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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent Encoder-Decoder Architecture with
Asif Nawaz1, Zhiqiu Huang1,2,3, Senzhang Wang1
1Department of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China.
Sensors (Basel, Switzerland)
|September 12, 2020
Summary
This study introduces a deep learning model to fill in missing GPS data points in trajectories. The enhanced model improves accuracy for navigation and tracking in urban computing systems.
Area of Science:
- Ubiquitous Computing
- Data Science
Background:
- Large GPS datasets are crucial for urban computing, but data quality issues like sparse and incomplete trajectories hinder performance.
- Existing methods for handling incomplete GPS data often rely on complex heuristics and require domain expertise.
Purpose of the Study:
- To develop a deep learning model for generating missing points in GPS trajectories.
- To enhance the model's performance using an attention mechanism.
Main Methods:
- Proposed a deep learning-based bidirectional convolutional recurrent encoder-decoder architecture.
- Integrated an attention mechanism between the encoder and decoder.
- Evaluated the model on the Microsoft Geolife dataset with varying grid resolutions and missing data lengths.
Main Results:
- The proposed model significantly reduced the average displacement error compared to state-of-the-art methods.
- Performance improvements were observed across different grid resolutions and lengths of missing GPS segments.
Conclusions:
- Deep learning, specifically the proposed encoder-decoder architecture with attention, is effective for reconstructing incomplete GPS trajectories.
- This approach offers a more robust and less domain-dependent solution for improving GPS data quality in urban computing.
