Abstract :
At present, user context perception, recognition and prediction is a hot research topic in smart community. As an important user context information, more and more researchers have studied the traffic pattern recognition, however, these studies cannot meet the requirement of high recognition accuracy, rich recognition categories and convenient implementation at the same time. Therefore, the purpose of this paper is to design a traffic pattern recognition system which can meet the above requirements. Due to the smart phone has a certain computing capacity, and its built-in sensors are more and more abundant, we develop the traffic pattern recognition software based on smart phone. This paper starts from the introduction of pattern recognition system and methods, and presents the research review of traffic pattern recognition. Then the classification method, Random Forests Model, and positioning technology based on smart phones applied in this paper are proposed. Lastly, the development of traffic pattern recognition software based on iOS framework is displayed in details.
Keywords :
Global Positioning System; iOS (operating system); pattern classification; random processes; smart phones; traffic engineering computing; built-in sensors; classification method; computing capacity; pattern recognition methods; pattern recognition system; positioning technology; random forest model; smart community; smart phone; traffic pattern recognition software; user context information; user context perception; user context prediction; user context recognition; Acceleration; Feature extraction; Global Positioning System; Intelligent sensors; Pattern recognition; Traffic control; Classification Model; Pattern Recognition; Positioning Technology; Random Forests; iOS Framework;
