scholarly journals A Trajectory Partition Method Based on Combined Movement Features

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Ji Tang ◽  
Linfeng Liu ◽  
Jiagao Wu
Keyword(s):  
Data Mining ◽  
Change Points ◽  
Feature Points ◽  
Trajectory Data ◽  
Trade Off ◽  
Movement Trajectories ◽  
Trajectory Data Mining ◽  
Primary Procedure ◽  
Simulation Results ◽  
Partition Method

Trajectory data mining has become an increasing concern in the location-based applications, and the trajectory partition is taken as the primary procedure of trajectory data mining. The amount of movement trajectories of nodes is typically very large, and the trajectory shapes are extremely diverse, which makes the trajectory partition a vital issue to the trajectory data mining results. In this work, the movement behaviors of nodes are analyzed from the aspects of moving speeds, stop points, and moving directions, and then a novel Trajectory Partition Method based on combined movement Features (TPMF) is proposed to partition the trajectories. In TPMF, we first extract the change points where the movement speeds of nodes are varied significantly; then, we extract the stop points by detecting the speed variations of nodes; finally, the Douglas-Peucker algorithm is applied to partition the subtrajectories according to the extracted feature points (change points and stop points). Simulations are carried out on the Geolife trajectory dataset, and the simulation results indicate that TPMF can achieve a preferable trade-off between the simplification rate and the trajectory partition error, while the running time is shortened as well.

scholarly journals Handover detection approach based on trajectory data mining techniques

2018 ◽  
Vol 2018 (16) ◽  
pp. 1534-1537 ◽  
Author(s):  
Nan Han ◽  
Shaojie Qiao ◽  
Dunhu Liu ◽  
Peng Ding ◽  
Yongqing Zhang ◽  
...  
Keyword(s):  
Data Mining ◽  
Trajectory Data ◽  
Data Mining Techniques ◽  
Detection Approach ◽  
Trajectory Data Mining

The discovery of personally semantic places based on trajectory data mining

2016 ◽  
Vol 173 ◽  
pp. 1142-1153 ◽  
Author(s):  
Mingqi Lv ◽  
Ling Chen ◽  
Zhenxing Xu ◽  
Yinglong Li ◽  
Gencai Chen
Keyword(s):  
Data Mining ◽  
Trajectory Data ◽  
Trajectory Data Mining

scholarly journals Big Trajectory Data Mining: A Survey of Methods, Applications, and Services

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4571
Author(s):  
Di Wang ◽  
Tomio Miwa ◽  
Takayuki Morikawa
Keyword(s):  
Data Mining ◽  
Social Dynamics ◽  
Trajectory Data ◽  
Traffic Dynamics ◽  
Spatiotemporal Information ◽  
Trajectory Mining ◽  
Trajectory Data Mining ◽  
Urban Problems ◽  
Practical Implications ◽  
Wide Usage

The increasingly wide usage of smart infrastructure and location-aware terminals has helped increase the availability of trajectory data with rich spatiotemporal information. The development of data mining and analysis methods has allowed researchers to use these trajectory datasets to identify urban reality (e.g., citizens’ collective behavior) in order to solve urban problems in transportation, environment, public security, etc. However, existing studies in this field have been relatively isolated, and an integrated and comprehensive review is lacking the problems that have been tackled, methods that have been tested, and services that have been generated from existing research. In this paper, we first discuss the relationships among the prevailing trajectory mining methods and then, classify the applications of trajectory data into three major groups: social dynamics, traffic dynamics, and operational dynamics. Finally, we briefly discuss the services that can be developed from studies in this field. Practical implications are also delivered for participants in trajectory data mining. With a focus on relevance and association, our review is aimed at inspiring researchers to identify gaps among tested methods and guiding data analysts and planners to select the most suitable methods for specific problems.

Trajectory data mining: integrating semantics

2013 ◽  
Vol 26 (5) ◽  
pp. 516-535 ◽  
Author(s):  
Ahmed Elragal ◽  
Nada El-Gendy
Keyword(s):  
Data Mining ◽  
Trajectory Data ◽  
Trajectory Data Mining

scholarly journals Trajectory Data Mining in Distributed Sensor Networks

2015 ◽  
Vol 11 (7) ◽  
pp. 913165
Author(s):  
Shaojie Qiao ◽  
Huidong (Warren) Jin ◽  
Yunjun Gao ◽  
Lu-An Tang ◽  
Huanlai Xing
Keyword(s):  
Data Mining ◽  
Sensor Networks ◽  
Trajectory Data ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Trajectory Data Mining

scholarly journals PARALLEL SPATIOTEMPORAL SPECTRAL CLUSTERING WITH MASSIVE TRAJECTORY DATA

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1173-1180 ◽  
Author(s):  
Y. Z. Gu ◽  
K. Qin ◽  
Y. X. Chen ◽  
M. X. Yue ◽  
T. Guo
Keyword(s):  
Data Mining ◽  
Computational Complexity ◽  
Spectral Clustering ◽  
Large Scale ◽  
Trajectory Data ◽  
Wuhan City ◽  
Large Scale Problems ◽  
Trajectory Data Mining ◽  
Taxi Trajectory ◽  
High Computational Complexity

Massive trajectory data contains wealth useful information and knowledge. Spectral clustering, which has been shown to be effective in finding clusters, becomes an important clustering approaches in the trajectory data mining. However, the traditional spectral clustering lacks the temporal expansion on the algorithm and limited in its applicability to large-scale problems due to its high computational complexity. This paper presents a parallel spatiotemporal spectral clustering based on multiple acceleration solutions to make the algorithm more effective and efficient, the performance is proved due to the experiment carried out on the massive taxi trajectory dataset in Wuhan city, China.

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