A Prediction Method for Destination Based on the Semantic Transfer Model
With the widespread use of the mobile devices, destination prediction has become an important issue for location-based services (LBSs). Most existing methods are based on various Markov chain models, in which the predicted destinations are trained by historical trajectories. A problem among most of...
Main Authors: | Qilong Han, Dan Lu, Kejia Zhang, Xiaojiang Du, Mohsen Guizani |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2019-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/8721085/ |
Similar Items
-
Destination Prediction by Identifying and Clustering Prominent Features from Public Trajectory Datasets
by: Li Yang, et al.
Published: (2015-07-01) -
Probabilistic Time Geographic Modeling Method Considering POI Semantics
by: Ai-Sheng Wang, et al.
Published: (2024-01-01) -
Segmented Trajectory Clustering-Based Destination Prediction in IoVs
by: Chao Wang, et al.
Published: (2020-01-01) -
A multi-task learning-based framework for global maritime trajectory and destination prediction with AIS data
by: Wells Wang, et al.
Published: (2022-01-01) -
SEABIG: A Deep Learning-Based Method for Location Prediction in Pedestrian Semantic Trajectories
by: Wanlong Zhang, et al.
Published: (2019-01-01)