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DeepMM: Deep Learning Based Map Matching With Data Augmentation
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DOI：10.1109/TMC.2020.3043500.png)
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As a fundamental component in map service, map matching is of great importance for many trajectory-based applications, e.g., route optimization, traffic scheduling, and fleet management. In practice, Hidden Markov Model and its variants are widely used to provide accurate and efficient map matching service. However, HMM-based methods fail to utilize the knowledge (e.g., the mobility pattern) of enormous trajectory big data, which are useful for intelligent map matching. Furthermore, with many following-up works, they are still easily influenced by the common noisy and sparse records in the reality. In this paper, we revisit the map matching task from the data perspective and propose to utilize the great power of massive data and deep learning to solve these problems. Based on the seq2seq learning framework, we build a trajectory2road model with attention mechanism to map the sparse and noisy trajectory into the accurate road network. Different from previous algorithms, our deep learning based model complete the map matching in the latent space, which provides the high tolerance to the noisy trajectory and also enhances the matching with the knowledge of mobility pattern. Extensive experiments demonstrate that the proposed model outperforms the widely used HMM-based methods by more than 10 percent (absolute accuracy) in various situations especially the noisy and sparse settings.
Keyword:
Trajectory
Hidden Markov models
Roads
Noise measurement
Deep learning
Data models
Pattern matching
Map matching
deep learning
geographic information system
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Online Map-Matching of Noisy and Sparse Location Data With Hidden Markov and Route Choice Models
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