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.
Convolutional LSTM based transportation mode learning from raw GPS trajectories
IET Intelligent Transport Systems ; 14 , 6 ; 570-577
2020-06-01
8 pages
Article (Journal)
Electronic Resource
English
convolution neural network , data mining , GPS trajectory data , trajectory data mining , recurrent neural nets , raw global positioning system trajectory data , feature engineering , moving devices , high‐level features , domain expertise , Global Positioning System , GPS features , deep learning‐based convolutional long short term memory model , raw GPS trajectories , learning (artificial intelligence) , weather features , data preprocessing , weather data set , transportation mode learning , convolutional neural nets , Microsoft Geolife data , convolutional LSTM‐based transportation mode , traffic information systems , location acquisition technologies