Abstract
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.
Highlights
The motion behaviors of residents has a certain regularity according to a certain time cycle, and the hidden patterns and trends are crucial for urban development and governance
In this ensemble learning framework, the SVM and XGBoost are employed as the component classifiers in addition to the RF and CRF models to enhance the diversity of component classifiers
A total of 7 transportation modes of individuals can be determined by the proposed model, including walking, bicycle, bus, car, train, and subway and hybrid modes
Summary
The motion behaviors of residents has a certain regularity according to a certain time cycle, and the hidden patterns and trends are crucial for urban development and governance. M. Guo et al.: Transportation Mode Recognition With Deep Forest Based on GPS Data applied in the representation of TMR, which can be divided into two categories: point features, including velocity, acceleration, turning angle, and sinuosity; and segment features, such as mean velocity or maximum acceleration. Inspired by its promising capacity in classification tasks, we propose an improved deep forest method to automatically recognize transportation modes, including a hybrid mode, with only GPS data. In this framework, we employ 72 global trajectory features extracted by using statistical methods to distinguish transportation modes.
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