Journal
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Volume E100D, Issue 5, Pages 1132-1135Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
DOI: 10.1587/transinf.2016EDL8252
Keywords
transportation mode detection; deep feature; trajectory mining
Funding
- National Natural Science Foundation of China [61672040]
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Existing studies on transportation mode detection from global positioning system (GPS) trajectories mainly adopt handcrafted features. These features require researchers with a professional background and do not always work well because of the complexity of traffic behavior. To address these issues, we propose a model using a sparse autoencoder to extract point-level deep features from point-level handcrafted features. A convolution neural network then aggregates the point-level deep features and generates a trajectory-level deep feature. A deep neural network incorporates the trajectory-level handcrafted features and the trajectory-level deep feature for detecting the users' transportation modes. Experiments conducted on Microsoft's GeoLife data show that our model can automatically extract the effective features and improve the accuracy of transportation mode detection. Compared with the model using only handcrafted features and shallow classifiers, the proposed model increases the maximum accuracy by 6%.
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