Identifying Mobility Pattern of Specific User Types Based on Mobility Data

To better understand users and their information demands, it is useful to divide them into user groups. These user groups can be assigned characteristics and mobility preferences. With the help of these parameters, the individual user can be better addressed. In this work a user model was created for the commuter and validated with mobility data. Based on this model, an analysis tool for mobility data was developed. The mobility data analysis tool was designed to identify commuter routes in the dataset. The analysis tool was tested using daily mobility data collected by student in 2018 using the app “MobiDiary”. The results of the analysis show that filtering the trips with the criteria “trip purpose” and “start time” can be a first approach identifying commuter trips. However, a more precise filtering of commuter routes is much more complex. The general findings of this work indicate, that the model trained on the labeled data set, where the participants provided trip purposes, needs to be aware of more parameters for being able to identify commuter trips only based on not labeled trip data.