The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

As of December 2023, 5G data traffic in South Korea was in excess of 878 petabytes. As the user numbers of 5G increased, the traffic amount also rose accordingly.

Preliminary research efforts regarding Social Media Platforms and their contribution to website traffic in LAMs. Through the Similar Web API, the leading social networks (Facebook, Twitter, Youtube, Instagram, Reddit, Pinterest, LinkedIn) that drove traffic to each one of the 220 cases in our dataset were identified and analyzed in the first sheet. Aggregated results proved that Facebook platform was responsible for 46.1% of social traffic (second sheet).

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.

We present a dataset targeting a large set of popular pages (Alexa top-500), from probes from several ISPs networks, browsers software (Chrome, Firefox) and viewport combinations, for over 200,000 experiments realized in 2019.We purposely collect two distinct sets with two different tools, namely Web Page Test (WPT) and Web View (WV), varying a number of relevant parameters and conditions, for a total of 200K+ web sessions, roughly equally split among WV and WPT. Our dataset comprises variations in terms of geographical coverage, scale, diversity and representativeness (location, targets, protocol, browser, viewports, metrics).For Web Page Test, we used the online service www.webpagetest.org at different locations worldwide (Europe, Asia, USA) and private WPT instances in three locations in China (Beijing, Shanghai, Dongguan). The list of target URLs comprised the main pages and five random subpages from Alexa top-500 worldwide and China. We varied network conditions : native connections and 4G, FIOS, 3GFast, DSL, and custom shaping/loss conditions. The other elements in the configuration were fixed: Chrome browser on desktop with a fixed screen resolution, HTTP/2 protocol and IPv4.For Web View, we collected experiments from three machines located in France. We selected two versions of two browser families (Chrome 75/77, Firefox 63/68), two screen sizes (1920x1080, 1440x900), and employ different browser configurations (one half of the experiments activate the AdBlock plugin) from two different access technologies (fiber and ADSL). From a protocol standpoint, we used both IPv4 and IPv6, with HTTP/2 and QUIC, and performed repeated experiments with cached objects/DNS. Given the settings diversity, we restricted the number of websites to about 50 among the Alexa top-500 websites, to ensure statistical relevance of the collected samples for each page.The two archives IFIPNetworking2020_WebViewOrange.zip and IFIPNetworking2020_Webpagetest.zip correspond respectively to the Web View experiments and to the Web Page Test experiments.Each archive contains three files:- config.csv: Description of parameters and conditions for each run,- metrics.csv: Value of different metrics collected by the browser,- progressionCurves.csv: Progression curves of the bytes progress as seen by the network, from 0 to 10 seconds by steps of 100 milliseconds,- listUrl folder: Indexes the sets of urls.Regarding config.csv, the columns are: - index: Index for this set of conditions, - location: Location of the machine, - listUrl: List of urls, located in the folder listUrl - browserUsed: Internet browser and version - terminal: Desktop or Mobile - collectionEnvironment: Identification of the collection environment - networkConditionsTrafficShaping (WPT only): Whether native condition or traffic shaping (4G, FIOS, 3GFast, DSL, or custom Emulator conditions) - networkConditionsBandwidth (WPT only): Bandwidth of the network - networkConditionsDelay (WPT only): Delay in the network - networkConditions (WV only): network conditions - ipMode (WV only): requested L3 protocol, - requestedProtocol (WV only): requested L7 protocol - adBlocker (WV only): Whether adBlocker is used or not - winSize (WV only): Window sizeRegarding metrics.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - DOM Content Loaded Event End (ms): DOM time, - First Paint (ms) (WV only): First paint time, - Load Event End (ms): Page Load Time from W3C, - RUM Speed Index (ms) (WV only): RUM Speed Index, - Speed Index (ms) (WPT only): Speed Index, - Time for Full Visual Rendering (ms) (WV only): Time for Full Visual Rendering - Visible portion (%) (WV only): Visible portion, - Time to First Byte (ms) (WPT only): Time to First Byte, - Visually Complete (ms) (WPT only): Visually Complete used to compute the Speed Index, - aatf: aatf using ATF-chrome-plugin - bi_aatf: bi_aatf using ATF-chrome-plugin - bi_plt: bi_plt using ATF-chrome-plugin - dom: dom using ATF-chrome-plugin - ii_aatf: ii_aatf using ATF-chrome-plugin - ii_plt: ii_plt using ATF-chrome-plugin - last_css: last_css using ATF-chrome-plugin - last_img: last_img using ATF-chrome-plugin - last_js: last_js using ATF-chrome-plugin - nb_ress_css: nb_ress_css using ATF-chrome-plugin - nb_ress_img: nb_ress_img using ATF-chrome-plugin - nb_ress_js: nb_ress_js using ATF-chrome-plugin - num_origins: num_origins using ATF-chrome-plugin - num_ressources: num_ressources using ATF-chrome-plugin - oi_aatf: oi_aatf using ATF-chrome-plugin - oi_plt: oi_plt using ATF-chrome-plugin - plt: plt using ATF-chrome-pluginRegarding progressionCurves.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - url: Url of the current page. SUBPAGE stands for a path. - run: Current run (linked with index of the config for WPT) - filename: Filename of the pcap - fullname: Fullname of the pcap - har_size: Size of the HAR for this experiment, - pagedata_size: Size of the page data report - pcap_size: Size of the pcap - App Byte Index (ms): Application Byte Index as computed from the har file (in the browser) - bytesIn_APP: Total bytes in as seen in the browser, - bytesIn_NET: Total bytes in as seen in the network, - X_BI_net: Network Byte Index computed from the pcap file (in the network) - X_bin_0_for_B_completion to X_bin_99_for_B_completion: X_bin_k_for_B_completion is the bytes progress reached after k*100 millisecondsIf you use these datasets in your research, you can reference to the appropriate paper:@inproceedings{qoeNetworking2020, title={Revealing QoE of Web Users from Encrypted Network Traffic}, author={Huet, Alexis and Saverimoutou, Antoine and Ben Houidi, Zied and Shi, Hao and Cai, Shengming and Xu, Jinchun and Mathieu, Bertrand and Rossi, Dario}, booktitle={2020 IFIP Networking Conference (IFIP Networking)}, year={2020}, organization={IEEE}}

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).**

Cameras located on state controlled roads that provide a live feed of traffic conditions via a web page.

The RMS Transport Management Centre has live traffic cameras strategically located across Sydney's road network in the inner city, North, South and West. The images from the cameras are updated approximately every 60 seconds.

Data available to download in GeoJSON and JPEG formats.