چكيده به لاتين
Classification of different forms of human transportation, such as stationary, walking, running, car, train, etc., is one of the current topics in order to increase the comfort of human life. Nowadays, the possibility of using the information of various sensors in smartphones, along with the high efficiency of machine learning algorithms and especially deep neural networks, to perform this type of classification has been welcomed. The main purpose of this dissertation is to find an algorithm for classifying different transportation modes using GPS data, which considers the use of deep neural networks in this direction. The RT-TMD model is designed for this purpose, which has the ability to detect transportation mode from 5 different classes. Also, to increase the accuracy of detection, TPD auxiliary network is designed, which has increased the accuracy by 2%. In total, the proposed system is capable of detecting transportation mode from 5 classes with an accuracy of 82.6% for every 10 GPS points. A comparison of the proposed system with recent papers shows that this system is superior to many activities due to its 10-point online predictability and accuracy, and in one case has the advantage of faster detection.
