In order to explore the main progress and current status of domestic research on smart learning environment, this paper takes 260 core and CSSCI journal papers included in the CNKI database as the research objects, an...In order to explore the main progress and current status of domestic research on smart learning environment, this paper takes 260 core and CSSCI journal papers included in the CNKI database as the research objects, and uses CiteSpace visual analysis software and uses bibliometrics and knowledge graph analysis as the main research methods, summarizes and analyzes the time distribution of the literature, the distribution of institutions and authors, co-occurrence and clustering of keywords, and research hotspots,etc.展开更多
Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transpor...Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.展开更多
文摘In order to explore the main progress and current status of domestic research on smart learning environment, this paper takes 260 core and CSSCI journal papers included in the CNKI database as the research objects, and uses CiteSpace visual analysis software and uses bibliometrics and knowledge graph analysis as the main research methods, summarizes and analyzes the time distribution of the literature, the distribution of institutions and authors, co-occurrence and clustering of keywords, and research hotspots,etc.
文摘Raw Global Positioning System (GPS) data can provide rich context information for behaviour understanding and transport planning. However, they are not yet fully understood, and fine-grained identification of transportation mode is required. In this paper, we present a robust framework without geographic information, which can effectively and automatically identify transportation modes including car, bus, bike and walk. Firstly, a trajectory segmentation algorithm is designed to divide raw GPS trajectory into single mode segments. Secondly, several modern features are proposed which are more discriminating than traditional features. At last, an additional postprocessing procedure is adopted with considering the wholeness of trajectory. Based on Random Forest classifier, our framework can achieve a promising accuracy by distance of 82.85% for identifying transportation modes and especially 91.44% for car mode.
