Changes

On January 12, 2023 at 1:14:29 PM UTC, admin:

Changed title to Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany) (previously Trajectory analysis at intersections for traffic rule identification)

Set author of Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany) to Zourlidou, S., Golze, J. and Sester, M. (previously Stefania Zourlidou, Jens Golze)

Updated description of Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany) from
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](https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png) ## 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. ## Related Publications: * __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
to
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](https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png) ## 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

Changed value of field source_metadata_modified to 2022-08-02T09:39:50.274969 in Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany)

Added resource ReadMe.pdf to Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany)

Deleted resource All resource data from Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany)

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Removed field datastore_active from resource JunctionF.csv in Speed profiles and GPS Trajectories for Traffic Rule Recognition (6 Junctions, Hannover, Germany)

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            n 2   "author": "Stefania Zourlidou, Jens Golze", n 2   "author": "Zourlidou, S., Golze, J. and Sester, M.",
            3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de", 3   "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
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            5   "doi": "10.25835/0043786", 5   "doi": "10.25835/0043786",
            6   "doi_date_published": "2020-09-14", 6   "doi_date_published": "2020-09-14",
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            11   "have_copyright": "Yes", 11   "have_copyright": "Yes",
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            15   "license_title": "CC-BY-NC-3.0", 15   "license_title": "CC-BY-NC-3.0",
            16   "maintainer": "Jens Golze", 16   "maintainer": "Jens Golze",
            17   "maintainer_email": "jens.golze@ikg.uni-hannover.de", 17   "maintainer_email": "jens.golze@ikg.uni-hannover.de",
            18   "metadata_created": "2021-10-14T10:16:09.218830", 18   "metadata_created": "2021-10-14T10:16:09.218830",
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            20   "name":  20   "name": 
            21 trajectory-analysis-at-intersections-for-traffic-rule-identification", 21 trajectory-analysis-at-intersections-for-traffic-rule-identification",
            22   "notes": "This dataset is a subset of a much larger data collection  22   "notes": "This dataset is a subset of a much larger data collection 
            23 and used for the analysis of speed- and time-profiles of trajectories  23 and used for the analysis of speed- and time-profiles of trajectories 
            24 crossing different selected intersections. Resulting findings can be  24 crossing different selected intersections. Resulting findings can be 
            25 used for the intersection categorization according to traffic  25 used for the intersection categorization according to traffic 
            26 regulation types.\r\n\r\nThe six selected intersections (A - F) and  26 regulation types.\r\n\r\nThe six selected intersections (A - F) and 
            27 the crossing trajectory samples (green) can be seen in the subsequent  27 the crossing trajectory samples (green) can be seen in the subsequent 
            28 71e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png)\r\n\r\n##  28 71e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png)\r\n\r\n## 
            29 Data Acquisition\r\nThe trajectory samples were recorded using an  29 Data Acquisition\r\nThe trajectory samples were recorded using an 
            30 android smartphone while driving a car in and around the city of  30 android smartphone while driving a car in and around the city of 
            31 Hannover, Germany. The acquisition period was from December 2017 to  31 Hannover, Germany. The acquisition period was from December 2017 to 
            32 March 2019 by only a single person. The recording of the trajectories  32 March 2019 by only a single person. The recording of the trajectories 
            33 has taken place without restrictions in order to reflect a normal  33 has taken place without restrictions in order to reflect a normal 
            n 34 behavior of everyday car journeys.\r\n\r\n## Related  n 34 behavior of everyday car journeys. The sampling rate is approximately 
            35 1 sample per second.\r\n\r\n## Related Publications:\r\n* __Zourlidou, 
            36 S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection 
            37 Traffic Regulations From Crowdsourced Data. Preprints 2022, 
            38 2022070012. DOI: 10.20944/preprints202207.0012.v1\r\n\r\n* 
            39 __Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation 
            40 Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, 
            41 AGILE GIScience Ser., 3, 22, 2022. 
            42 https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
            43 Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
            44 Recognition with an Attention Mechanism Using Speed-Profile and 
            45 Satellite Imagery data. The International Archives of the 
            46 Photogrammetry, Remote Sensing and Spatial Information Sciences 
            47 XLIII-B4-2022, S. 287\u201329. 
            48 https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
            35 Publications:\r\n* __Wang, C., Zourlidou, S., Golze, J. and Sester,  49 __Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
            36 M., 2020:__ Trajectory analysis at intersections for traffic rule  50 Trajectory analysis at intersections for traffic rule identification. 
            37 identification. Geo-spatial Information Science, 11(4):1-10.  51 Geo-spatial Information Science, 11(4):1-10. 
            38 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H.,  52 https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
            n 39 Zourlidou, S. and Sester, M., 2020:__ Traffic Control Recognition with  n 53 Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
            40 Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 2020,  54 with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
            41 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, J.,  55 2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
            42 Zourlidou, S. and Sester, M., 2020:__ Traffic Regulator Detection  56 J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
            43 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf.  57 Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
            44 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S.,  58 https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
            n 45 Fischer, C. and Sester, M., 2019:__  Classification of street  n 59 Fischer, C. and Sester, M. (2019):__  Classification of street 
            46 junctions according to traffic regulators. In: _Kyriakidis, P.,  60 junctions according to traffic regulators. In: _Kyriakidis, P., 
            47 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_.  61 Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
            48 Accepted short papers and posters from the 22nd AGILE conference on  62 Accepted short papers and posters from the 22nd AGILE conference on 
            49 geo-information science. Cyprus University of Technology 17\u201320  63 geo-information science. Cyprus University of Technology 17\u201320 
            n 50 June 2019, Limassol, Cyprus\r\n\r\n", n 64 June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
            65 __Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
            66 Trajectory Dataset of the Region of Hannover, Germany. 
            67 https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
            68 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            69 Information for the Chicago Trajectory Dataset. 
            70 https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
            71 and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic 
            72 Regulation Information of the Region of Edessa, Greece. 
            73 https://doi.org/10.25835/v0mzwob3\r\n\r\n* __Zourlidou, S., Golze, J. 
            74 and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            75 Information of the City of Hannover, Germany. 
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            185       "last_modified": "2020-09-14T09:15:08.175847", 177       "last_modified": "2020-09-14T09:15:08.175847",
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            198       "url_type": "" 189       "url_type": ""
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            205       "description": "Intersection E with overlaid trajectory samples  195       "description": "Intersection E with overlaid trajectory samples 
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            210       "last_modified": "2020-09-14T09:15:23.038127", 200       "last_modified": "2020-09-14T09:15:23.038127",
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            212       "mimetype": "image/png", 202       "mimetype": "image/png",
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            230       "description": "Intersection F with overlaid trajectory samples  218       "description": "Intersection F with overlaid trajectory samples 
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            247 resource/d02b7f48-3669-4497-9fe0-5015fb74ead4/download/junctionf.png", 234 resource/d02b7f48-3669-4497-9fe0-5015fb74ead4/download/junctionf.png",
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            274 d31f5cd/resource/4be6c2d0-3f28-4945-9639-a53e8a5513a7/download/a.csv", 259 d31f5cd/resource/4be6c2d0-3f28-4945-9639-a53e8a5513a7/download/a.csv",
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            301 d31f5cd/resource/7d20ecb4-4e5d-4e00-a052-279a82d6098c/download/b.csv", 284 d31f5cd/resource/7d20ecb4-4e5d-4e00-a052-279a82d6098c/download/b.csv",
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            309       "description": "Contains all crossing trajectories of  291       "description": "Contains all crossing trajectories of 
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            365 track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n*  343 track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
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            382 d31f5cd/resource/2b5e9501-de55-4674-9c8b-ca5a935caac7/download/e.csv", 359 d31f5cd/resource/2b5e9501-de55-4674-9c8b-ca5a935caac7/download/e.csv",
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            390       "description": "Contains all crossing trajectories of  366       "description": "Contains all crossing trajectories of 
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            392 track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n*  368 track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
            393 northutm\r\n* eastutm\r\n* speed", 369 northutm\r\n* eastutm\r\n* speed",
            394       "format": "CSV", 370       "format": "CSV",
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            409 d31f5cd/resource/b7455de0-4497-443f-a74b-fe2993c9154a/download/f.csv", 384 d31f5cd/resource/b7455de0-4497-443f-a74b-fe2993c9154a/download/f.csv",
            410       "url_type": "" 385       "url_type": ""
            411     }, 386     },
            412     { 387     {
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            417       "description": "Contains the __ground-truth__ information for  391       "description": "Contains the __ground-truth__ information for 
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            419 intersection\r\n* 3 / 4 (3-way / 4-way)\r\n* trajectory samples\r\n*  393 intersection\r\n* 3 / 4 (3-way / 4-way)\r\n* trajectory samples\r\n* 
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            424       "last_modified": "2020-09-14T10:07:00.995272", 398       "last_modified": "2020-09-14T10:07:00.995272",
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            434       "state": "active", 407       "state": "active",
            435       "url":  408       "url": 
            436 urce/27a9afa2-eedb-441e-b205-74255ac42bc0/download/junction_info.csv", 409 urce/27a9afa2-eedb-441e-b205-74255ac42bc0/download/junction_info.csv",
            437       "url_type": "" 410       "url_type": ""
            438     }, 411     },
            439     { 412     {
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            448       "last_modified": "2020-09-14T12:09:36.774947", 420       "last_modified": "2020-09-14T12:09:36.774947",
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            463   ], 456   ],
            464   "source_metadata_created": "2020-09-10T06:44:32.618481", 457   "source_metadata_created": "2020-09-10T06:44:32.618481",
            n 465   "source_metadata_modified": "2020-12-02T11:01:47.641531", n 458   "source_metadata_modified": "2022-08-02T09:39:50.274969",
            466   "state": "active", 459   "state": "active",
            467   "tags": [ 460   "tags": [
            468     { 461     {
            469       "display_name": "GPS trajectories", 462       "display_name": "GPS trajectories",
            470       "id": "ee87ca28-3380-472c-ac9c-97971e1082d9", 463       "id": "ee87ca28-3380-472c-ac9c-97971e1082d9",
            471       "name": "GPS trajectories", 464       "name": "GPS trajectories",
            472       "state": "active", 465       "state": "active",
            473       "vocabulary_id": null 466       "vocabulary_id": null
            474     }, 467     },
            475     { 468     {
            476       "display_name": "clustering", 469       "display_name": "clustering",
            477       "id": "104521c5-968e-4e65-ab6d-3ffbe437ce58", 470       "id": "104521c5-968e-4e65-ab6d-3ffbe437ce58",
            478       "name": "clustering", 471       "name": "clustering",
            479       "state": "active", 472       "state": "active",
            480       "vocabulary_id": null 473       "vocabulary_id": null
            481     }, 474     },
            482     { 475     {
            483       "display_name": "intersection classification", 476       "display_name": "intersection classification",
            484       "id": "7151c183-aaa0-4466-a647-c8761d279f69", 477       "id": "7151c183-aaa0-4466-a647-c8761d279f69",
            485       "name": "intersection classification", 478       "name": "intersection classification",
            486       "state": "active", 479       "state": "active",
            487       "vocabulary_id": null 480       "vocabulary_id": null
            488     }, 481     },
            489     { 482     {
            490       "display_name": "speed-profiles", 483       "display_name": "speed-profiles",
            491       "id": "8cf2e344-1a1c-432c-abad-b9e0a2c377e9", 484       "id": "8cf2e344-1a1c-432c-abad-b9e0a2c377e9",
            492       "name": "speed-profiles", 485       "name": "speed-profiles",
            493       "state": "active", 486       "state": "active",
            494       "vocabulary_id": null 487       "vocabulary_id": null
            495     }, 488     },
            496     { 489     {
            497       "display_name": "traffic rules", 490       "display_name": "traffic rules",
            498       "id": "7c0e8fe9-5cc4-401a-9b35-b696c760280a", 491       "id": "7c0e8fe9-5cc4-401a-9b35-b696c760280a",
            499       "name": "traffic rules", 492       "name": "traffic rules",
            500       "state": "active", 493       "state": "active",
            501       "vocabulary_id": null 494       "vocabulary_id": null
            502     } 495     }
            503   ], 496   ],
            504   "terms_of_usage": "Yes", 497   "terms_of_usage": "Yes",
            t 505   "title": "Trajectory analysis at intersections for traffic rule  t 498   "title": "Speed profiles and GPS Trajectories for Traffic Rule 
            506 identification", 499 Recognition (6 Junctions, Hannover, Germany)",
            507   "type": "vdataset", 500   "type": "vdataset",
            508   "url":  501   "url": 
            509 trajectory-analysis-at-intersections-for-traffic-rule-identification", 502 trajectory-analysis-at-intersections-for-traffic-rule-identification",
            510   "version": "" 503   "version": ""
            511 } 504 }