access icon free Convolutional LSTM based transportation mode learning from raw GPS trajectories

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.

Inspec keywords: recurrent neural nets; data mining; learning (artificial intelligence); convolutional neural nets; Global Positioning System; traffic information systems

Other keywords: raw GPS trajectories; high-level features; weather data set; transportation mode learning; trajectory data mining; convolutional LSTM-based transportation mode; feature engineering; convolution neural network; weather features; moving devices; Microsoft Geolife data; location acquisition technologies; raw global positioning system trajectory data; GPS features; domain expertise; data preprocessing; GPS trajectory data; deep learning-based convolutional long short term memory model

Subjects: Knowledge engineering techniques; Traffic engineering computing; Radionavigation and direction finding; Neural computing techniques; Data handling techniques

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