Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. 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.

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.

In March 2024, search platform Google.com generated approximately 85.5 billion visits, down from 87 billion platform visits in October 2023. Google is a global search platform and one of the biggest online companies worldwide.

Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

Relationship









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.'

In January 2025 mobile devices excluding tablets accounted for over 62 percent of web page views worldwide. Meanwhile, over 75 percent of webpage views in Africa were generated via mobile. In contrast, just over half of web traffic in North America still took place via desktop connections with mobile only accounting for 51.1 percent of total web traffic. While regional infrastructure remains an important factor in broadband vs. mobile coverage, most of the world has had their eyes on the recent 5G rollout across the globe, spearheaded by tech-leaders China and the United States. The number of mobile 5G subscriptions worldwide is forecast to reach more than 8 billion by 2028. Social media: room for growth in Africa and southern Asia Overall, more than 92 percent of the world’s mobile internet subscribers are also active on social media. A fast-growing market, with newcomers such as TikTok taking the world by storm, marketers have been cashing in on social media’s reach. Overall, social media penetration is highest in Europe and America while in Africa and southern Asia, there is still room for growth. As of 2021, Facebook and Google-owned YouTube are the most popular social media platforms worldwide. Facebook and Instagram are most effective With nearly 3 billion users, it is no wonder that Facebook remains the social media avenue of choice for the majority of marketers across the world. Instagram, meanwhile, was the second most popular outlet. Both platforms are low-cost and support short-form content, known for its universal consumer appeal and answering to the most important benefits of using these kind of platforms for business and advertising purposes.

In 2023, most of the global website traffic was still generated by humans but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 32 percent of global web traffic in the most recently measured period, representing an increase of 1.8 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past two years, indicating a surge in the sophistication of cyber threats. Simultaneously, simple bad bots saw a 6 percent increase compared to the previous year, suggesting a shift in the landscape of automated threats. Meanwhile, areas like entertainment, and law & government face the highest amount of advanced bad bots, with more than 78 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 57.2 percent of the gaming segment's web traffic. Meanwhile, almost half of the online traffic for telecom and ISPs was moved by malicious applications. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and financial services experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

In March 2024, close to 4.4 billion unique global visitors had visited Wikipedia.org, slightly down from 4.4 billion visitors since August of the same year. Wikipedia is a free online encyclopedia with articles generated by volunteers worldwide. The platform is hosted by the Wikimedia Foundation.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

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.

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.

The October 2024 iHsinchu Free Wireless Internet Hotspot Traffic Statistics Overview for Public Viewing

Average Annual Daily Traffic data for use with GIS mapping software, databases, and web applications are from Caliper Corporation and contain data on the total volume of vehicle traffic on a highway or road for a year divided by 365 days.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

Snapshot img

Secure Web Gateway Market Size 2024-2028

The secure web gateway market size is forecast to increase by USD 19.45 billion at a CAGR of 26.79% between 2023 and 2028. The market is experiencing significant growth due to the increasing number of online security threats targeting web products and websites. Traditional firewalls once considered a cyber barrier, are no longer sufficient to protect against unauthorized traffic, malicious websites, and data leakage.

To address these challenges, secure web gateways employ a 7-layered traffic inspection approach, providing application-level control and data leakage prevention. As more companies adopt cloud-based solutions and allow remote workers, the need for advanced web security solutions becomes increasingly important. The rising adoption of cloud-based security technologies is a major market growth factor, despite the high implementation costs.

What will be the size of the Secure Web Gateway Market During the Forecast Period?

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The cyber threat environment continues to evolve, with system viruses and unknown spyware posing significant risks to both individual and organizational data. Unsecured communication channels and malicious web traffic are common avenues for cyber-attacks, making it crucial for businesses to implement strong security solutions. A secure web gateway acts as a cyber barrier, safeguarding against online security threats and protecting against unauthorized traffic, malware attacks, and data breaches. Malicious web traffic, including viruses, malware, and harmful websites, can infiltrate an internal network through unsecured endpoints.

Moreover, trojan horses, adware, and spyware are common threats that can compromise individual data and organizational data. Remote employees working from home present an additional challenge, as they may access the company network through unsecured Wi-Fi or unprotected devices. A secure web gateway acts as a centralized filtering system, controlling web requests and blocking access to malicious websites. It provides an essential layer of security, preventing unauthorized access to sensitive data and protecting against known and unknown threats. By implementing a secure web gateway, companies can enforce company policy, ensuring that all web traffic is secure and compliant. Online security threats are a constant concern for businesses, with data breaches and malware attacks becoming increasingly common.

Furthermore, a secure web gateway helps mitigate these risks by providing a comprehensive solution for securing end-user data and web security. It blocks malicious web traffic, preventing the spread of viruses, malware, and other harmful software. By implementing a secure web gateway, businesses can protect their valuable data and maintain their online reputation. In conclusion, a secure web gateway is an essential component of any organization's cybersecurity strategy. It provides a critical layer of protection against online security threats, including malicious web traffic, viruses, malware, and harmful websites. By implementing a secure web gateway, businesses can safeguard their data, protect against unauthorized traffic, and maintain compliance with industry regulations.

Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Deployment

  Cloud
  On-premises


End-user

  BFSI
  IT and telecom
  Government and defense
  Others


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Deployment Insights

The cloud segment is estimated to witness significant growth during the forecast period. The market is expected to experience substantial expansion in the coming years due to the escalating requirement for comprehensive data and identity security in corporations. With the rise in cybercrime and the increasing number of threats from hackers, the demand for sophisticated security solutions is surging. The adoption of cloud security services is gaining traction among large enterprises and small businesses as they transfer an increasing volume of sensitive and confidential data. The proliferation of mobile devices for both professional and personal use is further fueling the demand for cloud security solutions, as these devices are highly susceptible to attacks.

Furthermore, secure Web Gateways provide advanced functionalities such as antivirus, application control, data loss prevention, and HTTPS inspection to protect enterprises from harmful code and potential leaks. The financial sector, including banks, is a significant end user of Secure Web Gateways due to the sensitive nature of the data they handle. The

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

In November 2024, Google.com was the most popular website worldwide with approximately 6.25 billion unique monthly visitors. YouTube.com was ranked second with an estimated 3.64 billion unique monthly visitors. Both websites are among the most visited websites worldwide.

In January 2024, ChatGPT saw approximately 484.82 million visits from users worldwide to its web address, chat.openai.com. This represents a slight decrease from the previous year, when global traffic to the popular chatbot website reached over 650 million monthly visits in May 2023, coinciding with the release of the software's mobile app for the App Store.

This statistic shows the data volume development in stationary broadband internet traffic via landline in Germany from 2001 to 2022, with an estimate for 2023. In 2023, the data volume was estimated to have amounted to roughly 142.1 billion gigabytes.

Online retail websites have made strong traffic gains due to the global coronavirus pandemic as large parts of the population are staying at home and ordering items online which they usually would purchase in-store. Amazon.com had a monthly traffic of almost 3.2 billion visitors in 2022, followed by eBay.com with nearly 590 million visits on each month.