GroupSeeker
An Applicable Framework for Travel Companion Discovery from Vast Trajectory Data
Bibliographic Data
| ID | 22034069 |
|---|---|
| Authors | Ruihong Yao (National University of Defense Technology), Yao Ruihong (National University of Defense Technology), Fei Wang (0000-0003-0860-6317, National University of Defense Technology, corresponding author), Shuhui Chen (0000-0001-7413-8174, National University of Defense Technology), Shuang Zhao (0000-0002-3392-0554, National University of Defense Technology) |
| Year | 2020 |
| Volume | 9 |
| Issue | 6 |
| Pages | 404 |
| Publication date | 2020-06-20 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | ISPRS International Journal of Geo-Information (JOURNAL) |
| Journal identifiers | ISSN: 2220-9964 • E-ISSN: 2220-9964 |
| Publisher | MDPI AG (PUBLISHER • IT) |
| DOI | 10.3390/ijgi9060404 |
| OpenAlex | W3036091369 |
| Language | EN |
| References cited | 38 |
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
Big data · Cluster analysis · Data mining · Database · Skyline · Trajectory · Computer Science · Data Management and Algorithms · Geographic Information Systems Studies · Human Mobility and Location-Based Analysis · Artificial Intelligence
| Highly cited | No |
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