期刊
IEEE ACCESS
卷 8, 期 -, 页码 150891-150901出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.3015242
关键词
Feature extraction; Transportation; Trajectory; Global Positioning System; Tunneling magnetoresistance; Forestry; Acceleration; Transportation mode recognition; ensemble learning; deep forest; hybrid transportation mode
资金
- National Natural Science Foundation of China [61871020, 61305013]
- Science and Technology Program of Beijing Municipal Education Commission (key program) [KZ201810016019]
- Beijing Municipal Universities High-Level Innovation Team Construction Project [IDHT20190506]
Transportation mode recognition (TMR) is a common but critical task in the human behavior research field, which provides decision support for urban traffic planning, public facility arrangement, travel route recommendations, etc. The rapid development of urban information technology, mobile sensors and artificial intelligence has generated solutions for TMR; however, they rely on extra sensors and Geographic Information System (GIS) information, which are not always available. Recognition is usually simplified by disregarding the trajectories among transportation mode change points. In this paper, we proposed an ensemble learning-based approach to automatically recognize transportation modes (including a hybrid mode) using only Global Positioning System (GPS) data. A total of 72 features were extracted to better distinguish different transportation modes. Furthermore, we exploited a deep forest to combine various types of classification models, which facilitates robust learning with different trajectory samples and modes. The experimental results for the Geolife dataset show the efficiency of our approach, and the improved deep forest model achieved the best performance among all experiments that we conducted with 88.6% accuracy.
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