Abstract
Abstract. Location Based Services (LBS) are definitely very helpful for people that interact within an unfamiliar environment, but also for those that already possess a certain level of familiarity with it. In order to avoid overwhelming familiar users with unnecessary information, the level of details offered by the LBS shall be adapted to the level of familiarity with the environment: providing more details to unfamiliar users and a lighter amount of information (that would be superfluous, if not even misleading) to the users that are more familiar with the current environment. Currently, the information exchange between the service and its users is not taking into account familiarity. Within this work, we investigate the potential of machine learning for a binary classification of environment familiarity (i.e., familiar vs unfamiliar) with the surrounding environment. For this purpose, a 3D virtual environment based on a part of Vienna, Austria was designed using datasets from the municipal government. During a navigation experiment with 22 participants we collected ground truth data in order to train four machine learning algorithms. The captured data included motion and orientation of the users as well as visual interaction with the surrounding buildings during navigation. This work demonstrates the potential of machine learning for predicting the state of familiarity as an enabling step for the implementation of LBS better tailored to the user.
Highlights
Location Based Services (LBS) are influencing the way people interact with each other as well as with their surrounding environment and there are still several challenges that have to be overcome (Huang et al, 2018)
All the datasets used were imported into CityEngine and rulebased modeling and Computer Generated Architecture (CGA) rules were applied to calculate the heights of the buildings
The four machine learning algorithms were evaluated by confusion matrices and overall accuracy
Summary
Location Based Services (LBS) are influencing the way people interact with each other as well as with their surrounding environment and there are still several challenges that have to be overcome (Huang et al, 2018). Knowing if a human is familiar with her surrounding environment is a very relevant topic as it enables to improve the quality of the provided service. Since it is practically impossible to ask every user explicitly about her familiarity with her surrounding environment, it is important to be able to predict it based on objectively measurable factors in an implicit manner. Geospatial data, such as user location and orientation, can be captured through mobile devices such as smart phones which are used for LBS. A navigation system could adapt the visualizations of the route or the flow of information according to whether the user is familiar with the surroundings or not
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