End-to-end trajectory transportation mode classification using Bi-LSTM recurrent neural network
Conference Publication ResearchOnline@JCUAbstract
Transportation mode classification is a key task in trajectory data mining. It adds human behaviour semantics to raw trajectories for trip recommendation, traffic management and transport planning. Previous approaches require heavy pre-processing and feature extraction processes in order to build a classifier, which is complicated and time-consuming. Recurrent neural network has demonstrated its capacity in sequence modelling tasks ranging from machine translation, speech recognition to image captioning. In this paper, we propose a trajectory transportation mode classification framework that is based on an end-to-end bidirectional LSTM classifier. The proposed classification process does not require any feature extraction process, but automatically learns features from trajectories, and use them for classification. We further improve this framework by feeding the time interval as an external feature by embedding. Our experiments on real GPS datasets demonstrate that our approach outperforms existing methods with regard to AUC.
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Publication Name
ISKE 2017: 12th International Conference on Intelligent Systems and Knowledge Engineering
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ISBN/ISSN
978-1-5386-1829-5
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Pages Count
5
Location
Nanjing, China
Publisher
Institute of Electrical and Electronics Engineers
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Publisher Location
Piscataway, NJ, USA
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DOI
10.1109/ISKE.2017.8258799