The purpose of this thesis is the development of a mobile application that can distinguish between walking, biking, driving by car, driving by bus and standing still, which enables the user to perform a personal analysis of their travel duration according to different means of transport. The objective was achieved by gathering accelerometer and GPS data from sensors included in modern mobile devices and using it to train classification models with logistic regression. The classification parameters were calculated using Newton's method and gradient descent with various arguments. The models were utilized in an Android application, which distinguishes between transportation modes in real time and saves the routes for later analysis. The application also provides options for subsequent correction of wrongly classified outliers and further distinction between bus and car based on stopovers.