Trajectory Data Pattern Mining

2014 ◽  
pp. 51-66 ◽  
Author(s):  
Elio Masciari ◽  
Gao Shi ◽  
Carlo Zaniolo
Keyword(s):  
Pattern Mining ◽  
Trajectory Data

Research on Ship Classification Based on Trajectory Features

2017 ◽  
Vol 71 (1) ◽  
pp. 100-116 ◽  
Author(s):  
Kai Sheng ◽  
Zhong Liu ◽  
Dechao Zhou ◽  
Ailin He ◽  
Chengxu Feng
Keyword(s):  
Pattern Mining ◽  
Extraction Methods ◽  
Behaviour Pattern ◽  
Automatic Identification ◽  
Identification System ◽  
Trajectory Data ◽  
Trajectory Features ◽  
Ship Classification ◽  
Historical Trajectory

It is important for maritime authorities to effectively classify and identify unknown types of ships in historical trajectory data. This paper uses a logistic regression model to construct a ship classifier by utilising the features extracted from ship trajectories. First of all, three basic movement patterns are proposed according to ship sailing characteristics, with related sub-trajectory partitioning algorithms. Subsequently, three categories of trajectory features with their extraction methods are presented. Finally, a case study on building a model for classifying fishing boats and cargo ships based on real Automatic Identification System (AIS) data is given. Experimental results indicate that the proposed classification method can meet the needs of recognising uncertain types of targets in historical trajectory data, laying a foundation for further research on camouflaged ship identification, behaviour pattern mining, outlier behaviour detection and other applications.

scholarly journals UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model

2019 ◽  
Vol 8 (11) ◽  
pp. 518 ◽  
Author(s):  
Ning Guo ◽  
Shashi Shekhar ◽  
Wei Xiong ◽  
Luo Chen ◽  
Ning Jing
Keyword(s):  
Similarity Measure ◽  
Pattern Mining ◽  
Clustering Methods ◽  
Trajectory Data ◽  
Real World Data ◽  
Asynchronous Sampling ◽  
Ellipse Model ◽  
Spatiotemporal Clustering ◽  
Motion Features ◽  
Trajectory Similarity

Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

scholarly journals Parallel Sequential Pattern Mining of Massive Trajectory Data

2010 ◽  
Vol 3 (3) ◽  
pp. 343-356 ◽  
Author(s):  
Shaojie Qiao ◽  
Tianrui Li ◽  
Jing Peng ◽  
Jiangtao Qiu
Keyword(s):  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Trajectory Data

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.

Trajectory Pattern Mining: Methods and Applications

2014 ◽  
Vol 490-491 ◽  
pp. 1361-1367
Author(s):  
Xin Huang ◽  
Hui Juan Chen ◽  
Mao Gong Zheng ◽  
Ping Liu ◽  
Jing Qian
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Pattern Mining ◽  
Data Sets ◽  
Trajectory Data ◽  
Data Mining Algorithms ◽  
Mining Methods ◽  
Trajectory Pattern ◽  
Mining Algorithms ◽  
Trajectory Pattern Mining

With the advent of location-based social media and locationacquisition technologies, trajectory data are becoming more and more ubiquitous in the real world. A lot of data mining algorithms have been successfully applied to trajectory data sets. Trajectory pattern mining has received a lot of attention in recent years. In this paper, we review the most inuential methods as well as typical applications within the context of trajectory pattern mining.

scholarly journals SPATIO-TEMPORAL PATTERN MINING ON TRAJECTORY DATA USING ARM

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 395-399 ◽  
Author(s):  
S. Khoshahval ◽  
M. Farnaghi ◽  
M. Taleai
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Pattern Mining ◽  
Activity Patterns ◽  
Handheld Devices ◽  
Trajectory Data ◽  
Hidden Information ◽  
Rule Mining ◽  
User Activity ◽  
Gps Trajectories

Preliminary mobile was considered to be a device to make human connections easier. But today the consumption of this device has been evolved to a platform for gaming, web surfing and GPS-enabled application capabilities. Embedding GPS in handheld devices, altered them to significant trajectory data gathering facilities. Raw GPS trajectory data is a series of points which contains hidden information. For revealing hidden information in traces, trajectory data analysis is needed. One of the most beneficial concealed information in trajectory data is user activity patterns. In each pattern, there are multiple stops and moves which identifies users visited places and tasks. This paper proposes an approach to discover user daily activity patterns from GPS trajectories using association rules. Finding user patterns needs extraction of user’s visited places from stops and moves of GPS trajectories. In order to locate stops and moves, we have implemented a place recognition algorithm. After extraction of visited points an advanced association rule mining algorithm, called Apriori was used to extract user activity patterns. This study outlined that there are useful patterns in each trajectory that can be emerged from raw GPS data using association rule mining techniques in order to find out about multiple users’ behaviour in a system and can be utilized in various location-based applications.

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.

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