Detecting Transportation Modes Using Deep Neural Network

Hao WANG  GaoJun LIU  Jianyong DUAN  Lei ZHANG  

IEICE TRANSACTIONS on Information and Systems   Vol.E100-D   No.5   pp.1132-1135
Publication Date: 2017/05/01
Publicized: 2017/02/15
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2016EDL8252
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
transportation mode detection,  deep feature,  trajectory mining,  

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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%.