This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway 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 ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the 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). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for 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.

HCAADT is the theoretical estimate of the total number of heavy commercial vehicles using a specific segment of roadway (in both directions) on any given day of the year. This estimate represents the total number of heavy commercial vehicles per year divided by 365 and is developed using factors to adjust for season on sampled road systems in a particular given year. This information is displayed using the approximate collection site for each unique estimation.

Notice: Starting with the 2017 dataset, HCAADT no longer includes historical years as attribute fields within the GIS files. Instead, the last 30 years of available historical HCAADT data are provided in a zipped Access database. These changes were made to automate and accelerate the annual distribution of HCAADT data products. Download the historic data here: ftp://ftp2.dot.state.mn.us/pub/outbound/TDA/Traffic%20Monitoring/Products/2017_Public_Files_HCAADT/

With more than 44,000 Portable Traffic Count (PTC) Stations located throughout North Carolina, Traffic Survey has adopted a collection schedule. Please see our website: https://www.ncdot.gov/projects/trafficsurvey/for further details. The data in this file was digitized referencing the available NCDOT Linear Referencing System (LRS) and is not the result of using GPS equipment in the field, nor latitude and longitude coordinates. The referencing provided is based on the 2015 Quarter 1 publication of the NCDOT Linear Referencing System (LRS). Some differences will be found when using different quarterly publications with this data set. The data provided is seasonally factored to an estimate of an annual average of daily traffic. The statistics provided are: CVRG_VLM_I: Traffic Survey's seven digit unique station identifier COUNTY: County NameROUTE: Numbered route identifier, or local name if not State maintainedLOCATION: Description of the Annual Average Daily Traffic station location AADT_2015: Estimated Annual Average Daily Traffic in vehicles per day for 2015AADT_2014: Estimated Annual Average Daily Traffic in vehicles per day for 2014AADT_2013: Estimated Annual Average Daily Traffic in vehicles per day for 2013 AADT_2012: Estimated Annual Average Daily Traffic in vehicles per day for 2012 AADT_2011: Estimated Annual Average Daily Traffic in vehicles per day for 2011 AADT_2010: Estimated Annual Average Daily Traffic in vehicles per day for 2010 AADT_2009: Estimated Annual Average Daily Traffic in vehicles per day for 2009 AADT_2008: Estimated Annual Average Daily Traffic in vehicles per day for 2008 AADT_2007: Estimated Annual Average Daily Traffic in vehicles per day for 2007 AADT_2006: Estimated Annual Average Daily Traffic in vehicles per day for 2006 AADT_2005: Estimated Annual Average Daily Traffic in vehicles per day for 2005 AADT_2004: Estimated Annual Average Daily Traffic in vehicles per day for 2004 AADT_2003: Estimated Annual Average Daily Traffic in vehicles per day for 2003 AADT_2002: Estimated Annual Average Daily Traffic in vehicles per day for 2002 Note: A value of zero in the AADT field indicates no available AADT data for that year. Please note the following: Not ALL roads have PTC stations located on them. With the exception of Interstate, NC and US routes, NCDOT County Maps refer to roads using a four digit Secondary Road Number, not a road’s local name. If additional information is needed, or an issue with the data is identified, please contact the Traffic Survey Group at 919 814-5116. Disclaimer related to the spatial accuracy of this file: Data in this file was digitized referencing the available NCDOT GIS Data Layer, LRS Arcs Shapefile Format from Quarter 1 release and is not the result of using GPS equipment in the field.North Carolina Department of Transportation shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of data, and relative positional accuracy of the data. This data cannot be construed to be a legal document.

We provide a roads dataset that includes the spatial location of roads, the estimated age of each road, and the predicted traffic volume of each road between 1986 and 2020 in Wyoming, USA. Our annual estimates of traffic volume are available for each road and include estimates for all vehicles and truck only traffic. Moreover, we provide the estimated age of each road, where a minimum value of 1986 indicates that the road existed in 1986, and any later year indicates the most likely year that road was developed. This dataset will be beneficial for any research focused on the mechanistic effects of road traffic on wildlife populations. Our roads dataset is based on a comprehensive inventory of paved and unpaved roads in Wyoming of 2015 National Aerial Imagery Program (NAIP) aerial imagery (Fancher et al. 2023). We developed annual estimates of road age and vehicular traffic volume across 147,108 km of highways, arterials, collectors, local, and gravel/graded roads within the state of Wyoming. To assign road age, we leveraged a suite of ancillary data on surface disturbances (e.g., oil and gas drilling operations, wind turbines, and open pit mines) with known establishment dates. Then, we predicted traffic volume for each year across Wyoming using XGBoost, a novel machine learning method, to relate ongoing traffic monitoring by the Wyoming Department of Transportation with a separate set of spatial covariates hypothesized to explain traffic patterns across large regions.

calculator.net is ranked #375 in US with 56.35M Traffic. Categories: Wellness. Learn more about website traffic, market share, and more!

This dataset contains the current estimated congestion for the 29 traffic regions. For a detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab.

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.

The Highway Performance Monitoring System, managed by the Federal Highway Administration, provides essential data on average annual daily traffic across U.S. roadways, but it has limited representation of medium- and heavy-duty vehicles on non-interstate roads. This gap limits research and policy analysis on the impacts of truck traffic, especially concerning air quality and public health. To address this, we use random forest regression to estimate medium- and heavy-duty vehicle traffic volumes in areas with sparse data. This results in a more comprehensive dataset, which enables the estimation of traffic density at the census block level as a proxy for traffic-related air pollution exposure. Our high-resolution spatial data products, rigorously validated, provide a more accurate representation of truck traffic and its environmental and health impacts. These datasets are valuable for transportation planning, public health research, and policy decisions aimed at mitigating the effects o..., , , # Estimated roadway segment traffic data by vehicle class for the United States: A machine learning approach

Dataset DOI: 10.5061/dryad.gmsbcc2zz

Description of the Data and Available Formats

1. HPMS Network with Road Link Average Annual Daily Traffic (AADT):

Description: This dataset includes estimates for light-, medium-, and heavy-duty vehicle traffic across U.S. roadways. The data is derived from the 2018 Highway Performance Monitoring System (HPMS), managed by the Federal Highway Administration (FHWA). The HPMS provides essential information on average annual daily traffic (AADT), but it has limited representation of medium- and heavy-duty vehicles on non-interstate roads. To address this limitation, we applied random forest regression to estimate AADT for medium-duty vehicle (MDV) and heavy-duty vehicle (HDV) traffic in regions with sparse data. Light-duty vehicle (LDV) AADT was then estimated by subtracting the sum of MDV AADT and HDV AADT from the total AADT f...,

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

Traffic Density Estimation

## Overview

Traffic Density Estimation is a dataset for object detection tasks - it contains Car annotations for 923 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.

The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.

According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.

For more information, please visit MDOT Traffic Monitoring Program.

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.

In 2019, the Chinese marketplace Alibaba was the leading worldwide B2B e-commerce in terms of online traffic. The Alexa tool assessing the online traffic of websites put it on the top of the ranking, with a score of ***. The Russian Rosfirm and the U.S. platform Vinsuite followed in the ranking with a score of ***** and *****, respectively.

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.

Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan

Update Cycle: AADT & CAADT volumes are created and released every year.

Transportation Data Management System (TDMS) AADT Calculation Help

Traffic Monitoring Program

The most common way to quantify roadkill risk in different sections of infrastructures is to collect information on the location of casualties and then, model the probability using the environmental and infrastructure variables associated with the roadkill sites. This approach is not applicable in roads with low traffic intensity as they have a small number of victims (e.g. unpaved roads), where there is a high removal rate of casualties by scavengers (e.g. in natural areas), or when it has to be estimated before the infrastructure is built. We developed an indirect approach to evaluate the risk of collisions with wildlife within Doñana Natural Area (SW Spain), considering the abundance and phenology of species, the characteristics of the environment, and traffic intensity. First we characterized the road network, corresponding to 2190 km of roads (4.04 km/km2) of which only 2% were paved; and extracted environmental variables for the complete network in sections of 200m. Then, we characterized the traffic using data from automatic counting systems for main roads and for the rest we built a model of traffic intensity using data from a stratified sampling design in 62 sites using magnetometers, estimating traffic intensity to the whole network of roads. We characterized the abundance of multiple species using track censuses in 183 sites using 200 m transects; obtaining information on abundance, crossing intensity and the distance moved along the road (estimator of the time of exposure to vehicles or exposure). With this information we created a model of the number of crossing events per species in sections of 200 m using environmental predictors and applied the models to the whole network of roads. We estimated the roadkill risk using the index risk = log (no. crossings x traffic intensity x exposure), standardized between 0 and 1. We calculated the index for the whole network of roads. As an example, we show the predictions corresponding to the roadkill risk for several species, clearly identifying areas of high risk which are localized along roads with high traffic intensity and within them, specific sections with maximum risk. The predictions matched well with the observations of road-killed data recorded in the area.

The Annual Average Daily Traffic (AADT) is the estimated mean daily traffic volume and the Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles. For continuous sites, estimates are calculated by summing the Annual Average Days of the Week and dividing by seven. For short-count sites, estimates are made by factoring a short count using Seasonal and Axle (if applicable) day-of-week adjustment factors.Data Coverage: The dataset covers the entire Federal Aid System in the State of Michigan Update Cycle: AADT & CAADT volumes are created and released every year.Transportation Data Management System (TDMS) AADT Calculation HelpTraffic Monitoring Program

craigslist.org is ranked #72 in US with 130.96M Traffic. Categories: Online Services, Real Estate. Learn more about website traffic, market share, and more!

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2023. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.

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).**

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.

As of the second quarter of 2022, Shopee Philippines, an online department store and marketplace for retailers to sell their products, registered estimated monthly traffic of about ** million on its e-commerce website. Following by a considerable margin was Lazada, with an estimated online website traffic of roughly ** million visitors. Both companies lead the e-commerce market in the Philippines.