In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Context

The UK government amassed traffic data from 2000 and 2016, recording over 1.6 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It's a huge picture of a country undergoing change.

Note that all the contained accident data comes from police reports, so this data does not include minor incidents.

Content

ukTrafficAADF.csv tracks how much traffic there was on all major roads in the given time period (2000 through 2016). AADT, the core statistic included in this file, stands for "Average Annual Daily Flow", and is a measure of how activity a road segment based on how many vehicle trips traverse it. The AADT page on Wikipedia is a good reference on the subject.

Accidents data is split across three CSV files: accidents_2005_to_2007.csv, accidents_2009_to_2011.csv, and accidents_2012_to_2014.csv. These three files together constitute 1.6 million traffic accidents. The total time period is 2005 through 2014, but 2008 is missing.

A data dictionary for the raw dataset at large is available from the UK Department of Transport website here. For descriptions of individual columns, see the column metadata.

Acknowledgements

The license for this dataset is the Open Givernment Licence used by all data on data.gov.uk (here). The raw datasets are available from the UK Department of Transport website here.

Inspiration

• How has changing traffic flow impacted accidents?
• Can we predict accident rates over time? What might improve accident rates?
• Plot interactive maps of changing trends, e.g. How has London has changed for cyclists? Busiest roads in the nation?
• Which areas never change and why? Identify infrastructure needs, failings and successes.
• How have Rural and Urban areas differed (see RoadCategory)? How about the differences between England, Scotland, and Wales?
• The UK government also like to look at miles driven. You can do this by multiplying the AADF by the corresponding length of road (link length) and by the number of days in the years. What does this tell you about UK roads?

Digital technology and Internet use, website traffic strategies, by North American Industry Classification System (NAICS) and size of enterprise for Canada from 2012 to 2013.

The City of Pasadena has a longstanding interest in protecting neighborhoods from cut-through traffic and speeding vehicles. As early as the 1980’s, the City authorized installation of speed humps to slow traffic in residential areas. Today, almost 400 of these traffic management devices have been installed along with many other traffic management measures.Traffic counts are conducted throughout the City of Pasadena either through resident requests, development projects, specific and general plans, or engineering studies. The Department of Transportation has collected these traffic counts and made them available to the public through the use of a Traffic Count Database.

A report of website traffic broken down by country of origin

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

This traffic-count data is provided by the City of Pittsburgh's Department of Mobility & Infrastructure (DOMI). Counters were deployed as part of traffic studies, including intersection studies, and studies covering where or whether to install speed humps. In some cases, data may have been collected by the Southwestern Pennsylvania Commission (SPC) or BikePGH. Data is currently available for only the most-recent count at each location. Traffic count data is important to the process for deciding where to install speed humps. According to DOMI, they may only be legally installed on streets where traffic counts fall below a minimum threshhold. Residents can request an evaluation of their street as part of DOMI's Neighborhood Traffic Calming Program. The City has also shared data on the impact of the Neighborhood Traffic Calming Program in reducing speeds. Different studies may collect different data. Speed hump studies capture counts and speeds. SPC and BikePGH conduct counts of cyclists. Intersection studies included in this dataset may not include traffic counts, but reports of individual studies may be requested from the City. Despite the lack of count data, intersection studies are included to facilitate data requests. Data captured by different types of counting devices are included in this data. StatTrak counters are in use by the City, and capture data on counts and speeds. More information about these devices may be found on the company's website. Data includes traffic counts and average speeds, and may also include separate counts of bicycles. Tubes are deployed by both SPC and BikePGH and used to count cyclists. SPC may also deploy video counters to collect data. NOTE: The data in this dataset has not updated since 2021 because of a broken data feed. We're working to fix it.

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.

Comprehensive dataset analyzing Walmart.com's daily website traffic, including 16.7 million daily visits, device distribution, geographic patterns, and competitive benchmarking data.

A dataset to show the 2018 traffic count from 62 recorders located around Bradford. Not all locations have 100% of the data for 2018. The % of the data is identified in the dataset.

Across popular online marketplace websites visited by users in Australia in February 2025, temu.com registered the highest growth in its website traffic compared to the previous year, with an annual growth of over ** percent. In comparison, ebay.com.au saw a decrease in its website traffic compared to the previous year, with an annual decrease of around **** percent.

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

A time-series quantitative dataset designed to track and document the total traffic and visits directed to the official website on a monthly basis throughout the entire year of 2025

Feature layer containing authoritative traffic count segments 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.

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

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.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

Recorded volume data at SCATS intersections or pedestrian crossings in Hamilton. To get data for this dataset, please call the API directly talking to the HCC Data Warehouse: https://api.hcc.govt.nz/OpenData/get_traffic_signal_detector_count?Page=1&Start_Date=2020-10-01&End_Date=2020-10-02. For this API, there are three mandatory parameters: Page, Start_Date, End_Date. Sample values for these parameters are in the link above. When calling the API for the first time, please always start with Page 1. Then from the returned JSON, you can see more information such as the total page count and page size. For help on using the API in your preferred data analysis software, please contact dale.townsend@hcc.govt.nz. NOTE: Anomalies and missing data may be present in the dataset.

Column_InfoSite_Number, int : SCATS ID - Unique identifierDetector_Number, int : Detector number that the count is recorded toDate, datetime : Start of the 15 minute time interval that the count was recorded forCount, int : Number of vehicles that passed over the detector

Relationship

This table reference to table Traffic_Signal_Detector

Analytics

For convenience Hamilton City Council has also built a Quick Analytics Dashboard over this dataset that you can access here.

Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Each point holds the measured average daily traffic volume for that site during the most recent survey. The site type attribute defines whether a classifier was used for the traffic count.Historical count data is available on the ADT website located at https://apps.co.marion.or.us/adt/