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Symbolfoto: Das AIT ist Österreichs größte außeruniversitäre Forschungseinrichtung

Geo-spatial big data analytics

Developing technically sound and efficient solutions for mobility data collection and analysis from machine-generated sensor data poses multiple challenges in terms of storing and retrieving massive spatial-temporal datasets, extraction of useful mobility information from incomplete and possibly inaccurate sensor and transportation network datasets, and intuitive visualization of both raw data and analyzed mobility data. AIT has profound competencies in big data and geo-spatial database management, statistical learning, and visual analytics and contributes to the relevant scientific communitites.


P. Widhalm, M. Leodolter, N. Brändle: "Top In The Lab, Flop In The Field? Evaluation Of A Sensor-based Travel Activity Classifier With The SHL Dataset"; In: Proceedings 6th International Workshop on Human Activity Sensing Corpus and Application (HASCA2018) (in conjuction with UbiComp2018), Singapore; 2018;

A. Graser and P. Widhalm: “Modelling massive AIS streams with quad trees and Gaussian Mixtures”, 21st AGILE Conference on Geographic Information Science June 12-15, 2018 Lund, Sweden

A. Graser, J. Schmidt, F. Roth, N. Brändle:  "Untangling Origin-Destination Flows in Geographic Information Systems";  Information Visualization, SAGE (2017).

R. Fritze, A. Graser, M. Sinnl:  "Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria"; International Journal Of Medical Informatics, 111 (2018), S. 24 - 36.

P. Widhalm, Y. Yang, M. Ulm, M. Gonzalez:  "Discovering Urban Activity Patterns in Cell Phone Data";  Transportation, 42 (2015), 4; 597 - 623.

M. Ulm, P. Widhalm, N. Brändle:  "Characterization of mobile phone localization errors with OpenCellID data"; in:"4th International Conference on Advanced Logistics and Transport (ICALT2015)", IEEE, (2015).

P. Widhalm, P. Nitsche, N. Brändle:  "Transport Mode Detection with Realistic Smartphone Sensor Data";  in: "Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012)", (2012).

M. Leodolter, P. Widhalm, C. Plant, N. Brändle:  "Semi-supervised segmentation of accelerometer time series for transport mode classification"; 
in: "Proceedings 5th IEEE International Conference on Models and Technologies for Intelligent Transportation Systems (MT-ITS)", (2017), 663 - 668.

A. Graser, M. Leodolter, H. Koller:  "Towards Better Urban Travel Time Estimates Using Street Network Centrality";  in: "Proceedings of the 1st ICA European Symposium on Cartography.", Towards Better Urban Travel Time Estimates Using Street Network Centrality, (2015), ISBN: 978-1-907075-03-2.

A. Graser, V. Olaya Ferrero: "Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS";  ISPRS International Journal of Geo-Information, 4 (2015), 4; S. 2219 - 2245.