Vol 34 (7) ◽  
pp. 850-861 ◽  
Author(s):  
Guan Yuan ◽  
Shixiong Xia ◽  
Lei Zhang ◽  
Yong Zhou ◽  
Cheng Ji
Keyword(s):  
Radio Frequency Identification ◽  
Clustering Algorithm ◽  
Real Data ◽  
Structural Similarity ◽  
Location Based Services ◽  
Similarity Function ◽  
Data Sets ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Index Tree

With the development of location-based services, such as the Global Positioning System and Radio Frequency Identification, a great deal of trajectory data can be collected. Therefore, how to mine knowledge from these data has become an attractive topic. In this paper, we propose an efficient trajectory-clustering algorithm based on an index tree. Firstly, an index tree is proposed to store trajectories and their similarity matrix, with which trajectories can be retrieved efficiently; secondly, a new conception of trajectory structure is introduced to analyse both the internal and external features of trajectories; then, trajectories are partitioned into trajectory segments according to their corners; furthermore, the similarity between every trajectory segment pairs is compared by presenting the structural similarity function; finally, trajectory segments are grouped into different clusters according to their location in the different levels of the index tree. Experimental results on real data sets demonstrate not only the efficiency and effectiveness of our algorithm, but also the great flexibility that feature sensitivity can be adjusted by different parameters, and the cluster results are more practically significant.

Trajectory Clustering by Sampling and Density

2014 ◽  
Vol 48 (6) ◽  
pp. 74-85 ◽  
Author(s):  
Jiacai Pan ◽  
Qingshan Jiang ◽  
Zheping Shao
Keyword(s):  
Traffic Flow ◽  
Clustering Algorithm ◽  
Moving Objects ◽  
Distance Measure ◽  
Classical Method ◽  
Trajectory Clustering ◽  
Trajectory Data ◽  
Density Based Clustering ◽  
Clustering Quality ◽  
Parameter Values

AbstractThe trajectory data of moving objects contain huge amounts of information pertaining to traffic flow. It is incredibly important to extract valuable knowledge from this particular kind of data. Trajectory clustering is one of the most widely used approaches to complete this extraction. However, the current practice of trajectory clustering always groups similar subtrajectories that are partitioned from the trajectories; these methods would thus lose important information of the trajectory as a whole. To deal with this problem, this paper introduces a new trajectory-clustering algorithm based on sampling and density, which groups similar traffic movement tracks (car, ship, airplane, etc.) for further analysis of the characteristics of traffic flow. In particular, this paper proposes a novel technique of measuring distances between trajectories using point sampling. This distance measure does not divide the trajectory and thus conserves the integrated knowledge of these trajectories. This trajectory clustering approach is a new adaptation of a density-based clustering algorithm to the trajectories of moving objects. This paper then adopts the entropy theory as the heuristic for selecting the parameter values of this algorithm and the sum of the squared error method for measuring the clustering quality. Experiments on real ship trajectory data have shown that this algorithm is superior to the classical method TRACLUSS in the run time and that this method works well in discovering traffic flow patterns.

scholarly journals APDS: A framework for discovering movement pattern from trajectory database

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771988816
Author(s):  
Guan Yuan ◽  
Zhongqiu Wang ◽  
Zhixiao Wang ◽  
Fukai Zhang ◽  
Li Yuan ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Pattern Mining ◽  
Pattern Discovery ◽  
Movement Pattern ◽  
Moving Object ◽  
Periodic Pattern ◽  
Trajectory Data ◽  
Discovery System ◽  
Spatiotemporal Data Mining

Currently, the boosting of location acquisition devices makes it possible to track all kinds of moving objects, and collect and store their trajectories in database. Therefore, how to find knowledge from huge amount of trajectory data has become an attractive topic. Movement pattern is an efficient way to understand moving objects’ behavior and analyze their habits. To promote the application of spatiotemporal data mining, a moving object activity pattern discovery system is designed and implemented in this article. First of all, raw trajectory data are preprocessed using methods like data clean, data interpolation, and compression. Second, a simplified density-based trajectory clustering algorithm is implemented to find and group similar movement patterns. Third, in order to discover the trends and periodicity of movement pattern, a trajectory periodic pattern mining algorithm is developed. Finally, comprehensive experiments with different parameters are conducted to validate the pattern discovery system. The experimental results show that the system is robust and efficient to analyze moving object trajectory data and discover useful patterns.

A review of moving object trajectory clustering algorithms

2016 ◽  
Vol 47 (1) ◽  
pp. 123-144 ◽  
Author(s):  
Guan Yuan ◽  
Penghui Sun ◽  
Jie Zhao ◽  
Daxing Li ◽  
Canwei Wang
Keyword(s):  
Clustering Algorithms ◽  
Moving Object ◽  
Trajectory Clustering ◽  
Moving Object Trajectory

scholarly journals An Overview of Moving Object Trajectory Compression Algorithms

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Penghui Sun ◽  
Shixiong Xia ◽  
Guan Yuan ◽  
Daxing Li
Keyword(s):  
Moving Objects ◽  
Moving Object ◽  
Trajectory Data ◽  
Compression Algorithms ◽  
Valuable Data ◽  
Implementation Processes ◽  
Exponential Increase ◽  
Validation Criteria ◽  
Gps Devices ◽  
Moving Object Trajectory

Compression technology is an efficient way to reserve useful and valuable data as well as remove redundant and inessential data from datasets. With the development of RFID and GPS devices, more and more moving objects can be traced and their trajectories can be recorded. However, the exponential increase in the amount of such trajectory data has caused a series of problems in the storage, processing, and analysis of data. Therefore, moving object trajectory compression undoubtedly becomes one of the hotspots in moving object data mining. To provide an overview, we survey and summarize the development and trend of moving object compression and analyze typical moving object compression algorithms presented in recent years. In this paper, we firstly summarize the strategies and implementation processes of classical moving object compression algorithms. Secondly, the related definitions about moving objects and their trajectories are discussed. Thirdly, the validation criteria are introduced for evaluating the performance and efficiency of compression algorithms. Finally, some application scenarios are also summarized to point out the potential application in the future. It is hoped that this research will serve as the steppingstone for those interested in advancing moving objects mining.

scholarly journals A DBSCAN based Algorithm for Ship Spot Area Detection in AIS Trajectory Data

2019 ◽  
Vol 291 ◽  
pp. 01008 ◽  
Author(s):  
Bao Lei
Keyword(s):  
Clustering Algorithm ◽  
Original Data ◽  
Detection Algorithm ◽  
Traffic Information ◽  
Data Sets ◽  
Trajectory Data ◽  
Location Data ◽  
Spot Area ◽  
Maritime Traffic ◽  
Sample Data

The big data acquired by AIS system contains abundant maritime traffic information. With the wide application of data mining in various fields in recent years, the mining on AIS data has draw attention of related researchers. Based on the ship AIS location data, this paper studies the relevant spot area detection algorithm. Firstly, the sample data are pre-processed from the original data, and the residence point of each ship is identified according to the ship speed and course change. Then a DBSCAN based clustering algorithm is used to cluster several latitude and longitude lattice, that is spot areas. The experiments on real AIS data sets shows that the algorithm is efficient and correct.

The Microstructure of Fused Cast AZS Materials before, during and after the Use for Glass Melting

2008 ◽  
Vol 39-40 ◽  
pp. 607-612
Author(s):  
Bernhard Fleischmann
Keyword(s):  
Glass Furnace ◽  
Glass Melting ◽  
Hot Spot ◽  
Float Glass ◽  
Vitreous Phase ◽  
Spot Area ◽  
Furnace Campaign ◽  
Three Phases

A part of a soldier block, placed in a float glass furnace near the hot spot area, was investigated to learn about the changes in the microstructure during the production of the block, during the use for glass melting and after the shut down of the furnace and the cooling of the block. Beside the three phases after the production (baddeleyite, corundum, vitreous phase) during use as a soldier block mullite and secondary corundum as well as secondary zirconia may occure. Cooling down the used block after the furnace campaign the beginning of the crystallisation of feldspars may be seen.

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