Traffic Regulator Ground-truth Information of the City of Hannover, Germany

This dataset contains the ground-truth intersection regulators for a majority of intersections of the city of Hannover, Germany. The ground-truth information is used in order to apply machine learning techniques on (car) GPS trajectory data in order to automatically detect the intersection regulation.


The GPS trajectories related to specifically this dataset are (also) available under: https://doi.org/10.25835/9bidqxvl

## Data Acquisition
The ground-truth information are acquired by visiting them on-site and apply manual labeling of each intersection arm individually. Furthermore, satellite images and street-level images were considered but only on a minor degree as on-site labeling is found to be more precise and up-to-date.

## Related Publications:
* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection Traffic Regulations From Crowdsourced Data. Preprints 2022, 2022070012. DOI: https://doi.org/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. (2020).__ Dataset: Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany). https://doi.org/10.25835/0043786

本数据集包含德国汉诺威市绝大多数交叉口的交通管制规则真值（ground truth）标注。该真值标注信息可用于在（车载）GPS轨迹数据上应用机器学习技术，以自动识别交叉口交通管制规则。


本数据集对应的GPS轨迹数据亦可通过以下链接获取：https://doi.org/10.25835/9bidqxvl

## 数据采集
该真值标注信息通过实地踏勘采集，并对每条交叉口支路逐一开展人工标注。此外，虽也参考了卫星影像与街景影像，但仅作为辅助手段，因实地标注被证明精度更高且时效性更强。

## 相关研究成果
* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ 基于众源数据的交叉口交通管制规则识别. 预印本2022, 2022070012. DOI: https://doi.org/10.20944/preprints202207.0012.v1
* __Zourlidou, S., Golze, J. and Sester, M. (2022):__ 结合众采GPS与地图数据的交通管制识别：一种混合方法, AGILE地理信息科学系列(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):__ 基于速度剖面与卫星影像的注意力机制交通管控识别. 国际摄影测量、遥感与空间信息科学档案XLIII-B4-2022, 第287–29页. https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022
* __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 交叉口轨迹分析用于交通规则识别. 地理空间信息科学(Geo-spatial Information Science), 11(4):1-10. https://doi.org/10.1080/10095020.2020.1843374
* __Cheng, H., Zourlidou, S. and Sester, M. (2020):__ 基于速度剖面的交通管控识别：一种深度学习方法. ISPRS国际地理信息期刊(ISPRS Int. J. Geo-Inf.) 2020, 9, 652. https://doi.org/10.3390/ijgi9110652
* __Golze, J., Zourlidou, S. and Sester, M. (2020):__ 基于GPS轨迹的交通管制器检测. 韩国制图与地理信息学报(KN J. Cartogr. Geogr. Inf.) https://doi.org/10.1007/s42489-020-00048-x
* __Zourlidou, S., Fischer, C. and Sester, M. (2019):__ 基于交通管制设施的街道交叉口分类. 载于：_Kyriakidis, P., Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 第22届AGILE地理信息科学会议录用短文与海报集. 塞浦路斯理工大学，2019年6月17–20日，利马索尔，塞浦路斯.

## 相关数据集
* __Zourlidou, S., Golze, J. and Sester, M. (2022).__ 数据集：德国汉诺威地区GPS轨迹数据集. https://doi.org/10.25835/9bidqxvl
* __Zourlidou, S., Golze, J. and Sester, M. (2022).__ 数据集：芝加哥轨迹数据集的交通管制真值信息. https://doi.org/10.25835/0vifyzqi
* __Zourlidou, S., Golze, J. and Sester, M. (2022).__ 数据集：希腊埃泽萨地区GPS轨迹数据集与交通管制信息. https://doi.org/10.25835/v0mzwob3
* __Zourlidou, S., Golze, J. and Sester, M. (2020).__ 数据集：用于交通规则识别的速度剖面与GPS轨迹数据集（德国汉诺威，6个交叉口）. https://doi.org/10.25835/0043786