Next Article in Journal
Supporting Disaster Resilience Spatial Thinking with Serious GeoGames: Project Lily Pad
Previous Article in Journal
Village-Level Homestead and Building Floor Area Estimates Based on UAV Imagery and U-Net Algorithm
Previous Article in Special Issue
Uber Movement Data: A Proxy for Average One-way Commuting Times by Car
Open AccessArticle

GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data

College of Computer Science, National University of Defense Technology, Changsha 410073, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(6), 404; https://doi.org/10.3390/ijgi9060404
Received: 5 May 2020 / Revised: 7 June 2020 / Accepted: 13 June 2020 / Published: 20 June 2020
(This article belongs to the Special Issue Recent Trends in Location Based Services and Science)
The popularity of mobile locate-enabled devices and Location Based Service (LBS) generates massive spatio-temporal data every day. Due to the close relationship between behavior patterns and movement trajectory, trajectory data mining has been applied in numerous fields to find the behavior pattern. Among them, discovering traveling companions is one of the most fundamental techniques in these areas. This paper proposes a flexible framework named GroupSeeker for discovering traveling companions in vast real-world trajectory data. In the real-world data resource, it is significant to avoid the companion candidate omitting problem happening in the time-snapshot-slicing-based method. These methods do not work well with the sparse real-world data, which is caused by the equipment sampling failure or manual intervention. In this paper, a 5-stage framework including Data Preprocessing, Spatio-temporal Clustering, Candidate Voting, Pseudo-companion Filtering, and Group Merging is proposed to discover traveling companions. The framework even works well when there is a long time span during several days. The experiments result on two real-world data sources which offer massive amount of data subsets with different scale and different sampling frequencies show the effective and robustness of this framework. Besides, the proposed framework has a higher-efficiency performing when discovering satisfying companions over a long-term period. View Full-Text
Keywords: traveling companion discovery; spatio-temporal trajectory mining; framework; association analysis; clusteirng; parameter-setting strategy traveling companion discovery; spatio-temporal trajectory mining; framework; association analysis; clusteirng; parameter-setting strategy
Show Figures

Figure 1

MDPI and ACS Style

Yao, R.; Wang, F.; Chen, S.; Zhao, S. GroupSeeker: An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data. ISPRS Int. J. Geo-Inf. 2020, 9, 404.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop