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  "author": "Zourlidou",
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  "author": " S. Zourlidou",
            
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  "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
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  "author_email": "stefania.zourlidou@ikg.uni-hannover.de",
            
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
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  "creator_user_id": "17755db4-395a-4b3b-ac09-e8e3484ca700",
            
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  "doi": "10.25835/0043786",
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  "doi": "10.25835/0043786",
            
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  "doi_date_published": "2020-09-14",
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  "doi_date_published": "2020-09-14",
            
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  "doi_publisher": "LUIS",
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  "doi_publisher": "LUIS",
            
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  "doi_status": "true",
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  "doi_status": "true",
            
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  "domain": "https://data.uni-hannover.de",
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  "domain": "https://data.uni-hannover.de",
            
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  "extra_authors": [
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  "extra_authors": [
            
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    {
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    {
            
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      "extra_author": " S."
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": " Golze"
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      "extra_author": " J. Golze",
            
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": " J. and Sester"
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      "extra_author": "M. Sester",
            
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      "orcid": ""
            
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    },
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    },
            
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    {
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    {
            
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      "extra_author": " M."
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      "extra_author": "",
            
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    }
            
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  ],
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  ],
            
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  "have_copyright": "Yes",
            
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  "license_id": "CC-BY-NC-3.0",
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  "license_id": "",
            
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  "license_title": "CC-BY-NC-3.0",
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  "license_title": "",
            
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  "maintainer": "Jens Golze",
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  "maintainer": "Jens Golze",
            
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  "maintainer_email": "jens.golze@ikg.uni-hannover.de",
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  "maintainer_email": "jens.golze@ikg.uni-hannover.de",
            
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  "metadata_created": "2021-10-14T10:16:09.218830",
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  "metadata_created": "2021-10-14T10:16:09.218830",
            
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  "metadata_modified": "2024-02-16T14:25:38.388252",
            
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  "name": 
38
  "name": 
            
35
trajectory-analysis-at-intersections-for-traffic-rule-identification",
39
trajectory-analysis-at-intersections-for-traffic-rule-identification",
            
36
  "notes": "This dataset is a subset of a much larger data collection 
40
  "notes": "This dataset is a subset of a much larger data collection 
            
37
and used for the analysis of speed- and time-profiles of trajectories 
41
and used for the analysis of speed- and time-profiles of trajectories 
            
38
crossing different selected intersections. Resulting findings can be 
42
crossing different selected intersections. Resulting findings can be 
            
39
used for the intersection categorization according to traffic 
43
used for the intersection categorization according to traffic 
            
40
regulation types.\r\n\r\nThe six selected intersections (A - F) and 
44
regulation types.\r\n\r\nThe six selected intersections (A - F) and 
            
41
the crossing trajectory samples (green) can be seen in the subsequent 
45
the crossing trajectory samples (green) can be seen in the subsequent 
            
42
71e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png)\r\n\r\n## 
46
71e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png)\r\n\r\n## 
            
43
Data Acquisition\r\nThe trajectory samples were recorded using an 
47
Data Acquisition\r\nThe trajectory samples were recorded using an 
            
44
android smartphone while driving a car in and around the city of 
48
android smartphone while driving a car in and around the city of 
            
45
Hannover, Germany. The acquisition period was from December 2017 to 
49
Hannover, Germany. The acquisition period was from December 2017 to 
            
46
March 2019 by only a single person. The recording of the trajectories 
50
March 2019 by only a single person. The recording of the trajectories 
            
47
has taken place without restrictions in order to reflect a normal 
51
has taken place without restrictions in order to reflect a normal 
            
48
behavior of everyday car journeys. The sampling rate is approximately 
52
behavior of everyday car journeys. The sampling rate is approximately 
            
49
1 sample per second.\r\n\r\n## Related Publications:\r\n* __Zourlidou, 
53
1 sample per second.\r\n\r\n## Related Publications:\r\n* __Zourlidou, 
            
50
S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection 
54
S., Sester, M. and Hu, S. (2022):__ Recognition of Intersection 
            
51
Traffic Regulations From Crowdsourced Data. Preprints 2022, 
55
Traffic Regulations From Crowdsourced Data. Preprints 2022, 
            
52
2022070012. DOI: 10.20944/preprints202207.0012.v1\r\n\r\n* 
56
2022070012. DOI: 10.20944/preprints202207.0012.v1\r\n\r\n* 
            
53
__Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation 
57
__Zourlidou, S., Golze, J. and Sester, M. (2022):__ Traffic Regulation 
            
54
Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, 
58
Recognition using Crowd-Sensed GPS and Map Data: a Hybrid Approach, 
            
55
AGILE GIScience Ser., 3, 22, 2022. 
59
AGILE GIScience Ser., 3, 22, 2022. 
            
56
https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
60
https://doi.org/10.5194/agile-giss-3-22-2022\r\n\r\n* __Cheng, H., 
            
57
Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
61
Lei, H., Zourlidou, S., Sester, M. (2022):__ Traffic Control 
            
58
Recognition with an Attention Mechanism Using Speed-Profile and 
62
Recognition with an Attention Mechanism Using Speed-Profile and 
            
59
Satellite Imagery data. The International Archives of the 
63
Satellite Imagery data. The International Archives of the 
            
60
Photogrammetry, Remote Sensing and Spatial Information Sciences 
64
Photogrammetry, Remote Sensing and Spatial Information Sciences 
            
61
XLIII-B4-2022, S. 287\u201329. 
65
XLIII-B4-2022, S. 287\u201329. 
            
62
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
66
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-287-2022\r\n\r\n* 
            
63
__Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
67
__Wang, C., Zourlidou, S., Golze, J. and Sester, M. (2020):__ 
            
64
Trajectory analysis at intersections for traffic rule identification. 
68
Trajectory analysis at intersections for traffic rule identification. 
            
65
Geo-spatial Information Science, 11(4):1-10. 
69
Geo-spatial Information Science, 11(4):1-10. 
            
66
https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
70
https://doi.org/10.1080/10095020.2020.1843374\r\n\r\n* __Cheng, H., 
            
67
Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
71
Zourlidou, S. and Sester, M. (2020):__ Traffic Control Recognition 
            
68
with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
72
with Speed-Profiles: A Deep Learning Approach. ISPRS Int. J. Geo-Inf. 
            
69
2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
73
2020, 9, 652. https://doi.org/10.3390/ijgi9110652\r\n\r\n* __Golze, 
            
70
J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
74
J., Zourlidou, S. and Sester, M. (2020):__ Traffic Regulator Detection 
            
71
Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
75
Using GPS Trajectories. KN J. Cartogr. Geogr. Inf. 
            
72
https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
76
https://doi.org/10.1007/s42489-020-00048-x\r\n\r\n* __Zourlidou, S., 
            
73
Fischer, C. and Sester, M. (2019):__  Classification of street 
77
Fischer, C. and Sester, M. (2019):__  Classification of street 
            
74
junctions according to traffic regulators. In: _Kyriakidis, P., 
78
junctions according to traffic regulators. In: _Kyriakidis, P., 
            
75
Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
79
Hadjimitsis, D., Skarlatos, D. and Mansourian, A., (eds) 2019_. 
            
76
Accepted short papers and posters from the 22nd AGILE conference on 
80
Accepted short papers and posters from the 22nd AGILE conference on 
            
77
geo-information science. Cyprus University of Technology 17\u201320 
81
geo-information science. Cyprus University of Technology 17\u201320 
            
78
June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
82
June 2019, Limassol, Cyprus.\r\n\r\n## Related Datasets:\r\n* 
            
79
__Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
83
__Zourlidou, S., Golze, J. and Sester, M. (2022).__ Dataset: GPS 
            
80
Trajectory Dataset of the Region of Hannover, Germany. 
84
Trajectory Dataset of the Region of Hannover, Germany. 
            
81
https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
85
https://doi.org/10.25835/9bidqxvl\r\n\r\n* __Zourlidou, S., Golze, J. 
            
82
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
86
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            
83
Information for the Chicago Trajectory Dataset. 
87
Information for the Chicago Trajectory Dataset. 
            
84
https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
88
https://doi.org/10.25835/0vifyzqi\r\n\r\n* __Zourlidou, S., Golze, J. 
            
85
and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic 
89
and Sester, M. (2022).__ Dataset: GPS Trajectory Dataset and Traffic 
            
86
Regulation Information of the Region of Edessa, Greece. 
90
Regulation Information of the Region of Edessa, Greece. 
            
87
https://doi.org/10.25835/v0mzwob3\r\n\r\n* __Zourlidou, S., Golze, J. 
91
https://doi.org/10.25835/v0mzwob3\r\n\r\n* __Zourlidou, S., Golze, J. 
            
88
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
92
and Sester, M. (2022).__ Dataset: Traffic Regulator Ground-truth 
            
89
Information of the City of Hannover, Germany. 
93
Information of the City of Hannover, Germany. 
            
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      "state": "active",
            
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171
      "description": "Intersection C with overlaid trajectory samples 
            
164
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and background image.",
            
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      "created": "2020-09-14T09:15:08.203897",
            
186
      "description": "Intersection D with overlaid trajectory samples 
194
      "description": "Intersection D with overlaid trajectory samples 
            
187
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and background image.",
            
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      "format": "PNG",
            
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      "name": "JunctionD.png",
            
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      "created": "2020-09-14T09:15:23.069227",
            
209
      "description": "Intersection E with overlaid trajectory samples 
217
      "description": "Intersection E with overlaid trajectory samples 
            
210
and background image.",
218
and background image.",
            
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      "format": "PNG",
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      "format": "PNG",
            
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      "mimetype_inner": null,
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      "mimetype_inner": null,
            
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      "name": "JunctionE.png",
            
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resource/94d81279-f4d9-44ef-824a-0f8a1ead220d/download/junctione.png",
            
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232
      "description": "Intersection F with overlaid trajectory samples 
240
      "description": "Intersection F with overlaid trajectory samples 
            
233
and background image.",
241
and background image.",
            
234
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      "format": "PNG",
            
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      "mimetype": "image/png",
            
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      "mimetype_inner": null,
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      "mimetype_inner": null,
            
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      "name": "JunctionF.png",
            
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      "state": "active",
            
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      "url": 
            
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256
resource/d02b7f48-3669-4497-9fe0-5015fb74ead4/download/junctionf.png",
            
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    {
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      "created": "2020-09-14T09:28:06.901537",
262
      "created": "2020-09-14T09:28:06.901537",
            
255
      "description": "Contains all crossing trajectories of 
263
      "description": "Contains all crossing trajectories of 
            
256
__intersection A__ with the following structure:\r\n\r\n* gid\r\n* 
264
__intersection A__ with the following structure:\r\n\r\n* gid\r\n* 
            
257
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
265
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
            
258
northutm\r\n* eastutm\r\n* speed",
266
northutm\r\n* eastutm\r\n* speed",
            
259
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      "hash": "",
            
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270
      "last_modified": "2020-09-14T09:30:13.084821",
            
263
      "metadata_modified": "2023-08-04T08:46:52.241656",
271
      "metadata_modified": "2023-08-04T08:46:52.241656",
            
264
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272
      "mimetype": "text/csv",
            
265
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      "mimetype_inner": null,
            
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      "name": "JunctionA.csv",
274
      "name": "JunctionA.csv",
            
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      "package_id": "1a97c0df-4659-43bd-99e9-d2573d31f5cd",
            
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279
      "state": "active",
            
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      "url": 
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      "url": 
            
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281
d31f5cd/resource/4be6c2d0-3f28-4945-9639-a53e8a5513a7/download/a.csv",
            
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      "url_type": ""
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      "url_type": ""
            
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    {
284
    {
            
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287
      "created": "2020-09-14T09:28:41.054344",
            
280
      "description": "Contains all crossing trajectories of 
288
      "description": "Contains all crossing trajectories of 
            
281
__intersection B__ with the following structure:\r\n\r\n* gid\r\n* 
289
__intersection B__ with the following structure:\r\n\r\n* gid\r\n* 
            
282
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
290
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
            
283
northutm\r\n* eastutm\r\n* speed",
291
northutm\r\n* eastutm\r\n* speed",
            
284
      "format": "CSV",
292
      "format": "CSV",
            
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      "hash": "",
293
      "hash": "",
            
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      "id": "7d20ecb4-4e5d-4e00-a052-279a82d6098c",
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      "last_modified": "2020-09-14T09:30:23.171516",
295
      "last_modified": "2020-09-14T09:30:23.171516",
            
288
      "metadata_modified": "2023-08-04T08:46:52.241772",
296
      "metadata_modified": "2023-08-04T08:46:52.241772",
            
289
      "mimetype": "text/csv",
297
      "mimetype": "text/csv",
            
290
      "mimetype_inner": null,
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      "mimetype_inner": null,
            
291
      "name": "JunctionB.csv",
299
      "name": "JunctionB.csv",
            
292
      "package_id": "1a97c0df-4659-43bd-99e9-d2573d31f5cd",
300
      "package_id": "1a97c0df-4659-43bd-99e9-d2573d31f5cd",
            
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      "size": 135449,
            
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      "state": "active",
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      "state": "active",
            
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      "url": 
305
      "url": 
            
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d31f5cd/resource/7d20ecb4-4e5d-4e00-a052-279a82d6098c/download/b.csv",
306
d31f5cd/resource/7d20ecb4-4e5d-4e00-a052-279a82d6098c/download/b.csv",
            
299
      "url_type": ""
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      "url_type": ""
            
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    },
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    {
309
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      "created": "2020-09-14T09:30:38.269882",
312
      "created": "2020-09-14T09:30:38.269882",
            
305
      "description": "Contains all crossing trajectories of 
313
      "description": "Contains all crossing trajectories of 
            
306
__intersection C__ with the following structure:\r\n\r\n* gid\r\n* 
314
__intersection C__ with the following structure:\r\n\r\n* gid\r\n* 
            
307
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
315
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
            
308
northutm\r\n* eastutm\r\n* speed",
316
northutm\r\n* eastutm\r\n* speed",
            
309
      "format": "CSV",
317
      "format": "CSV",
            
310
      "hash": "",
318
      "hash": "",
            
311
      "id": "80fc1fbd-28d8-4e49-afa0-8a29dbc58840",
319
      "id": "80fc1fbd-28d8-4e49-afa0-8a29dbc58840",
            
312
      "last_modified": "2020-09-14T09:30:38.233091",
320
      "last_modified": "2020-09-14T09:30:38.233091",
            
313
      "metadata_modified": "2023-08-04T08:46:52.241874",
321
      "metadata_modified": "2023-08-04T08:46:52.241874",
            
314
      "mimetype": "text/csv",
322
      "mimetype": "text/csv",
            
315
      "mimetype_inner": null,
323
      "mimetype_inner": null,
            
316
      "name": "JunctionC.csv",
324
      "name": "JunctionC.csv",
            
317
      "package_id": "1a97c0df-4659-43bd-99e9-d2573d31f5cd",
325
      "package_id": "1a97c0df-4659-43bd-99e9-d2573d31f5cd",
            
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      "position": 8,
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      "resource_type": null,
            
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      "size": 72674,
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      "size": 72674,
            
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      "state": "active",
329
      "state": "active",
            
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      "url": 
330
      "url": 
            
323
d31f5cd/resource/80fc1fbd-28d8-4e49-afa0-8a29dbc58840/download/c.csv",
331
d31f5cd/resource/80fc1fbd-28d8-4e49-afa0-8a29dbc58840/download/c.csv",
            
324
      "url_type": ""
332
      "url_type": ""
            
325
    },
333
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326
    {
334
    {
            
327
      "cache_last_updated": null,
335
      "cache_last_updated": null,
            
328
      "cache_url": null,
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      "cache_url": null,
            
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      "created": "2020-09-14T09:30:53.716147",
337
      "created": "2020-09-14T09:30:53.716147",
            
330
      "description": "Contains all crossing trajectories of 
338
      "description": "Contains all crossing trajectories of 
            
331
__intersection D__ with the following structure:\r\n\r\n* gid\r\n* 
339
__intersection D__ with the following structure:\r\n\r\n* gid\r\n* 
            
332
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
340
track_id\r\n* trip_id\r\n* date\r\n* timestamp\r\n* unixtime\r\n* 
            
333
northutm\r\n* eastutm\r\n* speed",
341
northutm\r\n* eastutm\r\n* speed",
            
334
      "format": "CSV",
342
      "format": "CSV",
            
335
      "hash": "",
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388
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each intersection A-F with the following structure:\r\n\r\n* 
            
407
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415
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  "terms_of_usage": "Yes",
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  "terms_of_usage": "Yes",
            
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  "title": "Speed profiles and GPS Trajectories for Traffic Rule 
521
  "title": "Speed profiles and GPS Trajectories for Traffic Rule 
            
514
Recognition (6 Junctions, Hannover, Germany)",
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Recognition (6 Junctions, Hannover, Germany)",
            
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trajectory-analysis-at-intersections-for-traffic-rule-identification",
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trajectory-analysis-at-intersections-for-traffic-rule-identification",
            
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  "version": ""
            
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}
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}