Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany)

This dataset is a subset of a much larger data collection and used for the analysis of speed- and time-profiles of trajectories crossing different selected intersections. Resulting findings can be used for the intersection categorization according to traffic regulation types.

The six selected intersections (A - F) and the crossing trajectory samples (green) can be seen in the subsequent figure: Intersections

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: GPS Trajectory Dataset of the Region of Hannover, Germany. https://doi.org/10.25835/9bidqxvl

  • 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

Data and Resources

Cite this as

S. Zourlidou, , J. Golze, M. Sester, (2020). Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

DOI retrieved: September 14, 2020

Additional Info

Field Value
Imported on October 14, 2021
Last update February 16, 2024
Source https://data.uni-hannover.de/dataset/trajectory-analysis-at-intersections-for-traffic-rule-identification
Author S. Zourlidou
More Authors
J. Golze
M. Sester
Author Email S. Zourlidou
Maintainer Jens Golze
Maintainer Email Jens Golze
Source Creation 10 September, 2020, 06:44 AM (UTC+0000)
Source Modified 02 August, 2022, 09:39 AM (UTC+0000)