GPS Trajectory Dataset of the Region of Hannover, Germany

This dataset is used for the classification of traffic intersection regulations using (car) GPS trajectory data. The coverage is mostly a large part of the city of Hannover, Germany. The overview of the GPS trajectory dataset is given in the below figure:  When the trajectory dataset is combined with the related intersection ground-truth information (available at: https://doi.org/10.25835/cqg0x1el):  ## Data Acquisition The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Hannover, Germany. The acquisition period was from December 2017 to March 2019 by only a single person. The recording of the trajectories has taken place without restrictions in order to reflect a normal behavior of everyday car journeys. The sampling rate is approximately 1 sample per second. ## Related Publications: * __Zourlidou, S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: 10.20944/preprints202207.0012.v1 * __Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience Ser., 3, 22, 2022. https://doi.org/10.5194/agile-giss-3-22-2022 * __Cheng, H., Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control Recognition with an Attention Mechanism Using Speed-Profile and Satellite Imagery data. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2022, S. 287–29. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022 * __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ Trajectory analysis at intersections for traffic rule identification. Geo-spatial Information Science, 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374 * __Cheng, H., Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020, 9, 652. https://doi.org/10.3390/ijgi9110652 * __Golze, J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. https://doi.org/10.1007/s42489-020-00048-x * __Zourlidou, S., Fischer, C. and Sester, M. (2019):__ Classification of street junctions according to traffic regulators. In: _Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. Accepted short papers and posters from the 22nd AGILE conference on geo-information science. Cyprus University of Technology 17–20 June 2019, Limassol, Cyprus. ## Related Datasets: * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth Information for the Chicago Trajectory Dataset. https://doi.org/10.25835/0vifyzqi * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece. https://doi.org/10.25835/v0mzwob3 * __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el * __Zourlidou, S., Golze, J. and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786