Mingming Wu
ABSTRACT
Trajectory recovery can benefit many applications such as migration pattern studies of animal and finding hot routes in the urban city. It is necessary to recover trajectory with limited trajectory points to utilize collected trajectory data in a reasonable and efficient way and to provide the better location based service for users. However, the trajectory data involves complex and nonlinear spatial-temporal impacts which cannot be captured by traditional trajectory recovery methods. Moreover, the existing methods consider little about the correlations between trajectory and traffic pattern in the urban city. The superiority of deep neural network makes it possible to recover trajectory with low data quality. We propose a Multi-Task Representation Learning Network (MRL-Net) framework which models the complex nonlinear spatial-temporal correlations in trajectory data with representation learning technique and capture the dependencies of trajectory points with recurrent neural networks. To the best of our knowledge, it is the first paper to address the trajectory recovery problem with representation learning and multi-task learning. Experiments on real-world trajectory data show that our model is superior to state-of-the-art methods.
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Index Terms
- Multi-Task Representation Learning Network for Trajectory Recovery
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