Peter Widhalm
Cited by
Cited by
Discovering urban activity patterns in cell phone data
P Widhalm, Y Yang, M Ulm, S Athavale, MC González
Transportation 42, 597-623, 2015
Supporting large-scale travel surveys with smartphones–A practical approach
P Nitsche, P Widhalm, S Breuss, N Brändle, P Maurer
Transportation Research Part C: Emerging Technologies 43, 212-221, 2014
Transport mode detection with realistic smartphone sensor data
P Widhalm, P Nitsche, N Brändie
Proceedings of the 21st international conference on pattern recognition …, 2012
A strategy on how to utilize smartphones for automatically reconstructing trips in travel surveys
P Nitsche, P Widhalm, S Breuss, P Maurer
Procedia-Social and Behavioral Sciences 48, 1033-1046, 2012
Estimating population density distribution from network-based mobile phone data
F Ricciato, P Widhalm, M Craglia, F Pantisano
Publications Office of the European Union, 2015
Beyond the “single-operator, CDR-only” paradigm: An interoperable framework for mobile phone network data analyses and population density estimation
F Ricciato, P Widhalm, F Pantisano, M Craglia
Pervasive and Mobile Computing 35, 65-82, 2017
Fast hidden Markov model map-matching for sparse and noisy trajectories
H Koller, P Widhalm, M Dragaschnig, A Graser
2015 IEEE 18th International Conference on Intelligent Transportation …, 2015
Characterization of mobile phone localization errors with OpenCellID data
M Ulm, P Widhalm, N Brändle
2015 4th International Conference on Advanced Logistics and Transport (ICALT …, 2015
Top in the lab, flop in the field? Evaluation of a sensor-based travel activity classifier with the SHL dataset
P Widhalm, M Leodolter, N Brändle
Proceedings of the 2018 ACM international joint conference and 2018 …, 2018
Development of a real-time model of the occupancy of short-term parking zones
R Hössinger, P Widhalm, M Ulm, K Heimbuchner, E Wolf, R Apel, ...
International Journal of Intelligent Transportation Systems Research 12, 37-47, 2014
The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration
A Graser, P Widhalm, M Dragaschnig
International Journal of Geographical Information Science 34 (12), 2517-2540, 2020
Potential of low-frequency automated vehicle location data for monitoring and control of bus performance
Y Yang, D Gerstle, P Widhalm, D Bauer, M Gonzalez
Transportation Research Record: Journal of the Transportation Research Board …, 2013
Automatic assessment of the knee alignment angle on full-limb radiographs
N Fakhrai, P Widhalm, C Chiari, M Weber, G Langs, R Donner, H Ringl, ...
European journal of radiology 74 (1), 236-240, 2010
Computational radiology in skeletal radiography
P Peloschek, S Nemec, P Widhalm, R Donner, E Birngruber, ...
European journal of radiology 72 (2), 252-257, 2009
Next-generation 3D visualization for visual surveillance
PM Roth, V Settgast, P Widhalm, M Lancelle, J Birchbauer, N Brändle, ...
2011 8th IEEE International Conference on Advanced Video and Signal Based …, 2011
Learning major pedestrian flows in crowded scenes
P Widhalm, N Brändle
2010 20th International Conference on Pattern Recognition, 4064-4067, 2010
Robust road link speed estimates for sparse or missing probe vehicle data
P Widhalm, M Piff, N Brändle, H Koller, M Reinthaler
2012 15th International IEEE Conference on Intelligent Transportation …, 2012
Identifying faulty traffic detectors with Floating Car Data
P Widhalm, H Koller, W Ponweiser
2011 IEEE Forum on Integrated and Sustainable Transportation Systems, 103-108, 2011
Data-driven trajectory prediction and spatial variability of prediction performance in maritime location based services
A Graser, J Schmidt, M Dragaschnig, P Widhalm
LBS 2019; Adjunct Proceedings of the 15th International Conference on …, 2019
Mobility sequence extraction and labeling using sparse cell phone data
Y Yang, P Widhalm, S Athavale, M Gonzalez
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
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