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:

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 two SAE Level 2 ADAS-equipped vehicles through automated lane change maneuvers and as part of a string once the desired lane was achieved and ACC was enabled. 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 automated lane changes and as part of a string. The road segment has four lanes in each direction and covers a major on-ramp and one off-ramp in the southbound direction and one on-ramp as well as two off-ramps 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 cloudy day. As part of this dataset, the following files were provided:

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 2 ADAS-equipped vehicles (one at a time) northbound through the 4 km long segment at an altitude of 200 meters. Once a vehicle finished the segment, the helicopter would return to the beginning of the segment to follow the next SAE Level 2 ADAS-equipped vehicle to ensure continuous data collection. The segment was selected to study mandatory and discretionary lane changing and last-minute, forced lane-changing maneuvers. The segment has five off-ramps and three on-ramps to the right and one off-ramp and one on-ramp to the left. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (3:00 PM-5:00 PM CT) on a cloudy day. As part of this dataset, the following files were provided:

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 the fixed location aerial videography approach with one high-resolution 8K camera mounted on a helicopter hovering over a short segment of I-94 focusing on the merge and diverge points in Chicago, IL. The altitude of the helicopter (approximately 213 meters) enabled the camera to capture 1.3 km of highway driving and a major weaving section in each direction (where I-90 and I-94 diverge in the northbound direction and merge in the southbound direction). The segment has two off-ramps and two on-ramps in the northbound direction. All roads have 88 kph (55 mph) speed limits. The camera captured footage during the evening rush hour (4:00 PM-6:00 PM CT) on a cloudy day. During this period, two SAE Level 2 ADAS-equipped vehicles drove through the segment, entering the northbound direction upstream of the target section, exiting the target section on the right through I-94, and attempting to perform a total of three lane-changing maneuvers (if safe to do so). These vehicles are indicated in the dataset. As part of this dataset, the following files were provided:,

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:,

This system will produce reports to enable FHWA to better and more effectively manage R&D projects on cost, schedule and performance criteria. It will also allow Turner Fairbank Highway Research center managers to more efficiently respond to data calls from internal and external sources (such as TRB and NSF) regarding the research that is performed at TFHRC.

Cette ressource fait partie du jeu de données Réseau urbain TLP Mobilités. Le fichier couvre la période du 01/11/24 au 13/04/25. réseau : TLP Mobilités modes de transport : bus Le tableau ci-dessous affiche si le fichier ou le champ est présent dans le GTFS. Static data gtfs-20241024-172839.zip 2024-11-01 2025-04-13 2024-11-25 Latest modification 100% Availability rate Up to date 95 warnings during validation GTFS details Download Automatic conversions Download GeoJSON Download NeTEx Automatic NeTEx conversion are created from the associated GTFS file and don't contain additional information which can be described in NeTEx. Automatic NeTEx conversions will not be available after 2025-06-01. Download the automatic NeTEx conversion Cancel Real-time data Horaires temps-réel du réseau Tarbes / Lourdes - TLP mobilités (GTFS / GTFS-RT) real-time Latest modification 100% Availability rate 599 errors during validation Features available in the resource: trip_updates vehicle_positions gtfs-rt details Browse the API Past versions of the dataset resources are available in the Backed up resources section. Documentation Producers can share in this section design guidelines, documentation, etc. Use the documentation type on data.gouv.fr. You're using this dataset? Send an email! Quality indicators Latest quality indicators Availability Compliance Freshness 100% 100% 100% transport.data.gouv.fr computes quality indicators daily to assess the quality of the published data. Learn more. Community resources Reusers can share in this section edits made on resources. Publish your own ressource on data.gouv.fr or browse our documentation. Discussions loading discussions... You need to be connected to start a discussion Backed up resources File Publication date Validity period Format gtfs-20241024-172839.zip 2024-11-25 2024-11-01 to 2025-04-13 GTFS GTFS 2024-11-05 2024-11-01 to 2025-04-13 GTFS gtfs-20240703-214137.zip 2024-08-05 2024-07-04 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-07-12 2024-07-12 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-07-11 2024-05-07 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-05-07 2024-05-07 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-04-03 2024-04-03 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-03-26 2024-03-26 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-03-16 2024-03-16 to 2024-10-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-03-15 2024-01-02 to 2024-03-23 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-01-10 2024-01-02 to 2024-03-23 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2024-01-01 2024-01-02 to 2024-03-23 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-12-18 2023-10-26 to 2023-12-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-10-27 2023-10-26 to 2023-12-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-10-26 2023-10-26 to 2023-12-31 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-10-25 2023-10-02 to 2023-11-04 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-10-02 2023-10-02 to 2023-11-04 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-09-04 2023-09-04 to 2023-10-28 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-08-24 2023-06-30 to 2023-09-03 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-06-30 2023-06-30 to 2023-09-03 GTFS Horaires théoriques du réseau Tarbes / Lourdes - TLP mobilités (GTFS) 2023-06-28 2023-04-08 to 2023-06-29 GTFS Horaires théoriques du réseau

M2T3NPTRB (or tavg3_3d_trb_Np) is a 3-dimensional 3-hourly time averaged data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of turbulence diagnostics on 42 pressure levels, such as total scalar diffusivity, total momentum diffusivity, momentum diffusivity from Louis, and Richardson number from Louis. The data field is available every three hour starting from 01:30 UTC, e.g.: 01:30, 04:30, … , 22:30 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

,Dane zebrane na cele realizacji projektu pt.: Opracowanie innowacyjnego system powietrznego typu VTOL służącego do monitorowania obszarów rolnych, leśnych oraz krajobrazu” w ramach Działania 1.1 Programu Operacyjnego Inteligentny Rozwój 2014-2020 współfinansowanego ze środków Europejskiego Funduszu Rozwoju Regionalnego tzw. „Szybka Ścieżka”. Nr wniosku POIR.01.01.01-00-0302/17. Celem badawczym projektu było uzyskanie kompleksowej wiedzy na temat oceny stanu roślinności z wykorzystaniem statku powietrznego wyposażonego w kamery multispektralne. Projekt obejmował ocenę stopnia wybarwienia roślin i powiązania intensywności koloru z podstawowymi czynnikami odpowiedzialnymi za zawartość chlorofilu tj. stanu odżywienia azotem i zawartością wody w glebie. System VTOL wykonywał pomiary zawartości azotu oraz wilgotności gleby. Badaniom poddane zostały najczęściej uprawiane produkty rolne w Polsce oraz tereny zalesione aglomeracji miejskich.,

국방전자조달 신기술(핵심기술, 핵심부품국산화, 신속시험, 기술현황 요청 등) 관련 입찰공고 사업 정보로서 순번, 업무구분, 입찰구분, 공고일자, 입찰건명(사업명), 발주기관 정보를 제공합니다.