NOTE: The Historic Traffic Data Dashboard & Feature Hosted Service have been retired.Network operations traffic data from Main Roads Western Australia for 2015 to 2019. The data provided includes data collected on the Perth Metropolitan State Road Network (PMSRN) at 15 minute intervals. The Historic Traffic Data is provided in CSV format per year. Each table has over 34 million rows and can be linked to the M-Links Road Network using the M-Links ID. A data dictionary for M-Links Road Network and the Historic Traffic Data is at the following link:https://bit.ly/2S86uSnNetwork Operations traffic data can also be accessed via the Daily Traffic Data API at the following link: https://bit.ly/34ZsyAK The network operations traffic data provided here is of variable quality and has not been checked, quality assured or manually corrected. An automated process is used to patch over missing or suspect data with the most representative data available within the database. Patches may be reapplied as new data becomes available and patched data may change over time. Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes. Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- “The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.”

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

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

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.

Historical Annual Average Daily Traffic Volume datasets (2001 -> 2019) provides annual average daily traffic (AADT) volume data for all vehicles and heavy vehicles in each direction on each road segment on the Victoria declared road network. The data is either estimated through data processes or actual volumes from data collection.

The Wisconsin Traffic Counts dataset combines traffic count data with GIS mapping technology to display data in a tabular format, on a map, or both. Traffic_Count_AADT is a source of Wisconsin DOT traffic data information for road sections of the State Highways or select Local Federal-Aid roads. Traffic counts are reported as the number of vehicles expected to pass a given location on an average day of the year. This value is called the "annual average daily traffic" or AADT. The AADT is produced for either continuous count sites or short duration count sites. WisDOT collects continuous count data from about 320 permanent data collection locations primarily located on the State Trunk Highway System. Data at continuous count sites are scheduled to be collected in hourly intervals each day of the year. A short duration traffic count usually collects hourly intervals for a 48-hour period, taken at the specific locations throughout the state. Using continuous count data, short duration counts are then adjusted for the variation in traffic volume that occurs throughout the year. Short duration counts are collected over three, six, or ten-year cycles at more than 26,000 rural and urban locations throughout the state. In addition to Wisconsin DOT use for transportation management purposes, Wisconsin DOT is required to collect and report these statistics to the Federal Highway Administration monthly and annually.Related Dataset: Traffic Counts

This dataset contains the historical estimated congestion for the 29 traffic regions, starting in approximately March 2018. Older records are in https://data.cityofchicago.org/d/emtn-qqdi. The most recent estimates for each segment are in https://data.cityofchicago.org/d/t2qc-9pjd. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions. Current estimates of traffic congestion by region are available at http://bit.ly/103beCf.

This collection of datasets contains all the estimations generated by the traffic flow model of the city of Santiago de Compostela. Each dataset of the collection contains the estimations generated during a specific month. Each record contains a reference to the identifier of a segment of the main street and road network of the city, the time instant corresponding to the estimation and the estimated value for the traffic flow intensity (number of vehicles per hour).

Abstract;The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations.

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• Data Set Information: The task for this dataset is to forecast the spatio-temporal traffic volume based on the historical traffic volume and other features in neighboring locations. Specifically, the traffic volume is measured every 15 minutes at 36 sensor locations along two major highways in Northern Virginia/Washington D.C. capital region. The 47 features include: 1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), 2) week day (7 features), 3) hour of day (24 features), 4) road direction (4 features), 5) number of lanes (1 feature), and 6) name of the road (1 feature). The goal is to predict the traffic volume 15 minutes into the future for all sensor locations. With a given road network, we know the spatial connectivity between sensor locations.

https://www.capitalregionusa.org/info/capital-region-transportation-guide

• Attribute Information: The 47 features include: (1) the historical sequence of traffic volume sensed during the 10 most recent sample points (10 features), (2) week day (7 features), (3) hour of day (24 features), (4) road direction (4 features), (5) number of lanes (1 feature), and (6) name of the road (1 feature).

Relevant Task Example; https://keras.io/examples/timeseries/timeseries_traffic_forecasting/

This is the City of Fairfield, California's Traffic Count Data.

The CTDOT Historical Traffic Monitoring GIS Data is a curated geospatial dataset integrated into the agency’s enterprise platforms, including the TED Portal and Open Data Hub. It supports transportation planning and safety analysis by providing historical traffic volumes and patterns across Connecticut’s roadway network. Managed by the Enterprise GIS Unit, the data is structured for internal use via ArcGIS Enterprise and selectively published to the public-facing Open Data Portal. It is actively maintained and synchronized through automated replication processes, although recent updates indicate occasional export issues requiring manual intervention. The dataset is also referenced in strategic planning tools and dashboards, and plays a key role in initiatives like the Highway Safety Road Map and the SMART grant application for connected infrastructure.

This dataset provides access to the historical values of the traffic flow model. This dataset was created as part of the TRAFAIR project - https://trafair.eu/

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.

The historical traffic API provides historical data on NSW incidents for the last three months.

Live Traffic NSW allows you to search for a particular date and location.

Please note: If you do not receive a response on your first attempt at retrieving data, try again a few minutes later. The Historical Data Search system may be temporarily idle.

elcome to the cutting-edge world of traffic prediction! This Kaggle dataset is a goldmine for data enthusiasts, machine learning aficionados, and urban planning visionaries. In this treasure trove of information, you'll find a comprehensive collection of real-world traffic data meticulously curated for your analytical prowess.

Unravel the complexities of urban mobility as you delve into diverse parameters such as historical traffic patterns, weather conditions, special events, and more. Whether you're a seasoned data scientist or a curious novice, this dataset offers a unique opportunity to fine-tune your predictive models and contribute to the future of transportation efficiency.

Harness the power of data to foresee traffic bottlenecks, optimize city planning, and revolutionize the way we navigate our urban landscapes. Join the community of innovators working towards a seamlessly connected and intelligently managed transportation ecosystem. The road to the future starts here—take the wheel and drive progress with the Traffic Prediction Dataset on Kaggle!

2021 Annual Average Daily Traffic figures for state highway traffic count locations. Data represents all directions of travel for the given location.

🇺🇸 미국 English 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 changes

AADT represents current (most recent) Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locations Active feature class as of the annual HPMS freeze in January. Historical AADT is found in another table. Please note that updates to this dataset are on an annual basis, therefore the data may not match ground conditions or may not be available for new roadways. Resource Contact: Christy Prentice, Traffic Forecasting & Analysis (TFA), http://www.dot.state.mn.us/tda/contacts.html#TFA

Check other metadata records in this package for more information on Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

Annual Average Daily Traffic Locations in Minnesota: Annual Average Daily Traffic Locations

This dataset contains the historical estimated congestion for over 1,000 traffic segments, starting 6/11/2024 (except for a single time slice on 3/8/2024). Older records are in https://data.cityofchicago.org/d/sxs8-h27x. The most recent estimates for each segment are in https://data.cityofchicago.org/d/n4j6-wkkf. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.