A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks
KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Machine Intelligence & kNowledge Engineering Lab
KAUST Grant NumberURF/1/3756-01-01
Online Publication Date2021-08-14
Print Publication Date2021-08-14
Permanent link to this recordhttp://hdl.handle.net/10754/670655
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AbstractTrajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.
CitationHan, P., Wang, J., Yao, D., Shang, S., & Zhang, X. (2021). A Graph-based Approach for Trajectory Similarity Computation in Spatial Networks. Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. doi:10.1145/3447548.3467337
SponsorsThe research reported in this publication was supported by funding from King Abdullah University of Science and Technology (KAUST), under award number URF/1/3756-01-01. And this paper was supported by NSFC. U2001212, 62032001 and 61932004. More-over, this work was also supposed by the National Natural Science Foundation of China No. 62002343.
Conference/Event nameKDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining