Assessing traffic performance using position density of sparse FCD. - Anita Graser; Wolfgang Ponweiser; Melitta Dragaschnig; Norbert Brändle; Peter Widhalm (ITSC, 2012)

Is OSM Good Enough for Vehicle Routing? A Study Comparing Street Networks in Vienna. - Anita Graser; Markus Straub; Melitta Dragaschnig (LBS, 2014)

Towards an Open Source Analysis Toolbox for Street Network Comparison: Indicators, Tools and Results of a Comparison of OSM and the Official Austrian Reference Graph. - Anita Graser; Markus Straub; Melitta Dragaschnig (Trans. GIS, 2014)

Fast Hidden Markov Model Map-Matching for Sparse and Noisy Trajectories. - Hannes Koller; Peter Widhalm; Melitta Dragaschnig; Anita Graser (ITSC, 2015)

The elevation factor: Digital elevation model quality and sampling impacts on electric vehicle energy estimation errors. - Anita Graser; Johannes Asamer; Wolfgang Ponweiser (MT-ITS, 2015)

Processing: A Python Framework for the Seamless Integration of Geoprocessing Tools in QGIS. - Anita Graser; Victor Olaya (ISPRS Int. J. Geo Inf., 2015)

GIS and Transport Modeling - Strengthening the Spatial Perspective. - Martin Loidl; Gudrun Wallentin; Rita Cyganski; Anita Graser; Johannes Scholz; Eva Haslauer (ISPRS Int. J. Geo Inf., 2016)

Improving vehicle speed prediction transferability with network centrality. - Maximilian Leodolter; Anita Graser (MT-ITS, 2017)

Geometric methods for estimating representative sidewalk widths applied to Vienna's streetscape surfaces database. - Tadej Brezina; Anita Graser; Ulrich Leth (J. Geogr. Syst., 2017)

Combining spatial information and optimization for locating emergency medical service stations: A case study for Lower Austria. - Robert Fritze; Anita Graser; Markus Sinnl (Int. J. Medical Informatics, 2018)

Untangling origin-destination flows in geographic information systems. - Anita Graser; Johanna Schmidt; Florian Roth; Norbert Brändle (Inf. Vis., 2019)

Exploratory Trajectory Analysis for Massive Historical AIS Datasets. - Anita Graser; Melitta Dragaschnig; Peter Widhalm; Hannes Koller; Norbert Brändle (MDM, 2020)

From Simple Features to Moving Features and Beyond? - Anita Graser; Esteban Zimányi; Chaitanya Krishna Bommakanti (CoRR, 2020)

The M³ massive movement model: a distributed incrementally updatable solution for big movement data exploration. - Anita Graser; Peter Widhalm; Melitta Dragaschnig (Int. J. Geogr. Inf. Sci., 2020)

Notebook-based Visual Analysis of Large Tracking Datasets. - Anita Graser (EDBT/ICDT Workshops, 2021)

An exploratory data analysis protocol for identifying problems in continuous movement data. - Anita Graser (J. Locat. Based Serv., 2021)

Mobility Data Science (Dagstuhl Seminar 22021). - Mohamed F. Mokbel; Mahmoud Attia Sakr; Li Xiong 001; Andreas Züfle; Jussara M. Almeida; Taylor Anderson 001; Walid G. Aref; Gennady L. Andrienko; Natalia V. Andrienko; Yang Cao 011; Sanjay Chawla; Reynold Cheng; et al. (Dagstuhl Reports, 2022)

Thinking Geographically about AI Sustainability. - Meilin Shi; Kitty Currier; Zilong Liu; Krzysztof Janowicz; Nina Wiedemann; Judith Verstegen; Grant McKenzie; Anita Graser; Rui Zhu 008; Gengchen Mai (AGILE Conf., 2023)

MobiSpaces: An Architecture for Energy-Efficient Data Spaces for Mobility Data. - Christos Doulkeridis; Georgios M. Santipantakis; Nikolaos Koutroumanis; George Makridis; Vasilis Koukos; George S. Theodoropoulos; Yannis Theodoridis; Dimosthenis Kyriazis; Pavlos Kranas; Diego Burgos; Ricardo Jiménez-Peris; Mariana M. G. Duarte; et al. (IEEE Big Data, 2023)

Deep Learning From Trajectory Data: a Review of Deep Neural Networks and the Trajectory Data Representations to Train Them. - Anita Graser; Anahid N. Jalali; Jasmin Lampert; Axel Weißenfeld; Krzysztof Janowicz (EDBT/ICDT Workshops, 2023)

Federated Learning for Predictive Maintenance and Quality Inspection in Industrial Applications. - Viktorija Pruckovskaja; Axel Weissenfeld; Clemens Heistracher; Anita Graser; Julia Kafka; Peter Leputsch; Daniel Schall 001; Jana Kemnitz (CoRR, 2023)

Towards Mobility Data Science (Vision Paper). - Mohamed F. Mokbel; Mahmoud Attia Sakr; Li Xiong 001; Andreas Züfle; Jussara M. Almeida; Taylor Anderson 001; Walid G. Aref; Gennady L. Andrienko; Natalia V. Andrienko; Yang Cao 011; Sanjay Chawla; Reynold Cheng; et al. (CoRR, 2023)

Towards eXplainable AI for Mobility Data Science. - Anahid N. Jalali; Anita Graser; Clemens Heistracher (CoRR, 2023)

Here Is Not There: Measuring Entailment-Based Trajectory Similarity for Location-Privacy Protection and Beyond. - Zilong Liu; Krzysztof Janowicz; Kitty Currier; Meilin Shi; Jinmeng Rao; Song Gao 001; Ling Cai; Anita Graser (CoRR, 2023)

Green Mobility Data Spaces. - Anita Graser; Christos Doulkeridis; George S. Theodoropoulos (ERCIM News, 2023)

Exploratory Analysis of Massive Movement Data. - Anita Graser (MDM, 2024)

Trajectools Demo: Towards No-Code Solutions for Movement Data Analytics. - Anita Graser; Melitta Dragaschnig (MDM, 2024)

Federated Learning for Anomaly Detection in Maritime Movement Data. - Anita Graser; Axel Weissenfeld; Clemens Heistracher; Melitta Dragaschnig; Peter Widhalm (MDM, 2024)

Mobility Data Science: Perspectives and Challenges. - Mohamed F. Mokbel; Mahmoud Attia Sakr; Li Xiong 001; Andreas Züfle; Jussara M. Almeida; Taylor Anderson 001; Walid G. Aref; Gennady L. Andrienko; Natalia V. Andrienko; Yang Cao 011; Sanjay Chawla; Reynold Cheng; et al. (ACM Trans. Spatial Algorithms Syst., 2024)

Learning From Trajectory Data With MobiML. - Anita Graser; Melitta Dragaschnig (EDBT/ICDT Workshops, 2025)

The QGIS project: Spatial without compromise. - Anita Graser; Tim Sutton; Marco Bernasocchi (Patterns, 2025)

MobilityDL: a review of deep learning from trajectory data. - Anita Graser; Anahid N. Jalali; Jasmin Lampert; Axel Weißenfeld; Krzysztof Janowicz (GeoInformatica, 2025)

Spatial Data Science Languages: commonalities and needs. - Edzer J. Pebesma; Martin Fleischmann; Josiah Parry; Jakub Nowosad; Anita Graser; Dewey Dunnington; Maarten Pronk; Rafael Schouten; Robin Lovelace; Marius Appel; Lorena Abad (CoRR, 2025)

Timeseries Foundation Models for Mobility: A Benchmark Comparison with Traditional and Deep Learning Models. - Anita Graser (CoRR, 2025)