scholarly journals Healthcare Trajectory Mining by Combining Multidimensional Component and Itemsets

2013 ◽  
pp. 109-123 ◽  
Author(s):  
Elias Egho ◽  
Chedy Raïssi ◽  
Dino Ienco ◽  
Nicolas Jay ◽  
Amedeo Napoli ◽  
...  
Keyword(s):  
Trajectory Mining ◽  
Healthcare Trajectory

scholarly journals Applicability Evaluation of Several Spatial Clustering Methods in Spatiotemporal Data Mining of Floating Car Trajectory

2021 ◽  
Vol 10 (3) ◽  
pp. 161
Author(s):  
Hao-xuan Chen ◽  
Fei Tao ◽  
Pei-long Ma ◽  
Li-na Gao ◽  
Tong Zhou
Keyword(s):  
Spatial Analysis ◽  
Spatial Clustering ◽  
Heat Index ◽  
Operating Efficiency ◽  
Clustering Methods ◽  
Clustering Method ◽  
Trajectory Mining ◽  
Density Peaks ◽  
Analysis Methods ◽  
Taxi Trajectory

Spatial analysis is an important means of mining floating car trajectory information, and clustering method and density analysis are common methods among them. The choice of the clustering method affects the accuracy and time efficiency of the analysis results. Therefore, clarifying the principles and characteristics of each method is the primary prerequisite for problem solving. Taking four representative spatial analysis methods—KMeans, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Clustering by Fast Search and Find of Density Peaks (CFSFDP), and Kernel Density Estimation (KDE)—as examples, combined with the hotspot spatiotemporal mining problem of taxi trajectory, through quantitative analysis and experimental verification, it is found that DBSCAN and KDE algorithms have strong hotspot discovery capabilities, but the heat regions’ shape of DBSCAN is found to be relatively more robust. DBSCAN and CFSFDP can achieve high spatial accuracy in calculating the entrance and exit position of a Point of Interest (POI). KDE and DBSCAN are more suitable for the classification of heat index. When the dataset scale is similar, KMeans has the highest operating efficiency, while CFSFDP and KDE are inferior. This paper resolves to a certain extent the lack of scientific basis for selecting spatial analysis methods in current research. The conclusions drawn in this paper can provide technical support and act as a reference for the selection of methods to solve the taxi trajectory mining problem.

scholarly journals Mobility Modes Awareness from Trajectories Based on Clustering and a Convolutional Neural Network

2019 ◽  
Vol 8 (5) ◽  
pp. 208 ◽  
Author(s):  
Rui Chen ◽  
Mingjian Chen ◽  
Wanli Li ◽  
Jianguang Wang ◽  
Xiang Yao
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Land Use Planning ◽  
Intelligent Transportation ◽  
Identification Accuracy ◽  
Trajectory Data ◽  
Trajectory Mining ◽  
World Maritime ◽  
Activity Information ◽  
Identification Model

Massive trajectory data generated by ubiquitous position acquisition technology are valuable for knowledge discovery. The study of trajectory mining that converts knowledge into decision support becomes appealing. Mobility modes awareness is one of the most important aspects of trajectory mining. It contributes to land use planning, intelligent transportation, anomaly events prevention, etc. To achieve better comprehension of mobility modes, we propose a method to integrate the issues of mobility modes discovery and mobility modes identification together. Firstly, route patterns of trajectories were mined based on unsupervised origin and destination (OD) points clustering. After the combination of route patterns and travel activity information, different mobility modes existing in history trajectories were discovered. Then a convolutional neural network (CNN)-based method was proposed to identify the mobility modes of newly emerging trajectories. The labeled history trajectory data were utilized to train the identification model. Moreover, in this approach, we introduced a mobility-based trajectory structure as the input of the identification model. This method was evaluated with a real-world maritime trajectory dataset. The experiment results indicated the excellence of this method. The mobility modes discovered by our method were clearly distinguishable from each other and the identification accuracy was higher compared with other techniques.

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.

Precision Advertising Based on the Scene of Trajectory Mining

2016 ◽  
Vol 9 (4) ◽  
pp. 135-142
Author(s):  
Yun Wu ◽  
Feng Yang ◽  
Min Ren ◽  
Le Han
Keyword(s):  
Trajectory Mining
Sign in / Sign up

Export Citation Format

Share Document