The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the Caltrans – Performance Measurement System (PeMS). Speed data from this dataset were used to derive the freeway travel time. There are three separate text files with one for each operational condition.

The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the Caltrans – Performance Measurement System (PeMS). Speed data from this dataset were used to derive the freeway travel time. There are three separate text files with one for each operational condition.

The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.

The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.

The data in this data environment was collected from the Pasadena, California testbed, namely I-210, SR 134, and nearby arterials. The source of these data is from the National Center for Environmental Information – National Oceanic and Atmospheric Administration. Precipitation information from this data source is used in the cluster analysis.

The data in this repository were collected from the San Diego, California testbed, namely, I-15 from the interchange with SR-78 in the north to the interchange with SR-163 in the south, along the mainline and at the entrance ramps. This file contains information on the field observation and simulation results for speed profile from the Dallas, Texas testbed. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

The data in this repository were collected from the San Diego, California testbed, namely, I-15 from the interchange with SR-78 in the north to the interchange with SR-163 in the south, along the mainline and at the entrance ramps. This file contains information on the field observation and simulation results for speed profile from the Dallas, Texas testbed. The time reported for the speed profiles are between 2:00PM to 8:00PM in increments of 10 minutes.

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 car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).

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 car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).

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 car-following instances recorded by Volpe staff. See also the runs table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/285w-yjf5) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/uvrt-varj).

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 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 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).

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).

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).

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 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 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 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 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 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 metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) 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 metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) 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 metadata about each data collection run. See also the instances table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/74ug-57tr) and radar table (https://datahub.transportation.gov/Automobiles/Enhancing-Microsimulation-Models-for-Improved-Work/4qbx-egtn).

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. Basic Safety Messages (BSM) sent by connected vehicles (CVs) through either the cellular network or Dedicated Short Range Communication (DSRC) when the vehicle is in the range of Roadside Units (RSU). These messages were received by the traffic management center (TMC).

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. Basic Safety Messages (BSM) sent by connected vehicles (CVs) through either the cellular network or Dedicated Short Range Communication (DSRC) when the vehicle is in the range of Roadside Units (RSU). These messages were received by the traffic management center (TMC).

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains real-time volume, speed and loop occupancy data that were collected from WSDOT’s simulated roadway sensors every 20 seconds and aggregated according to user defined procedures and threshold by the Infrastructure Traffic Sensor System (TSS) Data Aggregator software.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains real-time volume, speed and loop occupancy data that were collected from WSDOT’s simulated roadway sensors every 20 seconds and aggregated according to user defined procedures and threshold by the Infrastructure Traffic Sensor System (TSS) Data Aggregator software.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains queue warning messages that were recommended by the INFLO Q-WARN algorithm and sent by the traffic management center to vehicles to warn drivers upstream of the queue. The objective of queue warning is to provide a vehicle operator sufficient warning of impending queue backup in order to brake safely, change lanes, or modify route such that secondary collisions can be minimized or even eliminated.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

Data is from the small-scale demonstration of the Intelligent Network Flow Optimization (INFLO) Prototype System and applications in Seattle, Washington. Connected vehicle systems were deployed in 21 vehicles in a scripted driving scenario circuiting this I-5 corridor northbound and southbound during morning rush hour. This data set contains speed harmonization messages that were recommended by the INFLO SPD-HARM algorithm and sent by the traffic management center to the connected vehicles, which provided drivers with the suggested speed while driving on the segment of I-5 that was included in the test. The objective of speed harmonization is to dynamically adjust and coordinate maximum appropriate vehicle speeds in response to downstream congestion, incidents, and weather or road conditions in order to maximize traffic throughput and reduce crashes.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 1.1.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 3.0.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 3.0.

The WZDx Specification enables infrastructure owners and operators (IOOs) to make harmonized work zone data available for third party use. The intent is to make travel on public roads safer and more efficient through ubiquitous access to data on work zone activity. Specifically, the project aims to get data on work zones into vehicles to help automated driving systems (ADS) and human drivers navigate more safely. MCDOT leads the effort to aggregate and collect work zone data from the AZTech Regional Partners. A continuously updating archive of the WZDx feed data can be found at ITS WorkZone Data Sandbox. The live feed is currently compliant with WZDx specification version 3.0.

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

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

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

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program. This is the primary loop detector data table. It contains one-minute volume, occupancy, and data quality flags for the arterial loop detector data.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program.The freeway data consists of two months of data (Sept 15 2011 through Nov 15 2011) from dual-loop detectors deployed in the main line and on-ramps of a Portland-area freeway. The section of I-205 NB covered by this test data set is 10.09 miles long and the section of I-205 SB covered by this test data set is 12.01 miles long The data includes: flow, occupancy, and speed.

This set of data files was acquired under USDOT FHWA cooperative agreement DTFH61-11-H-00025 as one of the four test data sets acquired by the USDOT Data Capture and Management program.The freeway data consists of two months of data (Sept 15 2011 through Nov 15 2011) from dual-loop detectors deployed in the main line and on-ramps of a Portland-area freeway. The section of I-205 NB covered by this test data set is 10.09 miles long and the section of I-205 SB covered by this test data set is 12.01 miles long The data includes: flow, occupancy, and speed.

This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.

This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.

This dataset contains the up-to-date metadata on Work Zone feeds that meet the Work Zone Data Exchange (WZDx) specifications and is registered with USDOT ITS DataHub. The current work zone data from each feed can be accessed through their respective API links. Some links provide direct access, while others require a user to create their own API access key first. Please see the attached API Key Instructions document to learn how to sign up for API keys for the requisite feeds. The ITS Work Zone Sandbox, contains an archive of work zone data collected from each feed at a rate of at least every 15 minutes. This is not intended as a replacement for the work zone feeds and in many cases does not update as frequently as the feed does.