The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 6 data collection runs, collected using an Instrumented Research Vehicle (IRV) along freeways and arterials in western Massachusetts in the summer of 2016 to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/b3k6-qwyh) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr).

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains the instantaneous data processed from radar and GPS. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5).

The data describe freeway car-following behavior (such as velocity, acceleration, and relative position) for the car-following instances observed during 59 data collection runs, performed through the Federal Highway Administration (FHWA) Turner Fairbank Highway Research Center’s (TFHRC) Living Laboratory (LL). Data were collected using an Instrumented Research Vehicle (IRV) along freeways in northern Virginia to better understand work zone driver behaviors. The USDOT Volpe National Transportation Systems Center (Volpe Center) identified, isolated, and classified individual car following instances from within the raw datasets (classification parameters included roadway type, level of congestion, and speed limit), then processed, refined, and cleaned the dataset. This table contains metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/k74u-yqu6) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).

Click “Export” on the right to download the vehicle trajectory data. The associated metadata and additional data can be downloaded below under "Attachments". Researchers for the Next Generation Simulation (NGSIM) program collected detailed vehicle trajectory data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, Georgia. Data was collected through a network of synchronized digital video cameras. NGVIDEO, a customized software application developed for the NGSIM program, transcribed the vehicle trajectory data from the video. This vehicle trajectory data provided the precise location of each vehicle within the study area every one-tenth of a second, resulting in detailed lane positions and locations relative to other vehicles. Click the "Show More" button below to find additional contextual data and metadata for this dataset. For site-specific NGSIM video file datasets, please see the following: - NGSIM I-80 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-I-80-Vide/2577-gpny - NGSIM US-101 Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-US-101-Vi/4qzi-thur - NGSIM Lankershim Boulevard Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Lankershi/uv3e-y54k - NGSIM Peachtree Street Videos: https://data.transportation.gov/Automobiles/Next-Generation-Simulation-NGSIM-Program-Peachtree/mupt-aksf

2022년 기준으로 촬영한 인천광역시 도로구간(중앙ㅇ성인 있는 왕복 2차선 도로 이상)에 대한 3차원 고정밀 MMS 라이다 스캔 데이터

ITS DataHub has partnered with the Federal Highway Administration's (FHWA's) Next Generation SIMulation (NGSIM) program to make available detailed vehicle trajectory data and supporting data files along with the raw and processed video files from the NGSIM data collection efforts. Researchers for the NGSIM program collected the specified data on southbound US 101 and Lankershim Boulevard in Los Angeles, CA, eastbound I-80 in Emeryville, CA and Peachtree Street in Atlanta, GA. This article provides a brief overview of the NGSIM program data collection as well as what types of data are available on ITS DataHub. Some examples of possible uses for the data and information on how to cite the various NGSIM datasets are also included.

인천광역시 관내의 육교, 교량, 터널, 톨게이트 등 100건의 도로시설물에 대하여 라이다 촬영 데이터를 기반으로 3D 모델링한 데이터