Displays vehicle traffic volumes for arterial streets in Seattle based on spot studies that have been adjusted for seasonal variation. Data is a one time snapshot for 2022 and is maintained by Seattle Department of Transportation.Contact: Traffic OperationsRefresh Cycle: None, Snapshot for 2022 Only.

SDCC Traffic Congestion Saturation Flow Data 01 April to 19 September 2024. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions.

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 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. Historical traffic is based on the average of observed speeds over the past three years. 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.

General information: The data set includes traffic data from all locations in Hamburg where motor vehicle traffic (vehicle traffic) is recorded using infrared detectors 24 hours a day and every day of the year. The data contain real-time traffic volumes and are made available at counting points summarized for the road cross-section at 15-minute, 60-minute, daily and weekly intervals. The data from the counting points is also visualized in the corresponding geoportals of the FHH, e.g. Geo-Online and Verkehrsportal.

In addition to the real-time data, historical data is also available to the following extent: all data for the last two weeks in 15 -minute intervals, all data for the last two months for the 60-minute intervals, all data for the current and last year in daily intervals, all data since the beginning of the collection in weekly intervals.

Technical information: The infrared detectors are usually installed on traffic lights, but to a lesser extent also on other masts. The detectors record and count the traffic via the heat radiation of the individual road users. Since only infrared images are evaluated, data protection is guaranteed at all times.

Notes on data quality: The data is transmitted in real time to the FHH Urban Data Platform. In this way, they are available promptly to all users and interested parties. Due to the real-time component, however, various framework conditions must be observed: The data is not comprehensively quality-assured. Unusual deviations from the expected data and data gaps are automatically recognized by the system, but cannot be corrected in real time. Gaps that occur, e.g. due to a break in data transmission, can be supplied later. Under certain circumstances and in the case of longer failures, changes in the historical data can still occur after a few days.

The data is therefore updated daily for the following periods: Previous day: 15-minute intervals Six days ago : 15 min intervals and daily intervals day 28 days ago: daily intervals

The weekly values ​​are updated weekly for the values ​​of the previous week and the week four weeks ago.

The data published here is not officially verified data from the FHH. If such data is required, e.g. the data set "Traffic volume Hamburg" can be used. be used, which contains the "average (work)day traffic" in the development of the last few years. As with any traffic count, whether automated or manual, there are certain tolerances in the measurement accuracy. The requirements for the system used here are accuracies of +/- 5% when recording vehicle traffic volumes.

Further information about the real-time service:

The real-time data service contains the locations of the counting points for the Vehicle volume recorded with infrared detectors. The data is provided in JSON format via the SensorThings API (STA). For each counting place in the SensorThings API (STA), an object was created in the "Thing" entity. created. There is an object in the "Datastreams" entity for each temporal resolution level at the counting points or each traffic reference value. The real-time data on the number of vehicles per counting point and time interval is stored in the STA in the "Observations" entity. published.

The following spatial and temporal levels are differentiated:

-Counting point 15 minutes, 1 hour, 1 day, 1 week: number of vehicles

In the Datastreams entity, there is a JSON object under the "key" "properties" more "key-value-pairs". Based on the service and layer structure in GIS, we have defined service and layer as additional "key-value pairs" introduced under the JSON object properties. Here is an example:

{ "properties":{ "serviceName": "HHSTAAutomated traffic volume acquisition", "layerName": "NumberKfz< /em>Zaehlstelle_15-Min", "key":"value"} }

Due to unforeseen circumstances, this service is temporarily out of order. We are sorry for the trouble this may cause you. We are working hard to restore the service as soon as possible, but we cannot provide an estimated time of resolution at the moment.The API streams traffic data derived by the Main Roads SCATS software in real-time at signalised intersections on the Western Australian road network.

Average weekday traffic counts (AWT) are collected at count stations throughout the city and represent the daily average for Monday-Friday traffic volume. Count stations in the eastern section of the city are collected in even numbered years, while those in the city's western section are collected in odd numbered years. Field descriptions/definitions for traffic count data as follows: ObjectID: GIS auto-generated unique identifiermslink: mslink of street segmentsegment_na: street segment nameSTATION: count station numberSOURCE: designates segment with counter or linkedSTATION: volume count station numberSOURCE: volume count station or linked segmentAWT_Count: most recent average weekday traffic countAWT_Yr: year of most recent countShape: GIS geometry typeYear_Txt: year of most recent count (text field)Shape.STLength(): GIS calculated segment length

Displays vehicle traffic volumes for arterial streets in Seattle based on spot studies that have been adjusted for seasonal variation. Data is a one time snapshot for 2021 and is maintained by Seattle Department of Transportation.Contact: Traffic OperationsRefresh Cycle: None, Snapshot for 2021 Only.

This contains data maintained by SDOT that is related to the study of traffic patterns in the city. The volumes in the data represent the Average Annual Weekday Traffic (AAWDT) (5-day, 24-hour) for that section of roadway. | Attribute Information: https://www.seattle.gov/Documents/Departments/SDOT/GIS/2012_Traffic_Flow_Counts_OD.pdf | Update Cycle: As Needed | Contact Email: DOT_IT_GIS@seattle.gov

January 2024, Netflix.com generated over 412 million visits in the United States. Traffic to the SVoD platform increased by seven percent compared to the previous month. Overall, Netflix was the leading subscription video-on-demand service in terms of traffic during the examined period. Between the second half of 2022 and the beginning of 2023, search and visit volume trends on streaming sites in the market appeared to have normalized after the usage increase brought by the COVID-19 pandemic in 2020 and 2021.

Displays vehicle traffic volumes for arterial streets in Seattle based on spot studies that have been adjusted for seasonal variation. Data is a one time snapshot for 2019 and is maintained by Seattle Department of Transportation.Contact: Traffic OperationsRefresh Cycle: None, Snapshot for 2019 Only.

Linear network representing the estimated traffic flows for roads and highways managed by the Ministry of Transport and Sustainable Mobility (MTMD). These flows are obtained using a statistical estimation method applied to data from more than 4,500 collection sites spread over the main roads of Quebec. It includes DJMA (annual average daily flow), DJME (summer average daily flow), DJME (summer average daily flow (June, July, August, September) and DJMH (average daily winter flow (December, January, February, March) as well as other traffic data. It is important to note that these values are calculated for total traffic directions. Interactive map: Some files are accessible by querying a section of traffic à la carte with a click (the file links are displayed in the descriptive table that is displayed when clicking): • Historical aggregated data (PDF) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel) • Annual reports for permanent sites (PDF and Excel) • Hourly data (hourly average per weekday per month) (Excel)**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

In the ACT, we have bluetooth detectors placed in certain roads to monitor traffic flow that provides network-wide performance indicators in real time. Details about congestion & travel time can be accessed via APIs provided in this dataset

Traffic data collected by Attiki Odos from 600 Vehicle Detection Sensors (VDS). Data include VDS id and status, Occupancy in %, Traffic Flow (veh/hour) and Speed (km/hour). For more details on VDS and Traffic metadata see the corresponding resources.

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

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.

Displays traffic study flow count data maintained by Seattle Department of Transportation.Users can utilize following definition query for traffic count study data for a particular year. Note-ENTER YEAR is the particular year of interest.Definition Query: STDY_YEAR=ENTER YEAR AND FLOWMAP = 'Y'Refresh: Weekly

Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

LSA process data - beta version The data set includes LSA process data for a large number of nodes in Hamburg and contains current signal characteristics in real time. In addition, data is transmitted to detectors such as bicycle, pedestrian and vehicle requests as well as bus reports. This version is a BETA version. The following points should be considered when using the data:

1. The system is still in test operation and is being evaluated, so it can: a. Unforeseeable failures in data transmission and b. There are dropouts in the data update c. 2. A final quality assurance has not yet taken place. a. Data can be incorrect. This can particularly affect the location. b. The description of the entities is still partially incomplete c. possible inconsistency of the time stamps in the observations, such as resultTime (time stamp of the TLD system) VOR phenomenonTime (time stamp of the signal from the traffic signal system).

For a better understanding of the data, is in the area References and downloads are linked in a user manual.

Further information on the real-time service:

The OGC SensorThings API-compliant real-time data service contains data streams and positions of lane relationships at intersections with traffic lights for cyclists, pedestrians and motor vehicles in Hamburg urban area. If provided at the traffic signal system, the following data streams are delivered as JSON objects: primary signals, secondary signals, auxiliary signals, acoustic signals, car signal requests, bicycle signal requests, pedestrian signal requests, acoustic signal requests, public transport pre-registration, public transport registration, public transport de-registration, signal program and wave second. In the OGC SensorThings API, the information on the lane relationships is stored in the Thing entity. For the data streams listed above that are available on a specific thing, an entry is created in the Datastreams entity that references the corresponding thing.

All times are given in Coordinated Universal Time (UTC).</ p>

In the Datastreams entity, in the JSON object under the "key" "properties" more "key-value-pairs". Based on the service and layer structure in GIS, we have defined service and layer as additional "key-value pairs" introduced under the JSON object properties.

Here is an example:

{ "properties": { "serviceName": "HHSTAtraffic lights", "layerName": "primaysignal", "key":"value" } }

All possible values ​​for “layerName”: * primaysignal (primary signal), * secondäarysignal (secondary signal), * auxiliarysignal (auxiliary signal), * acoustic signal (acoustic signal), * detectorcar (car signal request), * detectorcyclist (cyclist signal request), * detectorpedestrian (pedestrian signal request), * detectoracoustic< em>trafficrequest (acoustic signal request), * buspre-requestpoint (public transport pre-registration), * busrequestpoint (public transport registration), * bus checkout (public transport checkout), * signalprogram (signal program number), * cycle_second (wave second)

Using these "key-value pairs" filters can then be defined for the REST request, e.g. https://tld.iot.hamburg.de/v1.1/Datastreams?$filter=properties/serviceName eq 'HHSTAtrafficlights' and properties/layerName eq 'primarysignal'

The real-time data can also be obtained via an MQTT broker. The IDs required for this can be obtained via a REST request and then used to subscribe to a data stream:

MQTT broker: tld.iot.hamburg.de Topic: v1.1/Datastreams({ id})/Observations

Furthermore, the MAP files (.xml and .kml) of all published nodes can be accessed via the following link: https://daten-hamburg.de/tlf_public/</ p>

Traffic Flow Data In Ho Chi Minh City, Viet Nam
This dataset falls under the category Traffic Generating Parameters.
It contains the following data: Traffic flow
This dataset was scouted on 2022-02-10 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.kaggle.com/thanhnguyen2612/traffic-flow-data-in-ho-chi-minh-city-viet-nam

This dataset contains the Department of Transport and Main Roads road location details (both spatial and through distance) as well as associated traffic data.

It allows users to locate themselves with respect to road section number and through distance using the spatial coordinates on the state-controlled road network.

Through distance – the distance in kilometres measured from the gazetted start point of the road section.

Note: "Road location and traffic data" resource has been updated as of July 2023.