This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated. Data are offered from 30-03.2020 to 9-08-2020. There is another set of data in this portal with the historical series: Traffic. History of traffic data since 2013 In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

This web map displays traffic count data provided by the City of Tempe Transportation Department. Data are symbolized by line thickness per each street section.Each segment's popup contains a weblink to historical traffic count data that are provided by the City of Tempe for public use.

This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The color coded map leverages historical, real time and predictive traffic data. Historical traffic is based on the average of observed speeds over the past three years. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Traffic Light Map Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.

What Makes Our Data Unique? What sets our Traffic Light Map Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.

How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.

Primary Use-Cases and Verticals The Traffic Light Map Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:

Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.

Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.

Integration with Our Broader Data Offering The Traffic Light Map Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.

In summary, Driver Technologies' Traffic Light Map Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Traffic Light Map Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

This map features near real-time traffic information for different countries in Africa, designed for a night time display. This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. The real-time and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The color coded map leverages historical, real time and predictive traffic data. Historical traffic is based on the average of observed speeds over the past three years. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows: Green (fast): 85 - 100% of free flow speeds Yellow (moderate): 65 - 85% Orange (slow); 45 - 65% Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Feature layer containing authoritative traffic count points for Sioux Falls, South Dakota.The traffic counts listed are 24-hour, weekday, two-directional counts. Traffic counts are normally collected during the summer months, but may be taken any season, as weather permits. The traffic counts are factored by the day of the week as well as by the month of the year to become an Average Annual Daily Total (AADT). Traffic volumes (i.e. count data) can fluctuate depending on the month, week, day of collection; the weather, type of road surface, nearby construction, etc. All of the historical data should be averaged to reflect the "normal" traffic count. More specific count data (time, date, hourly volume) can be obtained from the Sioux Falls Engineering Division at 367-8601.

Irys specializes in collecting and curating high-quality geolocation signals from millions of connected devices across the globe. Our real-time and historical foot traffic data, categorized under Map Data, is sourced through partnerships with tier-1 mobile applications and app developers. The advanced aggregated location data covers the entire world, providing valuable insights for diverse use-cases related to Transport and Logistic Data, Mobile Location Data, Mobility Data, and IP Address Data.

Our commitment to privacy compliance is paramount. We ensure that all data is collected in accordance with privacy regulations, accompanied by clear and compliant privacy notices. Our opt-in/out management allows for transparent control over data collection, use, and distribution to third parties.

Discover the power of foot traffic data with Irys – where precision meets privacy.

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.

This web map displays traffic count data provided by the City of Tempe Transportation Department. Data are symbolized by line thickness per each street section.Each segment's popup contains a weblink to historical traffic count data that are provided by the City of Tempe for public use. The historical data with this map extends from 2016 to present day.

2017 estimated Annual Average Daily Traffic volumes along the state highway system. Data represents all directions of travel for the given roadway segment.

Analysis of ‘Traffic Count Segments’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d81619ba-78d6-4252-a540-b647adaf367a on 11 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.

Historical data from this feature layer extends from 2016 to present day.

Contact: Sue Taaffe

Contact E-Mail: sue_taaffe@tempe.gov

Contact Phone: 480-350-8663

Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-counts

Link to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/

Data Source: SQL Server/ArcGIS Server

Data Source Type: Geospatial

Preparation Method: N/A

Publish Frequency: As information changes

Publish Method: Automatic

Data Dictionary

--- Original source retains full ownership of the source dataset ---

Traffic-related data collected by the Boston Transportation Department, as well as other City departments and State agencies. Various types of counts: Turning Movement Counts, Automated Traffic Recordings, Pedestrian Counts, Delay Studies, and Gap Studies.

~_Turning Movement Counts (TMC)_ present the number of motor vehicles, pedestrians, and cyclists passing through the particular intersection. Specific movements and crossings are recorded for all street approaches involved with the intersection. This data is used in traffic signal retiming programs and for signal requests. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Automated Traffic Recordings (ATR)_ record the volume of motor vehicles traveling along a particular road, measures of travel speeds, and approximations of the class of the vehicles (motorcycle, 2-axle, large box truck, bus, etc). This type of count is conducted only along a street link/corridor, to gather data between two intersections or points of interest. This data is used in travel studies, as well as to review concerns about street use, speeding, and capacity. Counts are typically conducted for 12- & 24-Hr periods.

~_Pedestrian Counts (PED)_ record the volume of individual persons crossing a given street, whether at an existing intersection or a mid-block crossing. This data is used to review concerns about crossing safety, as well as for access analysis for points of interest. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Delay Studies (DEL)_ measure the delay experienced by motor vehicles due to the effects of congestion. Counts are typically conducted for a 1-Hr period at a given intersection or point of intersecting vehicular traffic.

~_Gap Studies (GAP)_ record the number of gaps which are typically present between groups of vehicles traveling through an intersection or past a point on a street. This data is used to assess opportunities for pedestrians to cross the street and for analyses on vehicular “platooning”. Counts are typically conducted for a specific 1-Hr period at a single point of crossing.

Intensity information (vehicles/hour) represented by ranges and with the following colour code: The KML files are updated almost in real time, with a periodicity of 5 minutes. There are two other parameters that indicate the degree of traffic flow such as service level and load. Service level is also a qualitative and immediacy parameter, calculated from speed and occupancy. It is offered in the real-time traffic dataset with 4 levels: fluid, slow, withholding and congestion. It can also be viewed in the Informo portal with color coding. On the other hand, the load parameter is calculated from the intensity (vehicles/hour) and occupation. It can be found in the traffic data historical set. There are other related datasets in the Open Data Portal that offer the aforementioned parameters such as: Traffic. Real-time traffic data Traffic. Location of traffic measurement points. History of traffic data since 2013 In the section “ Associated documentation ” an explanatory document is provided in order to be able to correctly interpret the various fields of the dataset. Recommendations for use and additional information are also included to make them easier to understand. This information can be viewed, along with the rest of the traffic information of the city of Madrid, on the Informo portal: https://informo.madrid.es

Lines that represent historic traffic closures. This dataset includes closures pertaining to trails, sidewalks, and City of Waterloo roads. Note the service that contains this data is only refreshed periodically but the content and data representing closures is updated daily at 7am, 1pm and 5pm.

2015 Annual Average Daily Traffic figures and total truck percentages for state highway traffic count locations monitored by WSDOT's Transportation Data and GIS Office. Figures represent all directions of travel at the given location.

Historical data of traffic measurement points. Each month the data of the previous month are incorporated. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

Statewide Traffic Volume Historic and Forecast Historic traffic volume observations, future traffic volume forecasts, and adjustment factors -- are summarized using Utah's roadway planning summary segments -- for the Wasatch Front Regional Council metropolitan planning organization travel model area. This dataset can be viewed in an interactive map at: https://wfrc.org/traffic-volume-map/. This dataset provides segment level traffic volume data (historic estimates and future forecasts) within the state of Utah. Wasatch Front Regional Council (WFRC) metropolitan planning organization's travel model boundaries (including Salt Lake, Davis, western Weber, and southern Box Elder counties' urbanized areas). Future forecasts have been developed with the support of the Wasatch Front Travel Demand Model (v8.3.1) in conjunction with the adopted 2019 Regional Transportation Plan (RTP). This dataset was first released May 5th, 2020 (check the RELEASE field/column attribute for most recent update date). MAG travel model boundaries include the urbanized areas of Utah County. Cache travel model boundaries include Cache County. Dixie travel model boundaries include Washington County. Also contained within this dataset are adjustment factors, developed as part of a statewide effort led by UDOT, that can be used to scale the Average Annual Daily Traffic (AADT) volumes estimates and forecasts to provide more time-period specific volumes for a time period subsets (e.g. weekdays, weekends, specific months, seasons, maximum month, etc). Contact and additional information is available from https://wfrc.org/models-and-forecasting or through email contact to analytics@wfrc.org.UPDATE 12/14/2020: USTM segments updated with interim-year forecasts for non-MPO areas of state. Field names and descriptions are as follows: RELEASE (version of dataset) SEGID (Segment ID, combination of Route_ID and BMP) ROUTE_ID (Route Identification, <1000 for Interstate/State Routes, >1,000 for Federal Aid Routes) BMP (Begin Milepost, or milepost of beginning of segment) EMP (End Milepost, or milepost of ending of segment) FULLNAME (name of segment) CO_FIPS (County Federal Information Processing Standard, unique code for each county) CO_NAME (Name of county) X (Centroid of Segment, UTM Zone 12N) Y (Y Centroid of Segment, UTM Zone 12N) DISTANCE (length of segment in miles) F2050...F2019 (forecast AADT volumes for model years per 2019 RTP) CH17TO50...CH17TO19 (change in AADT volumes between model years) FNOTES (forecast notes, typically when drop or large increase in volumes) MOREINFO (url to more general information on models and forecasts) WFRC_FLG (flag value used internally by WFRC) AADT2017...AADT1981 (AADT estimates for a given year from UDOT) SUTRK2017 (Single-Unit, Box Type Truck percentage for 2017) CUTRK2017 (Combo-Unit, Semi Type Truck percentage for 2017) DOWFACFC (Day-of-Week Factor Functional Class) DOWFACAT (Day-of-Week Factor Area Type) FAC_MON...FAC_SUN (Day-of-Week factors for given days) FAC_WDAVG (Average Weekday Factor Monday-Thursday, multiply AADT by factor to get AWDT, divide AWDT by factor to get AADT) FAC_WEAVG (Average Weekend Factor Friday-Sunday) FAC_WEMAX (Max Weekend Factor Friday-Sunday) SSNGRP (Seasonal Factor Group) SSNVOLCLS (Seasonal Factor Volume Class) SSNATGROUP (Seasonal Factor Area Type Group) FAC_JAN...FAC_DEC (Month Factors, multiply AADT or AWDT get month ADT or AWDT) FAC_WIN (Winter Factor, December-February) FAC_SPR (Spring Factor, March-May) FAC_SUM (Summer Factor, June-August) FAC_FAL (Fall Factor, September-November) FAC_MAXMO (Month in which Maximum Month Factor is found) FAC_MAX (Maximum Month Factor)