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Transportation Mode Recognition With Deep Forest Based on GPS Data
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DOI：10.1109/ACCESS.2020.3015242.png)
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Abstract
En 中文
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.
Keywords:
Feature extraction
Transportation
Trajectory
Global Positioning System
Tunneling magnetoresistance
Forestry
Acceleration
Transportation mode recognition
ensemble learning
deep forest
hybrid transportation mode
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