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.

scholarly journals Vessel Crowd Movement Pattern Mining for Maritime Traffic Management

2019 ◽  
Vol 10 (2) ◽  
pp. 105-115
Author(s):  
Rong Wen ◽  
Wenjing Yan
Keyword(s):  
Decision Making ◽  
Traffic Management ◽  
Clustering Algorithm ◽  
Pattern Mining ◽  
Movement Pattern ◽  
Temporal Data Mining ◽  
Trajectory Data ◽  
Maritime Traffic ◽  
Travel Behaviors ◽  
Crowd Movement

Abstract The goal of maritime traffic management is to provide a safe and efficient maritime environment for different type of vessels facilitating port logistics and supply chain business. However, current maritime traffic management mainly relies on the massive individual vessel’s data for decision making. Lack of macro-level understanding of vessel crowd movement around port challenges maritime safety and traffic efficiency. In this paper, we describe a spatio-temporal data mining method to discover crowd movement patterns of vessels from their short-term history data. The method first captures vessels’ crowd movement features by building vessels’ tracklets with their speed and location. A movement vector clustering algorithm is developed to find different travel behaviors for different group of vessels. With nonparametric regression on the classified vessel movement vectors which represent the crowd travel behaviors, an overall vessel movement pattern can then be discovered. In this research, we tested real trajectory data of vessels near Singapore ports. Comparing with the actual massive vessel movement data, we found that this method was able to extract vessels’ crowd movement information. The hotspots on risk area in terms of vessel traffic and speed can be identified. The method can be used to provide decision-making support for maritime traffic management.

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.

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.

Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data

2017 ◽  
Vol 27 (03) ◽  
pp. 399-421 ◽  
Author(s):  
Sana Chakri ◽  
Said Raghay ◽  
Salah El Hadaj
Keyword(s):  
Knowledge Discovery ◽  
Moving Objects ◽  
Recent Literature ◽  
Geographical Information ◽  
Spatiotemporal Data ◽  
Trajectory Data ◽  
Success Story ◽  
Time Intervals ◽  
Geographical Data ◽  
Spatiotemporal Data Mining

Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.

scholarly journals Mobility Data Warehouses

2019 ◽  
Vol 8 (4) ◽  
pp. 170 ◽  
Author(s):  
Alejandro Vaisman ◽  
Esteban Zimányi
Keyword(s):  
Spatial Data ◽  
Moving Objects ◽  
Moving Object ◽  
Data Warehouses ◽  
Mobility Analysis ◽  
Trajectory Data ◽  
Data Types ◽  
Seamless Integration ◽  
Moving Object Database ◽  
Mobility Data

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB.

Particle Swarm Clustering-Based Moving Object Segmentation in the H.264 Compressed Domain

2012 ◽  
Vol 433-440 ◽  
pp. 4841-4844
Author(s):  
Pei Wang ◽  
Jing Wang
Keyword(s):  
Clustering Algorithm ◽  
Moving Objects ◽  
Object Segmentation ◽  
Median Filter ◽  
Particle Swarm ◽  
Moving Object ◽  
Compressed Domain ◽  
Motion Vectors ◽  
Moving Object Segmentation ◽  
Vector Median

An approach of the moving object segmentation is proposed in this paper. Firstly the motion fields are extracted from the compressed stream, where the noise and the unreal motion blocks are removed by vector median filter. Then the motion vectors are accumulated by motion estimation, in order to get denser and prominent motion vectors. Finally the moving objects are segmented adaptively by particle swarm clustering algorithm. It is demonstrated by the experimental results that the moving objects in the compressed domain can be segmented effectively.

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 QB4MobOLAP: A Vocabulary Extension for Mobility OLAP on the Semantic Web

Algorithms ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 265
Author(s):  
Irya Wisnubhadra ◽  
Safiza Kamal Baharin ◽  
Nurul A. Emran ◽  
Djoko Budiyanto Setyohadi
Keyword(s):  
Semantic Web ◽  
Spatial Data ◽  
Moving Objects ◽  
Open Data ◽  
Moving Object ◽  
Online Analytical Processing ◽  
Multidimensional Data ◽  
Trajectory Data ◽  
Advanced Analysis ◽  
Analytical Processing

The accessibility of devices that track the positions of moving objects has attracted many researchers in Mobility Online Analytical Processing (Mobility OLAP). Mobility OLAP makes use of trajectory data warehousing techniques, which typically include a path of moving objects at a particular point in time. The Semantic Web (SW) users have published a large number of moving object datasets that include spatial and non-spatial data. These data are available as open data and require advanced analysis to aid in decision making. However, current SW technologies support advanced analysis only for multidimensional data warehouses and Online Analytical Processing (OLAP) over static spatial and non-spatial SW data. The existing technology does not support the modeling of moving object facts, the creation of basic mobility analytical queries, or the definition of fundamental operators and functions for moving object types. This article introduces the QB4MobOLAP vocabulary, which enables the analysis of mobility data stored in RDF cubes. This article defines Mobility OLAP operators and SPARQL user-defined functions. As a result, QB4MobOLAP vocabulary and the Mobility OLAP operators are evaluated by applying them to a practical use case of transportation analysis involving 8826 triples consisting of approximately 7000 fact triples. Each triple contains nearly 1000 temporal data points (equivalent to 7 million records in conventional databases). The execution of six pertinent spatiotemporal analytics query samples results in a practical, simple model with expressive performance for the enabling of executive decisions on transportation analysis.

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