Publications
2021 |
Stähli, Lisa; Giannopoulos, Ioannis; Raubal, Martin Evaluation of pedestrian navigation in Smart Cities (Journal Article) In: Environment and Planning B: Urban Analytics and City Science, vol. 48, no. 6, pp. 1728–1745, 2021. (Abstract | Links | BibTeX | Tags: augmented reality, navigation, virtual environment) @article{stahli2021evaluation, This work addresses recent research in the area of pedestrian navigation aids that aims at finding alternatives to the widely used map-based turn-by-turn navigation systems in the context of Smart City environments. Four different approaches of pedestrian navigation systems were compared to each other in a user experiment that was conducted in a virtual environment: (1) map-based, (2) landmark-based, (3) augmented reality, and (4) public display navigation. The results of the experiment with 45 participants conducted in a virtual environment suggest that the augmented reality navigation performs best concerning efficiency and effectiveness and the landmark-based navigation performs worst in the context of Smart Cities. |
2019 |
Gokl, Lukas; McCutchan, Marvin; Mazurkiewicz, Bartosz; Fogliaroni, Paolo; Giannopoulos, Ioannis Towards Urban Environment Familiarity Prediction (Inproceedings) In: Gartner, Georg; Huang, Haosheng (Ed.): Advances in Cartography and GIScience of the ICA (ICA-Adv), pp. 5-1–5-8, International Cartographic Association (ICA), 2, 2019, (Vortrag: 15th International Conference on Location Based Services (LBS 2019), Wien; 2019-11-11 -- 2019-11-13). (Abstract | Links | BibTeX | Tags: environment familiarity, Machine Learning, virtual environment) @inproceedings{gokl19:5-1[TUW-282604], 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. |