OmniMOV - Mobility for all

A research project of the XMo Lab, dedicated to model the status quo of the mobility in Vienna and develop a tool that facilitates strategic, targeted improvements.

Funding:

  • Österreichische Forschungsförderungsgesellschaft mbH (FFG)

Project Partners:

  • Technische Universität Wien Institut für Mechanik und Mechatronik (Forschungsbereich Regelungstechnik und Prozessautomatisierung)
  • DI Christian Grubits
  • DI Dr. Robert Kölbl
  • Technische Universität Wien Department für Geodäsie und Geoinformation (Forschungsbereich Geoinformation)

Duration:

  • October 2022 – January 2026

OmniMOV aims to create an integrated, data-driven algorithmic framework that models and predicts mobility behavior across spatial, temporal, and socio-economic dimensions. The project bridges geographic information systems (GIS), existing mobility databases, and socio-economic datasets to better understand and forecast how individuals and households choose and use different mobility modes.
By integrating physical, physiological, and behavioral parameters, OmniMOV contributes to designing more sustainable and user-oriented mobility services. The project supports policymakers, urban planners, and researchers in evaluating and planning new mobility solutions within realistic scenarios.

Publications

FULL PAPERS

Gogousou, I., Canestrini, M., Alinaghi, N., Michail, D. & Giannopoulos, I. (2025). The Mobility Oracle: A Framework for Approximating Human Mobility. (under revision).

Canestrini, M. & Giannopoulos, I. (2025). Beyond Walking and Biking: Expanding the 15-Minute City Area through Public Transport. AGILE: GIScience Series, 6, 2. https://doi.org/10.5194/agile-giss-6-2-2025

Kölbl, R., Cakir, A., Magdalena, M., Gratzer A.L., Schirrer, A., Kozek M., (2025). Long-term Mobility Behavior for Selected Modes in the US, UK, Germany, and Switzerland from 1972 to 2018. Transportation Research Board 2025 Conference.

Kölbl, R., Kozek, M. and Jakubek, S. (2024). A framework for modal split and implications on transport growth and travel time savings. Transport Policy, 158, pp. 196–210. https://doi.org/10.1016/j.tranpol.2024.09.016

Canestrini, M., Gogousou, I., Michail, D., & Giannopoulos, I. (2024). Revealing differences in public transport share through district-wise comparison and relating them to network properties. In 16th International Conference on Spatial Information Theory (COSIT 2024). Schloss-Dagstuhl-Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.COSIT.2024.10

Gogousou, I., Canestrini, M., Alinaghi, N., Michail, D., & Giannopoulos, I. (2024). The Impact of Traffic Lights on Modal Split and Route Choice: A use-case in Vienna. AGILE: GIScience Series, 5, 4. https://doi.org/10.5194/agile-giss-5-4-2024

 

EXTENDED ABSTRACTS

Gogousou, I., Canestrini, M. & Giannopoulos, I. (2025). Seasonal Mobility: Human-Centered and Weather-Aware Routing. AGIT2025 Conference for Geoinformatics. https://doi.org/10.25598/agit/2025-46

Canestrini, M., Gogousou, I. & Giannopoulos, I. (2025). Boosting Public Transport! The Impact of Public Transport Frequency on Modal Split and Trip Duration. AGIT2025 Conference for Geoinformatics. https://doi.org/10.25598/agit/2025-44

Alinaghi N., Canestrini, M., Gogousou, I., & Giannopoulos, I. (2024). Evaluating Mobility-Friendly Regions: An Algorithmic Look at Urban Mobility. 17th International Conference on Travel Behavior Research (IATBR2024).

Canestrini, M., Alinaghi N., Gogousou, I., & Giannopoulos, I. (2024). Public Transport Mobility Index in Simple Terms – An OpenStreetMap Approach. 17th International Conference on Travel Behavior Research (IATBR2024).