Jonas Hamann M.Sc.
Wissenschaftlicher Mitarbeiter im ReLUT
Hamann, J. & Hagen, T. (2024). Revealing Trip Purposes in Raw GPS Data by Applying a Multi-Phase Clustering Approach to Semantic Trajectories. IEEE Transactions on Intelligent Transportation Systems.https://doi.org/10.1109/TITS.2024.3516141.
Hamann, J. Hagen, T. & Saki, S. (2025). Understanding Traffic Patterns using Clustered Semantic Trajectories and Local Geographic Units. Transportation Research Procedia, 82,2911-2930. https://doi.org/10.1016/j.trpro.2024.12.227.
Hamann, J. und T. Hagen (2024). Automatisiertes Erkennen von Fahrtzwecken in Floating Car Data. In: Proff, H. (eds) Next Chapter in Mobility. Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-42647-7_29
Saki, S., Hamann, J. & Hagen, T. (2022). TessPy: a python package for geographical tessellation. Journal of Open Source Software, 7(76), 4620, https://doi.org/10.21105/joss.04620
Hamann J. u. S. Saki (2022): TessPy – A Python Package for Spatial Tessellations. https://github.com/siavash-saki/tesspy
Hagen T., J. Hamann, S.Saki (2022): Discretization of Urban Areas using POI-based Tesselation. Working Papers Fachbereich Wirtschaft und Recht Nr. 23, Frankfurt University of Applied Sciences. https://doi.org/10.48718/7jjr-1c66
08.01.2025: 104th TRB Annual Meeting, Transportation Research Board (TRB), 05.01.-09.01.2025: Comparing the Comparable: A Method for Finding Best Practise Cases for Park and Ride Facilities. (Washington, DC)
6.9.-9.9.2022 European Transport Conference (ETC) in Mailand mit dem Thema: Discretization of urban areas using POI-based tessellations.