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: https://data.uni-hannover.de/dataset/57869009-330e-49be-bc63-56fb56952bdb/resource/c5fddb86-82f5-491a-9099-51e5a9109cda/download/edessa_traj.png" alt="Overview of the dataset containing the trajectories collected in Edessa.">

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" alt="">

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

OSM point features in Europe with tags categorized as POI in the Geofabrik taxonomy. Extracted from original OpenStreetMap database (osm.pbf) in 09.2019 (https://www.openstreetmap.org/#map=7/46.825/8.224)

This dataset is used for the classification of traffic intersection regulations using (car) GPS trajectory data. The coverage is mostly a large part of the city of Hannover, Germany.

The overview of the GPS trajectory dataset is given in the below figure: https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/ecfbc059-9ca3-4cbc-8774-de091f0fbcd6/download/hannover_traj.png" alt="Overview">

When the trajectory dataset is combined with the related intersection ground-truth information (available at: https://doi.org/10.25835/cqg0x1el): https://data.uni-hannover.de/dataset/379ed322-a9ea-48f3-bc13-2f5ea3174470/resource/bc832922-aedd-4bac-86b3-5d4a753bfecc/download/hannover_rules_traj.png" alt="Combined">

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: 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: 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

• 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

The data allows to reproduce Figure1, Figure2 and Figure3 of the article "The Scales of Human Mobility". The article is currently under peer review. The aggregated data here presented is derived from anonymized GPS trajectories of ~700,000 individuals worldwide collected by a major telecommunication company. The data processing and analyses are fully described in the article. Raw data are not publicly available to preserve individuals' privacy under the European General Data Protection Regulation.Figure_1_data_size.csv describes the size of individual containers at different hierarchical levels.Figure1_data_time.csv describes the time spent within a container at different hierarchical levels. Figure2_panels_a/b/c/d_source.csv include the Source data necessary to reproduce Figure 2. Figure3.pkl includes the size of the hierarchical levels for selected individuals, their corresponding country of origin, walkability around the home location, urban/rural level, and gender. Data in .pkl format and can be opened in Python (see here: https://docs.python.org/2/library/pickle.html)

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: https://data.uni-hannover.de/dataset/1a97c0df-4659-43bd-99e9-d2573d31f5cd/resource/0571e0e3-19d6-4e45-9da5-6cb932a3d7d7/download/junctionall.png" alt="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: 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. (2022). Dataset: Traffic Regulator Ground-truth Information of the City of Hannover, Germany. https://doi.org/10.25835/cqg0x1el

This dataset contains the vehicular traces from a location in Jeju-si, South Korea. The dataset contains 8,495,739 traces of vehicles. It comprises of major areas/junctions of which one is the intersection from where the Jeju International Airport and Jeju Seaport traffic passes on daily. Jeju International Airport is one of the busiest airpots in the world. Four types of vehicles were considered in the simulation of dataset, i.e., buses, trucks, passenger-cars, taxies. Each trace contains:time (timestep in seconds)id (unique id of vehicle, IDs starting with a prefix of jTx, jPs, jBs, and jTr represent a taxi, passenger, bus, and a truck respectively)latitude (y-axis value in geo-coordinate format)longitude (x-axis value in geo-coordinate formattype (type of vehicle, bus, truck, passenger-car, taxi)angle (angle ranging from 0~360 degree, representing the direction of a vehicle)speed (speed of a vehicle at a timestep t)lane (lane on which a vehicle is at timestep t)pos (position of a vehicle in meters travelled on a lane at a timestep t)

Title: Attributing pedestrian networks with semantic information based on multi-source spatial dataAbstract: The lack of associating pedestrian networks, i.e., the paths and roads used for non-vehicular travel, with information about semantic attribution is a major weakness for many applications, especially those supporting accurate pedestrian routing. Researchers have developed various algorithms to generate pedestrian walkways based on datasets, including high-resolution images, existing map databases, and GPS data; however, the semantic attribution of pedestrian walkways is often ignored. The objective of our study is to automatically extract semantic information including incline values and the different categories of pedestrian paths from multi-source spatial data, such as crowdsourced GPS tracking data, land use data, and motor vehicle road (MVR) networks. Incline values for each pedestrian path were derived from tracking data through elevation filtering using wavelet theory and a similarity-based map-matching method. To automatically categorize pedestrian paths into five classes including sidewalk, crosswalk, entrance walkway, indoor path, and greenway, we developed a hierarchical strategy of spatial analysis using land use data and MVR networks. The effectiveness of our proposed method is demonstrated using real datasets including GPS tracking data collected by volunteers, land use data acquired from OpenStreetMap, and MVR network data downloaded from Gaode Map.

This dataset contains the ground-truth intersection regulators for all intersections crossed by the open GPS trajectory data of Chicago, published on Mapconstruction.org.

Data Acquisition

The ground-truth data is generated by manually inspecting images at the intersections crossed by the published trajectory dataset on Mapconstruction.org. These images are derived from mapillary.com at the respective intersections. Satellite images are investigated as an additional data source, if needed. This procedure result in labels for each intersection arm.

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: 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

• 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

Context

Technology has many effects on the transportation industry.

Content

We have provided an accurate dataset describing a complete year (from 01/07/2013 to 30/06/2014) of the trajectories for all the 442 taxis running in the city of Porto, in Portugal (i.e. one CSV file named "train.csv"). These taxis operate through a taxi dispatch central, using mobile data terminals installed in the vehicles. We categorize each ride into three categories: A) taxi central based, B) stand-based or C) non-taxi central based. For the first, we provide an anonymized id, when such information is available from the telephone call. The last two categories refer to services that were demanded directly to the taxi drivers on a B) taxi stand or on a C) random street.

Each data sample corresponds to one completed trip. It contains a total of 9 (nine) features, described as follows:

• TRIP_ID: (String) It contains an unique identifier for each trip;

• CALL_TYPE: (char) It identifies the way used to demand this service. It may contain one of three possible values: ‘A’ if this trip was dispatched from the central; ‘B’ if this trip was demanded directly to a taxi driver on a specific stand; ‘C’ otherwise (i.e. a trip demanded on a random street).

• ORIGIN_CALL: (integer) It contains an unique identifier for each phone number which was used to demand, at least, one service. It identifies the trip’s customer if CALL_TYPE=’A’. Otherwise, it assumes a NULL value;

• ORIGIN_STAND: (integer): It contains an unique identifier for the taxi stand. It identifies the starting point of the trip if CALL_TYPE=’B’. Otherwise, it assumes a NULL value;

• TAXI_ID: (integer): It contains an unique identifier for the taxi driver that performed each trip;

• TIMESTAMP: (integer) Unix Timestamp (in seconds). It identifies the trip’s start;

• DAYTYPE: (char) It identifies the daytype of the trip’s start. It assumes one of three possible values: ‘B’ if this trip started on a holiday or any other special day (i.e. extending holidays, floating holidays, etc.); ‘C’ if the trip started on a day before a type-B day; ‘A’ otherwise (i.e. a normal day, workday or weekend).

• MISSING_DATA: (Boolean) It is FALSE when the GPS data stream is complete and TRUE whenever one (or more) locations are missing

• POLYLINE: (String): It contains a list of GPS coordinates (i.e. WGS84 format) mapped as a string. The beginning and the end of the string are identified with brackets (i.e. [ and ], respectively). Each pair of coordinates is also identified by the same brackets as [LONGITUDE, LATITUDE]. This list contains one pair of coordinates for each 15 seconds of trip. The last list item corresponds to the trip’s destination while the first one represents its start;

The total travel time of the trip (the prediction target of this competition) is defined as the (number of points-1) x 15 seconds. For example, a trip with 101 data points in POLYLINE has a length of (101-1) * 15 = 1500 seconds. Some trips have missing data points in POLYLINE, indicated by MISSING_DATA column, and it is part of the challenge how you utilize this knowledge.

Acknowledgements

Data from ECML/PKDD 15: Taxi Trip Time Prediction (II) Competition

Inspiration

Added this dataset because competition datasets do not appear in the dataset search and this dataset could help learn basic methods in the area of geo-spatial analysis and trajectory handling

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which was one of the six collected as part of the TGSIM project, contains data collected from six 4K cameras mounted on tripods, positioned on three overpasses along I-395 in Washington, D.C. The cameras captured distinct segments of the highway, and their combined overlapping and non-overlapping footage resulted in a continuous trajectory for the entire section covering 0.5 km. This section covers a major weaving/mandatory lane-changing between L'Enfant Plaza and 4th Street SW, with three lanes in the eastbound direction and a major on-ramp on the left side. In addition to the on-ramp, the section covers an off-ramp on the right side. The expressway includes one diverging lane at the beginning of the section on the right side and one merging lane in the middle of the section on the left side. For the purposes of data extraction, the shoulder of the merging lane is also considered a travel lane since some vehicles illegally use it as an extended on-ramp to pass other drivers (see I395_ref_image.png for details). The cameras captured continuous footage during the morning rush hour (8:30 AM-10:30 AM ET) on a sunny day. During this period, vehicles equipped with SAE Level 2 automation were deployed to travel through the designated section to capture the impact of SAE Level 2-equipped vehicles on adjacent vehicles and their behavior in congested areas, particularly in complex merging sections. These vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• I395-final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I395_ref_image.png is the aerial reference image that defines the geographic region and the associated roadway segments.
• I395_boundaries.csv contains the coordinates that define the roadway segments (n=X). The columns "x1" to "x5" represent the horizontal pixel values in the reference image, with "x1" being the leftmost boundary line and "x5" being the rightmost boundary line, while the column "y" represents corresponding vertical pixel values. In total, the boundary file defines five lanes. Each set of reference pixels can be converted to meters using the provided 1 pixel = 0.3-meter conversion, with (0,0) global reference coordinates at the top-left of the reference image.
• Annotation on Regions.zip includes images that visually map lanes I395_lane-1.png through I395_lane-6.png to their associated numerical lane IDs.

This dataset consists of weekly trajectory information of Gulf Stream Warm Core Rings (WCR) that existed between 2021 and 2023. This work builds upon two previous datasets:

(i) Warm Core Ring trajectory information from 2000 to 2010 -- Porter et al. (2022) (https://doi.org/10.5281/zenodo.7406675)

(ii) Warm Core Ring trajectory information from 2011 to 2020 -- Silver et al. (2022a) (https://doi.org/10.5281/zenodo.6436380).

Combining these three datasets (previous two and this one), a total of 24 years of weekly Warm Core Ring trajectories are now available. An example of how to use such a dataset can be found in Silver et al. (2022b).

The format of the dataset is similar to that of Porter et al. (2022) and Silver et al. (2022a), and the following description is adapted from those datasets. This dataset is comprised of individual files containing each ring’s weekly center location and its surface area for 81 WCRs that existed and tracked between January 1, 2021 and December 31, 2023 (5 WCRs formed in 2020 and still existed in 2021; 28 formed in 2021; 30 formed in 2022; 18 formed in 2023). Each Warm Core Ring is identified by a unique alphanumeric code 'WEyyyymmddX', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'X' represents the sequential sighting (formation) of the eddy in that particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the previous year and absorbed by the initial alphabets for the next year. For example, the first ring formed in 2022 has a trailing alphabet of 'H', which signifies that a total of seven rings were carried over from 2021 which were still present on January 1, 2022 and were assigned the initial seven alphabets (A, B, C, D, E, F and G). Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables every week, “Lon”- the ring center’s longitude, “Lat”- the ring center’s latitude, “Area” - the rings size in km^2, and “Date” in days – which is the number of days since Jan 01, 0000. Five rings formed in the year 2020 that carried over into the year 2021 were included in this dataset. These rings include ‘WE20200724Q’, ‘WE20200826R’, ‘WE20200911S’, ‘WE20200930T’, and ‘WE20201111W’. The two rings that formed in 2023, and were carried over into the following year were included with their full trajectories going into the year 2024. These rings include ‘WE20231006U’ and ‘WE20231211W’.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts (Gangopadhyay et al., 2019) used to create this dataset are 2-3 times a week from 2021-2023. Thus, we used approximately 360+ Charts for the 3 years of analysis. All of these charts were reanalyzed between -75° and -55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Description

This dataset contains fingerprint information of WLAN access points and BLE beacons with a known position and IMU sensor data. Data was collected on the floor of the Web and Information Systems Engineering (WISE) Lab at the VUB (Pleinlaan 9, 3rd floor) with 110 training reference points and 30 test data points. Each reference point was recorded for 20 seconds in four different orientations.

More information is given in the README.pdf file included in the dataset.

File Contents

/wlan_aps.csv: Wireless access point information

/ble_beacons.csv: BLE beacon positions

/misc: Miscelanieous resources

/misc/floorplan.png: PNG version of the floorplan

/misc/floorplan_medium.png: PNG version of the floorplan (medium quality)

/misc/datapoints.svg: Training and test data points visualisation

/misc/datapoints.csv: Training data points CSV

/misc/testdatapoints.csv: Test data points CSV

/misc/spaces.geo.json: GeoJSON feature collection of symbolic spaces

/misc/documentation.css: README documentation CSS (unrelated to the dataset)

/train: Training data points (110 in 4 orientations)

/train/raw: Raw unprocessed data points (not aggregated)

/train/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/train/raw/imu_fingerprints.csv: Raw IMU data collection

/train/raw/ble_fingerprints.csv: Raw BLE fingerprints

/train/aggregated: Processed aggregated data points

/train/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/train/aggregated/ble_fingerprints.csv: BLE fingerprints

/train/aggregated/imu_fingerprints.csv: IMU data collection

/train/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/test: Test data points (30 in 4 orientations)

/test/raw: Raw unprocessed test data points (not aggregated)

/test/raw/wlan_fingerprints.csv: Raw WLAN fingerprints

/test/raw/imu_fingerprints.csv: Raw IMU data collection

/test/raw/ble_fingerprints.csv: Raw BLE fingerprints

/test/aggregated: Processed aggregated data points

/test/aggregated/wlan_fingerprints.csv: WLAN fingerprints

/test/aggregated/ble_fingerprints.csv: BLE fingerprints

/test/aggregated/imu_fingerprints.csv: IMU data collection

/test/aggregated/wlan-ble_fingerprints.csv: WLAN and BLE fingerprints merged

/trajectories: Test trajectories (10)

/trajectories/???: Trajectory directory, ??? is the name of the trajectory

/trajectories/???/???_ble.csv: BLE advertisements received during the trajectory

/trajectories/???/???_imu.csv: IMU data from the trajectory

/trajectories/???/???_wlan.csv: WLAN signals received during the trajectory

This dataset contains weekly trajectory information of Gulf Stream Warm Core Rings from 2011-2020. This dataset is comprised of individual files containing each ring’s weekly center location and its area for 282 WCRs present between January 1, 2011 and December 31, 2020. Each Warm Core Ring and is identified by a unique alphanumeric code 'WEyyyymmddA', where 'WE' represents a Warm Eddy (as identified in the analysis charts); 'yyyymmdd' is the year, month and day of formation; and the last character 'A' represents the sequential sighting of the eddies in a particular year. Continuity of a ring which passes from one year to the next is maintained by the same character in the first sighting. For example, the first ring in 2017 having a trailing alphabet of 'E' indicates that four rings were carried over from 2016 which were still observed on January 1, 2017. Each ring has its own netCDF (.nc) filename following its alphanumeric code. Each file contains 4 variables, “Lon”- the ring center’s weekly longitude, “Lat”- the ring center’s weekly latitude, “Area” - the rings weekly size in km², and “Date” in days - representing the week since Jan 01, 0000.

The process of creating the WCR tracking dataset follows the same methodology of the previously generated WCR census (Gangopadhyay et al., 2019, 2020). The Jenifer Clark’s Gulf Stream Charts used to create this dataset are 2-3 times a week from 2011-2020. Thus, we used approximately 1560 Charts for the 10 years of analysis. All of these charts were reanalyzed between 75° and 55°W using QGIS 2.18.16 (2016) and geo-referenced on a WGS84 coordinate system (Decker, 1986).

Gangopadhyay, A., G. Gawarkiewicz, N. Etige, M. Monim and J. Clark, 2019. An Observed Regime Shift in the Formation of Warm Core Rings from the Gulf Stream, Nature - Scientific Reports, https://doi.org/10.1038/s41598-019-48661-9. www.nature.com/articles/s41598-019-48661-9.

Gangopadhyay, A., N. Etige, G. Gawarkiewicz, A. M. Silver, M. Monim and J. Clark, 2020. A Census of the Warm Core Rings of the Gulf Stream (1980-2017). Journal of Geophysical Research, Oceans, 125, e2019JC016033. https://doi.org/10.1029/2019JC016033.

QGIS Development Team. QGIS Geographic Information System (2016).

Decker, B. L. World Geodetic System 1984. World geodetic system 1984 (1986).

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected from twelve 4K stationary infrastructure cameras installed in the Foggy Bottom neighborhood of Washington, D.C. The cameras captured four intersections, adjacent crosswalks, road segments between the intersections, and partial road segments extending out from the intersections totaling more than one full block of coverage. These segments are represented by polygons to bound travel lanes, parking lanes, crosswalks, and intersections for detection and analysis purposes (see Reference_Image_Foggy Bottom.png for details). The cameras captured continuous footage during a weekday commute between 3:00PM-5:00PM ET on a sunny day. During this period, one test vehicle equipped with SAE Level 3 automation was deployed to perform various complex maneuvers at both stop signs and traffic signals, including both protected and permitted left turns, to capture human driving behaviors when interacting with automated vehicles. The automated vehicles are indicated in the dataset.

As part of this dataset, the following files were provided:

• TGSIM-Foggy Bottom-Data.csv contains the numerical data to be used for analysis that includes vehicle/bicycle/pedestrian trajectory data at every 0.1 second. Road user type, width, and length are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.0186613838586-meter conversion.
• Reference_Image_Foggy Bottom.png is the aerial reference image that defines the geographic region and the associated roadway segments.
• Foggy_Bottom_boundaries.txt contains the coordinates that define the roadway segments (n = 49). Each polygon is a list of four to six coordinate pairs measured in pixels (which can be converted to meters using the provided 1 pixel = 0.0186613838586-meter conversion), with (0,0) global reference coordinates at the top-left of the reference image.
• Annotation on Regions.zip, which includes i1.png, i2.png,..., i49.png, are images that visually map the road segment IDs (indicated by the number following the i in the file name) to the reference image.

GPS trajectory dataset for learning task-agnostic feature representations of geographic locations from unlabeled GPS trajectories

The water level data (modeled total water level and in situ measurements) and mapping data produced for the publication "Washout versus Washover: Distinct Trajectories of Barrier Reshaping" in the Journal of Geophysical Research: Earth Surface

This study extends a prior National Institute (NIJ) funded study on mirco level places that examined the concentration of crime at places over time. The current study links longitudinal crime data to a series of other databases. The purpose of the study was to examine the possible correlates of variability in crime trends over time. The focus was on how crime distributes across very small units of geography. Specifically, this study investigated the geographic distribution of crime and the specific correlates of crime at the micro level of geography. The study reported on a large empirical study that investigated the "criminology of place." The study linked 16 years of official crime data on street segments (a street block between two intersections) in Seattle, Washington, to a series of datasets examining social and physical characteristics of micro places over time, and examined not only the geography of developmental patterns of crime at place but also the specific factors that are related to different trajectories of crime. The study used two key criminological perspectives, social disorganization theories and opportunity theories, to inform their identification of risk factors in the study and then contrast the impacts of these perspectives in the context of multivariate statistical models.

A trajectory restoration algorithm for low-sampling-rate floating car data and complex urban road networks

The main dataset is a 70 MB file of trajectory data (I294_L1_final.csv) that contains position, speed, and acceleration data for small and large automated (L1) vehicles and non-automated vehicles on a highway in a suburban environment. Supporting files include aerial reference images for ten distinct data collection “Runs” (I294_L1_RunX_with_lanes.png, where X equals 8, 18, and 20 for southbound runs and 1, 3, 7, 9, 11, 19, and 21 for northbound runs). Associated centerline files are also provided for each “Run” (I-294-L1-Run_X-geometry-with-ramps.csv). In each centerline file, x and y coordinates (in meters) marking each lane centerline are provided. The origin point of the reference image is located at the top left corner. Additionally, in each centerline file, an indicator variable is used for each lane to define the following types of road sections: 0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments. The number attached to each column header is the numerical ID assigned for the specific lane (see “TGSIM – Centerline Data Dictionary – I294 L1.csv” for more details). The dataset defines eight lanes (four lanes in each direction) using these centerline files. Images that map the lanes of interest to the numerical lane IDs referenced in the trajectory dataset are stored in the folder titled “Annotation on Regions.zip”. The southbound lanes are shown visually in I294_L1_Lane-2.png through I294_L1_Lane-5.png and the northbound lanes are shown visually in I294_L1_Lane2.png through I294_L1_Lane5.png.

This dataset was collected as part of the Third Generation Simulation Data (TGSIM): A Closer Look at the Impacts of Automated Driving Systems on Human Behavior project. During the project, six trajectory datasets capable of characterizing human-automated vehicle interactions under a diverse set of scenarios in highway and city environments were collected and processed. For more information, see the project report found here: https://rosap.ntl.bts.gov/view/dot/74647. This dataset, which is one of the six collected as part of the TGSIM project, contains data collected using one high-resolution 8K camera mounted on a helicopter that followed three SAE Level 1 ADAS-equipped vehicles with adaptive cruise control (ACC) enabled. The three vehicles manually entered the highway, moved to the second from left most lane, then enabled ACC with minimum following distance settings to initiate a string. The helicopter then followed the string of vehicles (which sometimes broke from the sting due to large following distances) northbound through the 4.8 km section of highway at an altitude of 300 meters. The goal of the data collection effort was to collect data related to human drivers' responses to vehicle strings. The road segment has four lanes in each direction and covers major on-ramp and an off-ramp in the southbound direction and one on-ramp in the northbound direction. The segment of highway is operated by Illinois Tollway and contains a high percentage of heavy vehicles. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a sunny day.

As part of this dataset, the following files were provided:

• I294_L1_final.csv contains the numerical data to be used for analysis that includes vehicle level trajectory data at every 0.1 second. Vehicle size (small or large), width, length, and whether the vehicle was one of the test vehicles with ACC engaged ("yes" or "no") are provided with instantaneous location, speed, and acceleration data. All distance measurements (width, length, location) were converted from pixels to meters using the following conversion factor: 1 pixel = 0.3-meter conversion.
• I294_L1_RunX_with_lanes.png are the aerial reference images that define the geographic region and associated roadway segments of interest (see bounding boxes on northbound and southbound lanes) for each run X.
• I-294-L1-Run_X-geometry-with-ramps.csv contain the coordinates that define the lane centerlines for each Run X. The "x" and "y" columns represent the horizontal and vertical locations (in meters) in the reference image, respectively. The "ramp" columns define the type of roadway segment (0=no ramp, 1=on-ramps, 2=off-ramps, and 3=weaving segments). In total, the centerline files define eight lanes (four in each direction).
• Annotation on Regions.zip, which includes images that visually map four northbound lanes (I294_L1_Lane2.png through I294_L1_Lane5.png) and four southbound lanes (I294_L1_Lane-2.png through I294_L1_Lane-5.png) to their associated numerical lane IDs.