TY - JOUR
T1 - A Prediction Method for Destination Based on the Semantic Transfer Model
AU - Han, Qilong
AU - Lu, Dan
AU - Zhang, Kejia
AU - Du, Xiaojiang
AU - Guizani, Mohsen
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2019
Y1 - 2019
N2 - 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 these follow-up works is that they blindly rely on the Markov process, ignoring the geographical distribution and the time property of the trajectories. In this paper, we propose a novel destination prediction algorithm, called STTL, based on the time property of the partial trajectory, along with the semantic transfer probability model trained in advance. We have conducted extensive experiments on the Shanghai Taxi dataset. The experimental results show that the STTL outperforms other state-of-the-art approaches.
AB - 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 these follow-up works is that they blindly rely on the Markov process, ignoring the geographical distribution and the time property of the trajectories. In this paper, we propose a novel destination prediction algorithm, called STTL, based on the time property of the partial trajectory, along with the semantic transfer probability model trained in advance. We have conducted extensive experiments on the Shanghai Taxi dataset. The experimental results show that the STTL outperforms other state-of-the-art approaches.
KW - Destination prediction
KW - historical trajectories
KW - semantic transfer probability
KW - time-property
UR - http://www.scopus.com/inward/record.url?scp=85068320238&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85068320238&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2019.2918594
DO - 10.1109/ACCESS.2019.2918594
M3 - Article
AN - SCOPUS:85068320238
VL - 7
SP - 73756
EP - 73763
JO - IEEE Access
JF - IEEE Access
M1 - 8721085
ER -