Summary of Micro-macro Spatial-temporal Graph-based Encoder-decoder For Map-constrained Trajectory Recovery, by Tonglong Wei et al.
Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder for Map-Constrained Trajectory Recovery
by Tonglong Wei, Youfang Lin, Yan Lin, Shengnan Guo, Lan Zhang, Huaiyu Wan
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach, Micro-Macro Spatial-Temporal Graph-based Encoder-Decoder (MM-STGED), to recover intermediate missing GPS points in a sparse trajectory while adhering to road network constraints. The existing sequence-based models struggle to capture the micro-semantics of individual trajectories and ignore macro-semantics, such as road conditions and shared travel preferences. MM-STGED addresses these challenges by modeling each trajectory as a graph to describe its micro-semantics and designing a message-passing mechanism to learn trajectory representations. The model also incorporates macro-semantics into a graph-based decoder to guide trajectory recovery. Experimental results on sparse trajectories from two real-world datasets demonstrate the superiority of MM-STGED. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us better understand how people move around in cities using GPS data. Current methods for filling in missing GPS points have limitations, such as not considering important information like road conditions and what most people do. The researchers propose a new way to fill in these gaps by looking at individual movements (micro-semantics) and group patterns (macro-semantics). They tested their method on real-world GPS data and found it outperforms existing approaches. |
Keywords
» Artificial intelligence » Decoder » Encoder decoder » Semantics
