Changes

On November 28, 2024 at 1:04:24 PM UTC, admin:

Set author of GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece to Zourlidou, Stefania (previously S. Zourlidou)

Updated description of GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece from
This dataset is used for the classification of traffic intersections regulation rules using car GPS trajectories. In order to apply supervised classification methods, the ground-truth information is also contained in this dataset. The regulators are annotated based on the intersection arms at each intersection. An overview of the dataset's trajectories can be seen in the subsequent figure: ![Overview of the dataset containing the trajectories collected in Edessa.](https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png) An overview of the related intersection regulator ground-truth rules can be seen in the following figure: ![](https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c8d75ea3-83ed-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png) ## Data Acquisition The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Edessa, Greece. The acquisition period was from March 2018 to September 2018 (6 month) 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. Additionally to the GPS trajectories, the ground-truth regulator rules of the traffic intersections were annotated via street view images. ## 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: 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
to
This dataset is used for the classification of traffic intersections regulation rules using car GPS trajectories. In order to apply supervised classification methods, the ground-truth information is also contained in this dataset. The regulators are annotated based on the intersection arms at each intersection. An overview of the dataset's trajectories can be seen in the subsequent figure: ![Overview of the dataset containing the trajectories collected in Edessa.](https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png) An overview of the related intersection regulator ground-truth rules can be seen in the following figure: ![](https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c8d75ea3-83ed-445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png) ## Data Acquisition The trajectory samples were recorded using an android smartphone while driving a car in and around the city of Edessa, Greece. The acquisition period was from March 2018 to September 2018 (6 month) 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. Additionally to the GPS trajectories, the ground-truth regulator rules of the traffic intersections were annotated via street view images. ## 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: 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

Changed the license of GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece to CC-BY-NC-3.0 (previously )

Changed value of field source_metadata_modified to 2024-04-05T14:22:00.463520 in GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece

Changed value of field extra_authors to [{'extra_author': 'Golze, Jens', 'familyName': 'Golze', 'givenName': 'Jens'}, {'extra_author': 'Sester, Monika', 'familyName': 'Sester', 'givenName': 'Monika'}] in GPS Trajectory Dataset and Traffic Regulation Information of the Region of Edessa, Greece

              
    
          
          
        
        
            f 1 { f 1 {
            n 2   "author": " S. Zourlidou", n 2   "author": "Zourlidou, Stefania",
            3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de", 3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
            n n 4   "citation": [],
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            5   "doi": "10.25835/v0mzwob3", 6   "doi": "10.25835/v0mzwob3",
            6   "doi_date_published": "2022-08-01", 7   "doi_date_published": "2022-08-01",
            7   "doi_publisher": "LUIS", 8   "doi_publisher": "LUIS",
            8   "doi_status": "true", 9   "doi_status": "true",
            9   "domain": "https://data.uni-hannover.de", 10   "domain": "https://data.uni-hannover.de",
            10   "extra_authors": [ 11   "extra_authors": [
            11     { 12     {
            n 12       "extra_author": "  J. Golze", n 13       "extra_author": "Golze, Jens",
            13       "orcid": "" 14       "familyName": "Golze",
            15       "givenName": "Jens"
            14     }, 16     },
            15     { 17     {
            n 16       "extra_author": " M. Sester", n 18       "extra_author": "Sester, Monika",
            17       "orcid": "" 19       "familyName": "Sester",
            20       "givenName": "Monika"
            18     } 21     }
            19   ], 22   ],
            n n 23   "familyName": "Zourlidou",
            24   "givenName": "Stefania",
            20   "groups": [], 25   "groups": [],
            21   "have_copyright": "Yes", 26   "have_copyright": "Yes",
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            n 24   "license_id": "", n 29   "license_id": "CC-BY-NC-3.0",
            25   "license_title": "", 30   "license_title": "CC-BY-NC-3.0",
            26   "maintainer": "Jens Golze", 31   "maintainer": "Jens Golze",
            27   "maintainer_email": "jens.golze@ikg.uni-hannover.de", 32   "maintainer_email": "jens.golze@ikg.uni-hannover.de",
            28   "metadata_created": "2023-01-12T13:14:29.307047", 33   "metadata_created": "2023-01-12T13:14:29.307047",
            n 29   "metadata_modified": "2024-02-16T14:24:43.739694", n 34   "metadata_modified": "2024-11-28T13:04:23.994095",
            30   "name":  35   "name": 
            31 tory-collection-and-intersection-ground-truth-information-for-edessa", 36 tory-collection-and-intersection-ground-truth-information-for-edessa",
            32   "notes": "This dataset is used for the classification of traffic  37   "notes": "This dataset is used for the classification of traffic 
            33 intersections regulation rules using car GPS trajectories. In order to  38 intersections regulation rules using car GPS trajectories. In order to 
            34 apply supervised classification methods, the ground-truth information  39 apply supervised classification methods, the ground-truth information 
            35 is also contained in this dataset. The regulators are annotated based  40 is also contained in this dataset. The regulators are annotated based 
            36 on the intersection arms at each intersection.\r\n\r\nAn overview of  41 on the intersection arms at each intersection.\r\n\r\nAn overview of 
            37 the dataset's trajectories can be seen in the subsequent  42 the dataset's trajectories can be seen in the subsequent 
            38 figure:\r\n![Overview of the dataset containing the trajectories  43 figure:\r\n![Overview of the dataset containing the trajectories 
            39 collected in  44 collected in 
            40 fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png)\r\n\r\nAn  45 fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png)\r\n\r\nAn 
            41 overview of the related intersection regulator ground-truth rules can  46 overview of the related intersection regulator ground-truth rules can 
            42 be seen in the following  47 be seen in the following 
            43 -445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png)\r\n\r\n##  48 -445a-a1db-38ca092f94a6/download/edessa_traj_junc_rules.png)\r\n\r\n## 
            44 Data Acquisition\r\nThe trajectory samples were recorded using an  49 Data Acquisition\r\nThe trajectory samples were recorded using an 
            45 android smartphone while driving a car in and around the city of  50 android smartphone while driving a car in and around the city of 
            46 Edessa, Greece. The acquisition period was from March 2018 to  51 Edessa, Greece. The acquisition period was from March 2018 to 
            47 September 2018 (6 month) by only a single person. The recording of the  52 September 2018 (6 month) by only a single person. The recording of the 
            48 trajectories has taken place without restrictions in order to reflect  53 trajectories has taken place without restrictions in order to reflect 
            49 a normal behavior of everyday car journeys. The sampling rate is  54 a normal behavior of everyday car journeys. The sampling rate is 
            50 approximately 1 sample per second.\r\nAdditionally to the GPS  55 approximately 1 sample per second.\r\nAdditionally to the GPS 
            51 trajectories, the ground-truth regulator rules of the traffic  56 trajectories, the ground-truth regulator rules of the traffic 
            52 intersections were annotated via street view images.\r\n\r\n## Related  57 intersections were annotated via street view images.\r\n\r\n## Related 
            53 Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__  58 Publications:\r\n* __Zourlidou, S., Sester, M. and Hu, S. (2022):__ 
            54 Recognition of Intersection Traffic Regulations From Crowdsourced  59 Recognition of Intersection Traffic Regulations From Crowdsourced 
            55 Data. Preprints 2022, 2022070012. DOI:  60 Data. Preprints 2022, 2022070012. DOI: 
            n 56 10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, S., Golze, J.  n 61 https://doi.org/10.20944/preprints202207.0012.v1\r\n\r\n* __Zourlidou, 
            57 and Sester, M. (2022):__ Traffic Regulation Recognition using  62 S., Golze, J. and Sester, M. (2022):__ Traffic Regulation Recognition 
            58 Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE GIScience  63 using Crowd-Sensed GPS and Map Data: a Hybrid Approach, AGILE 
            59 Ser., 3, 22, 2022.  64 GIScience Ser., 3, 22, 2022. 
            60 https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H.,  65 https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
            61 Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control  66 Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
            62 Recognition with an Attention Mechanism Using Speed-Profile and  67 Recognition with an Attention Mechanism Using Speed-Profile and 
            63 Satellite Imagery data. The International Archives of the  68 Satellite Imagery data. The International Archives of the 
            64 Photogrammetry, Remote Sensing and Spatial Information Sciences  69 Photogrammetry, Remote Sensing and Spatial Information Sciences 
            65 XLIII-B4-2022, S. 287\u201329.  70 XLIII-B4-2022, S. 287\u201329. 
            66 https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n*  71 https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
            67 __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__  72 __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
            68 Trajectory analysis at intersections for traffic rule identification.  73 Trajectory analysis at intersections for traffic rule identification. 
            69 Geo-spatial Information Science, 11(4):1-10.  74 Geo-spatial Information Science, 11(4):1-10. 
            70 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H.,  75 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
            71 Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition  76 Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
            72 with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf.  77 with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
            73 2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze,  78 2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
            74 J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection  79 J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
            75 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf.  80 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
            76 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S.,  81 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
            77 Fischer, C. and Sester, M. (2019):__  Classification of street  82 Fischer, C. and Sester, M. (2019):__  Classification of street 
            78 junctions according to traffic regulators. In: _Kyriakidis, P.,  83 junctions according to traffic regulators. In: _Kyriakidis, P., 
            79 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_.  84 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
            80 Accepted short papers and posters from the 22nd AGILE conference on  85 Accepted short papers and posters from the 22nd AGILE conference on 
            81 geo-information science. Cyprus University of Technology 17\u201320  86 geo-information science. Cyprus University of Technology 17\u201320 
            82 June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n*  87 June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
            83 __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS  88 __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
            84 Trajectory Dataset of the Region of Hannover, Germany.  89 Trajectory Dataset of the Region of Hannover, Germany. 
            85 https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J.  90 https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
            86 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth  91 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            87 Information for the Chicago Trajectory Dataset.  92 Information for the Chicago Trajectory Dataset. 
            88 https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J.  93 https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
            89 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth  94 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            90 Information of the City of Hannover, Germany.  95 Information of the City of Hannover, Germany. 
            91 https://doi.org/10.25835/cqg0x1el \r\n\r\n* __Zourlidou, S., Golze, J.  96 https://doi.org/10.25835/cqg0x1el \r\n\r\n* __Zourlidou, S., Golze, J. 
            92 and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories  97 and Sester, M. (2020).__ Dataset: Speed profiles and GPS Trajectories 
            93 for Traffic Rule Recognition (6 Junctions, Hannover, Germany).  98 for Traffic Rule Recognition (6 Junctions, Hannover, Germany). 
            94 https://doi.org/10.25835/0043786", 99 https://doi.org/10.25835/0043786",
            95   "num_resources": 14, 100   "num_resources": 14,
            96   "num_tags": 4, 101   "num_tags": 4,
            n 97   "orcid": "", n 
            98   "organization": { 102   "organization": {
            99     "approval_status": "approved", 103     "approval_status": "approved",
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            457       "vocabulary_id": null 458       "vocabulary_id": null
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            465     } 466     }
            466   ], 467   ],
            467   "terms_of_usage": "Yes", 468   "terms_of_usage": "Yes",
            468   "title": "GPS Trajectory Dataset and Traffic Regulation Information  469   "title": "GPS Trajectory Dataset and Traffic Regulation Information 
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            470   "type": "vdataset", 471   "type": "vdataset",
            471   "url":  472   "url": 
            472 tory-collection-and-intersection-ground-truth-information-for-edessa", 473 tory-collection-and-intersection-ground-truth-information-for-edessa",
            473   "version": "" 474   "version": ""
            474 } 475 }