PUMA publications for /tag/everyaware%20toread%20gishttps://puma.uni-kassel.de/tag/everyaware%20toread%20gisPUMA RSS feed for /tag/everyaware%20toread%20gis2024-03-29T05:49:47+01:00Transportation Mode Detection Using Mobile Phones and GIS Informationhttps://puma.uni-kassel.de/bibtex/26eff579bee29983fbf72403faa9b04ae/hothohotho2013-12-06T08:34:33+01:00bewegung bewegungsart everyaware gis gps mode toread transportation <span class="authorEditorList"><span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Leon Stenneth" itemprop="url" href="/author/Leon%20Stenneth"><span itemprop="name">L. Stenneth</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Ouri Wolfson" itemprop="url" href="/author/Ouri%20Wolfson"><span itemprop="name">O. Wolfson</span></a></span>, <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Philip S. Yu" itemprop="url" href="/author/Philip%20S.%20Yu"><span itemprop="name">P. Yu</span></a></span>, und <span itemtype="http://schema.org/Person" itemscope="itemscope" itemprop="author"><a title="Bo Xu" itemprop="url" href="/author/Bo%20Xu"><span itemprop="name">B. Xu</span></a></span>. </span><span itemtype="http://schema.org/Book" itemscope="itemscope" itemprop="isPartOf"><em><span itemprop="name">Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems</span>, </em></span><em>Seite <span itemprop="pagination">54--63</span>. </em><em>New York, NY, USA, </em><em><span itemprop="publisher">ACM</span>, </em>(<em><span>2011<meta content="2011" itemprop="datePublished"/></span></em>)Fri Dec 06 08:34:33 CET 2013New York, NY, USAProceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems54--63GIS '11Transportation Mode Detection Using Mobile Phones and GIS Information2011bewegung bewegungsart everyaware gis gps mode toread transportation The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naïve Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.Transportation mode detection using mobile phones and GIS information