Summary of Siamese Multiple Attention Temporal Convolution Networks For Human Mobility Signature Identification, by Zhipeng Zheng et al.
Siamese Multiple Attention Temporal Convolution Networks for Human Mobility Signature Identification
by Zhipeng Zheng, Yuchen Jiang, Shiyao Zhang, Xuetao Wei
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 A novel Siamese Multiple Attention Temporal Convolutional Network (Siamese MA-TCN) is proposed to address the Human Mobility Signature Identification (HuMID) problem. This task involves discerning latent driving behaviors and preferences from diverse driver trajectories for driver identification, with significant implications in domains like ride-hailing and insurance. The current solutions exhibit limitations in adaptability when dealing with lengthy trajectories and struggle to extract crucial local information. To overcome these issues, the proposed model combines the strengths of TCN architecture and multi-head self-attention, enabling the proficient extraction of both local and long-term dependencies. A novel attention mechanism is also designed for efficient aggregation of multi-scale representations. Experimental evaluations on two real-world taxi trajectory datasets demonstrate the model’s ability to extract both local key information and long-term dependencies, showcasing its robustness and adaptability across varying dataset sizes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The HuMID problem aims to identify driving behaviors and preferences from driver trajectories. This is important for ride-hailing and insurance companies, as it can help prevent fraudulent activities. Current solutions have limitations when dealing with long routes and struggle to find important local information. A new model called Siamese MA-TCN is designed to solve this problem. It combines the strengths of two other models to extract both short-term and long-term information from driver trajectories. The results show that this model can be used on different datasets, making it a reliable choice. |
Keywords
» Artificial intelligence » Attention » Convolutional network » Self attention
