In January 2024, users who reached Reddit.com from links displayed after launching a research on search engines like Google or Yahoo generated over 4.6 billion visits. Between April 2022 and January 2024, search traffic volumes to Reddit experienced a positive trend.

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

From October 2024 to February 2025, ChatGPT outperformed competing AI-powered search engines in traffic referral, achieving a total growth of 155.52 percent. Perplexity placed second, despite experiencing more significant fluctuations, with a total growth of 54.78 percent by the conclusion of the analyzed period. With a 43.64 percent overall growth, Google's Gemini ranked third among other engines and maintained the most consistent traffic referral rate. Artificial intelligence-driven trends, notably AI-powered search, are changing online traffic patterns. This suggests a more significant change in the way users find information online and is expected to have a knock-on effect on the digital advertising sector.

In July 2019, the Metropolitan Police Department (MPD) implemented new data collection methods that enabled officers to collect more comprehensive information about each police stop in an aggregated manner. More specifically, these changes have allowed for more detailed data collection on stops, protective pat down (PPDs), searches, and arrests. (For a complete list of terms, see the glossary on page 2.) These changes support data collection requirements in the Neighborhood Engagement Achieves Results Amendment Act of 2016 (NEAR Act).The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from July 22, 2019 to December 31, 2022. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest.If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.” Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date ofbirth and the date of the stop.There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors. On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS.Due to this transition, the data collection and structures for the period between August 1, 2021 – December 31, 2021 were changed. The list below provides explanatory notes to consider when using this dataset.New fields for data collection resulted in an increase of outliers in stop duration (affecting 0.98% of stops). In order to mitigate the disruption of outliers on any analysis, these values have been set to null as consistent with past practices.Due to changes to the data structure that occurred after August 1, 2021, six attributes pertaining to reasons for searches of property and person are only available for the first seven months of 2021. These attributes are: Individual’s Actions, Information Obtained from Law Enforcement Sources, Information Obtained from Witnesses or Informants, Characteristics of an Armed Individual, Nature of the Alleged Crime, Prior Knowledge. These data structure changes have been updated to include these attributes going forward (as of April 23, 2022).Out of the four attributes for types of property search, warrant property search is only available for the first seven months of 2021. Data structure changes were made to include this type of property search in future datasets.The following chart shows how certain property search fields were aligned prior to and after August 1, 2021. A glossary is also provided following the chart. As of August 2, 2022, these fields have reverted to the original alignment.https://mpdc.dc.gov/sites/default/files/dc/sites/mpdc/publication/attachments/Explanatory%20Notes%202021%20Data.pdfIn October 2022 several fields were added to the dataset to provide additional clarity differentiating NOIs issued to bicycles (including Personal Mobility Devices, aka stand-on scooters), pedestrians, and vehicles as well as stops related specifically to MPD’s Harbor Patrol Unit and stops of an investigative nature where a police report was written. Please refer to the Data Dictionary for field definitions.In March 2023 an indicator was added to the data which reflects stops related to traffic enforcement and/or traffic violations. This indicator will be 1 if a stop originated as a traffic stop (including both stops where only a ticket was issued as well as stops that ultimately resulted in police action such as a search or arrest), involved an arrest for a traffic violation, and/or if the reason for the stop was Response to Crash, Observed Moving Violation, Observed Equipment Violation, or Traffic Violation.Between November 2021 and February 2022 several fields pertaining to items seized during searches of a person were not available for officers to use, leading to the data showing that no objects were seized pursuant to person searches during this time period. Finally, MPD is conducting on-going data audits on all data for thorough and complete information. For more information regarding police stops, please see: https://mpdc.dc.gov/stopdataFigures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 07/12/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

Use Case: Analyze Year Over Year Growth Rate by Region

Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

Traffic Count Viewer is an online mapping application, which users can use to explore traffic count reports in different locations within the Delaware Valley, including Philadelphia. Users search by location (address, city, zip code, or place name) to view point features on the interactive mapping visualization of traffic records. Clicking on a point of interest or grouping multiple points on the map yields traffic count information tables, which includes: Date of Counnt ; DVRPC File # ; Type ; Annual Average Daily Traffic (AADT) ; Municipality ; Route Number ; Road Name ; Count Direction ; and From/To Locations, as well as a link to the detailed (hourly) report. Data tables are exportable as .CSV and detailed reports are available for export in multiple formats (including basic .doc and .rtf outputs.) Traffic count data is collected by the Delaware Valley Regional Planning Commission and other agencies.

As of the fourth quarter of 2019, 49 percent of Yahoo's U.S. organic search traffic originated from mobile devices. Google had the highest share of organic mobile search traffic with 61 percent, slightly ahead of DuckDuckGo with 58 percent.

The accompanying data cover all MPD stops including vehicle, pedestrian, bicycle, and harbor stops for the period from January 1, 2023 – December 31, 2024. A stop may involve a ticket (actual or warning), investigatory stop, protective pat down, search, or arrest. If the final outcome of a stop results in an actual or warning ticket, the ticket serves as the official documentation for the stop. The information provided in the ticket include the subject’s name, race, gender, reason for the stop, and duration. All stops resulting in additional law enforcement actions (e.g., pat down, search, or arrest) are documented in MPD’s Record Management System (RMS). This dataset includes records pulled from both the ticket (District of Columbia Department of Motor Vehicles [DMV]) and RMS sources. Data variables not applicable to a particular stop are indicated as “NULL.” For example, if the stop type (“stop_type” field) is a “ticket stop,” then the fields: “stop_reason_nonticket” and “stop_reason_harbor” will be “NULL.” Each row in the data represents an individual stop of a single person, and that row reveals any and all recorded outcomes of that stop (including information about any actual or warning tickets issued, searches conducted, arrests made, etc.). A single traffic stop may generate multiple tickets, including actual, warning, and/or voided tickets. Additionally, an individual who is stopped and receives a traffic ticket may also be stopped for investigatory purposes, patted down, searched, and/or arrested. If any of these situations occur, the “stop_type” field would be labeled “Ticket and Non-Ticket Stop.” If an individual is searched, MPD differentiates between person and property searches. Please note that the term property in this context refers to a person’s belongings and not a physical building. The “stop_location_block” field represents the block-level location of the stop and/or a street name. The age of the person being stopped is calculated based on the time between the person’s date of birth and the date of the stop. There are certain locations that have a high prevalence of non-ticket stops. These can be attributed to some centralized processing locations. Additionally, there is a time lag for data on some ticket stops as roughly 20 percent of tickets are handwritten. In these instances, the handwritten traffic tickets are delivered by MPD to the DMV, and then entered into data systems by DMV contractors. On August 1, 2021, MPD transitioned to a new version of its current records management system, Mark43 RMS. Beginning January 1, 2023, fields pertaining to the bureau, division, unit, and PSA (if applicable) of the officers involved in events where a stop was conducted were added to the dataset. MPD’s Records Management System (RMS) captures all members associated with the event but cannot isolate which officer (if multiple) conducted the stop itself. Assignments are captured by cross-referencing officers’ CAD ID with MPD’s Timesheet Manager Application. These fields reflect the assignment of the officer issuing the Notice of Infraction (NOIs) and/or the responding officer(s), assisting officer(s), and/or arresting officer(s) (if an investigative stop) as of the end of the two-week pay period for January 1 – June 30, 2023 and as of the date of the stop for July 1, 2023 and forward. The values are comma-separated if multiple officers were listed in the report. For Stop Type = Harbor and Stop Type = Ticket Only, the officer assignment information will be in the NOI_Officer fields. For Stop Type = Ticket and Non-Ticket the officer assignments will be in both NOI Officer (for the officer that issued the NOI) and RMS_Officer fields (for any other officer involved in the event, which may also be the officer who issued the NOI). For Stop Type = Non-Ticket, the officer assignment information will be in the RMS_Officer fields. Null values in officer assignment fields reflect either Reserve Corps members, who’s assignments are not captured in the Timesheet Manager Application, or members who separated from MPD between the time of the stop and the time of the data extraction. Finally, MPD is conducting on-going data audits on all data for thorough and complete information. Figures are subject to change due to delayed reporting, on-going data quality audits, and data improvement processes.

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).**

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.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

The Network Traffic Analysis (NTA) market is experiencing robust growth, projected to reach $3.56 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 12.78% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing complexity of network infrastructure, coupled with the proliferation of cloud-based applications and the rise of cyber threats, necessitates sophisticated NTA solutions for enhanced security and performance monitoring. Organizations across various sectors, including BFSI (Banking, Financial Services, and Insurance), IT and Telecom, Government, Energy and Power, and Retail, are adopting NTA to gain better visibility into their network traffic, identify anomalies, and proactively address potential issues. The shift towards cloud-based deployment models is further accelerating market growth, offering scalability, flexibility, and reduced infrastructure costs. Competitive innovation within the NTA space, characterized by the development of AI-powered analytics and automation capabilities, is also contributing to this positive trajectory. However, certain restraints are impacting market growth. The high initial investment cost associated with implementing NTA solutions, particularly for smaller organizations, can be a barrier to entry. Furthermore, the need for skilled professionals to effectively manage and interpret NTA data poses a challenge. Despite these challenges, the long-term growth prospects for the NTA market remain strong. The increasing reliance on network connectivity across all aspects of business and the evolving threat landscape will continue to drive demand for advanced NTA solutions throughout the forecast period. The market is segmented by deployment (on-premise and cloud-based) and end-user vertical, with the cloud-based segment expected to show higher growth due to its inherent advantages. This comprehensive report provides an in-depth analysis of the global Network Traffic Analysis (NTA) market, covering the period from 2019 to 2033. It offers valuable insights into market size, growth drivers, emerging trends, challenges, and key players, utilizing data from the base year 2025 and forecasting until 2033. The report is essential for businesses, investors, and researchers seeking a thorough understanding of this dynamic market segment. Key search terms addressed include: Network Traffic Analysis, NTA Market, Network Security, Cybersecurity, Cloud-based NTA, On-premise NTA, Network Monitoring, Data Analytics, and more. Recent developments include: September 2022: AlphaSOC Inc., a security analytics company, introduced its AlphaSOC Analytics Engine (AE) solution, a cloud-native NTA product that identifies the compromised workloads across Google Cloud Platform, Microsoft Azure, and Amazon Web Services., April 2022: Palo Alto Networks launched a product called Okyo Garde Enterprise Edition, which has been designed to provide lateral migration by isolating the company network from the employee's network at home. It would also protect unmanaged work equipment at home, such as hardware prototypes, printers, and VoIP phones.. Key drivers for this market are: Emergence of Network Traffic Analysis as the Key to Cyber Security, Increasing Demand for Higher Access Speed. Potential restraints include: Growing Threat of Video Content Piracy and Security Threat of User Database Due to Spyware. Notable trends are: BFSI Sector is Expected to Hold a Significant Market Share.

Google Search Statistics 2023

• Google is the most searched website in the World.
• Google receives more visitors than any other site. Google is accessed 89.3 trillion times per month.
• Google is used by billions of people every day to conduct their searches. Google is much more than a simple search engine.
• Google provides many other services. Google Shopping and Google News also feature. Google Mail, Google's popular email service, is included.
• Google organic search traffic is 16.3% of the total US searches.

This dataset contains traffic citation information (criminal and civil) from January 2018 forward, including demographic information for officers as well as individuals. Data is updated monthly on the 1st.

View Operations Order 6.2: Arizona Traffic Ticket and Complaint (ATTC) Policy

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Competitive Analysis of Industry Rivals The market for competitive analysis is expected to grow significantly over the forecast period, driven by increasing need for businesses to understand their competitive landscape. Key players in the market include BuiltWith, WooRank, SEMrush, Google, SpyFu, Owletter, SimilarWeb, Moz, SunTec Data, and TrendSource. These companies offer a range of services to help businesses track their competitors' online performance, including website traffic, social media engagement, and search engine rankings. Some of the key trends driving the growth of the market include the increasing adoption of digital marketing by businesses, the growing importance of social media, and the increasing availability of data and analytics tools. The market is segmented by type, application, and region. In terms of type, the market is divided into product analysis, traffic analytics, sales analytics, and others. In terms of application, the market is divided into SMEs and large enterprises. In terms of region, the market is divided into North America, South America, Europe, Middle East & Africa, and Asia Pacific. The North American region is expected to dominate the market during the forecast period, due to the presence of a large number of established players in the market. The Asia Pacific region is expected to grow at the highest CAGR during the forecast period, due to the increasing adoption of digital marketing by businesses in the region. This report provides a comprehensive analysis of the industry rivals, encompassing their concentration, product insights, regional trends, and key industry developments.

Google dominated the Egyptian web traffic. As of November 2022, close to **** percent of the web traffic was referred via this search engine. Bing was its closest competitor, with only *** percent. Yahoo! came in third place, with a share of almost *** percent.

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

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. 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. 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).

Explore the historical Whois records related to hitt-traffic.net (Domain). Get insights into ownership history and changes over time.