期刊
IET INTELLIGENT TRANSPORT SYSTEMS
卷 14, 期 6, 页码 570-577出版社
WILEY
DOI: 10.1049/iet-its.2019.0017
关键词
learning (artificial intelligence); data mining; Global Positioning System; convolutional neural nets; recurrent neural nets; traffic information systems; high-level features; weather features; Microsoft Geolife data; GPS features; convolutional LSTM-based transportation mode; raw GPS trajectories; location acquisition technologies; raw global positioning system trajectory data; moving devices; GPS trajectory data; trajectory data mining; data preprocessing; feature engineering; domain expertise; deep learning-based convolutional long short term memory model; transportation mode learning; convolution neural network; weather data set
With the advancement of location acquisition technologies, a large amount of raw global positioning system (GPS) trajectory data is produced by many moving devices. Learning transportation modes from the GPS trajectory data is an important problem in the domain of trajectory data mining. Traditional supervised learning-based approaches rely heavily on data preprocessing and feature engineering, which require domain expertise and are time consuming. The authors propose a deep learning-based convolutional long short term memory (LSTM) model for transportation mode learning, in which the convolution neural network is first used to extract deep high-level features and then LSTM is used to learn the sequential patterns in the data that uses both GPS and weather features, thus making the full use of spatiotemporal operations. The authors have also analysed the impact of the geospatial region on human mobility. Experiments conducted on the Microsoft Geolife data set fused with the weather data set show that their model achieves the state-of-the-art results. The authors compare the performance of their model with the benchmark models, which shows the superiority of their model having 3% improvement in accuracy using only GPS features, and the accuracy is further improved by 4 and 7% on including the impact of geospatial region and weather attributes, respectively.
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