Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
IEEE Transactions on Knowledge and Data Engineering(2024)
摘要
Recovering intermediate missing GPS points in a sparse trajectory, while
adhering to the constraints of the road network, could offer deep insights into
users' moving behaviors in intelligent transportation systems. Although recent
studies have demonstrated the advantages of achieving map-constrained
trajectory recovery via an end-to-end manner, they still face two significant
challenges. Firstly, existing methods are mostly sequence-based models. It is
extremely hard for them to comprehensively capture the micro-semantics of
individual trajectory, including the information of each GPS point and the
movement between two GPS points. Secondly, existing approaches ignore the
impact of the macro-semantics, i.e., the road conditions and the people's
shared travel preferences reflected by a group of trajectories. To address the
above challenges, we propose a Micro-Macro Spatial-Temporal Graph-based
Encoder-Decoder (MM-STGED). Specifically, we model each trajectory as a graph
to efficiently describe the micro-semantics of trajectory and design a novel
message-passing mechanism to learn trajectory representations. Additionally, we
extract the macro-semantics of trajectories and further incorporate them into a
well-designed graph-based decoder to guide trajectory recovery. Extensive
experiments conducted on sparse trajectories with three different sampling
intervals that are respectively constructed from two real-world trajectory
datasets demonstrate the superiority of our proposed model.
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关键词
Graph neural network,macro-semantics,map-constrained trajectory recovery,micro-semantics,spatial-temporal data mining
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