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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.

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.'

According to the results of a survey conducted worldwide in 2023, nearly **** of responding digital marketers believed artificial intelligence (AI) would have a positive impact on website search traffic in the next five years. Some ** percent stated AI would have a neutral effect, while ** percent agreed that the technology would negatively impact search traffic.

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.

As of 2019, direct traffic accounts for the largest percentage of website traffic worldwide, with a share of 55 percent. Additionally, search traffic accounts for 29 percent of worldwide website traffic.

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.

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.

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.

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.

AADT represents current (most recent) Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locations Active feature class as of the annual HPMS freeze in January. Historical AADT is found in another table. Please note that updates to this dataset are on an annual basis, therefore the data may not match ground conditions or may not be available for new roadways. Resource Contact: Christy Prentice, Traffic Forecasting & Analysis (TFA), http://www.dot.state.mn.us/tda/contacts.html#TFA

Check other metadata records in this package for more information on Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

Annual Average Daily Traffic Locations in Minnesota: Annual Average Daily Traffic Locations

The keyword rank tracking software market is experiencing robust growth, driven by the increasing reliance on SEO for online visibility and the evolving complexity of search engine algorithms. The market, estimated at $1.5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 15% through 2033. This growth is fueled by several key factors. Firstly, businesses of all sizes are increasingly recognizing the critical role of organic search in driving website traffic and leads. Secondly, the ongoing evolution of search engine algorithms necessitates sophisticated tools capable of monitoring and analyzing rank fluctuations, enabling proactive optimization strategies. Thirdly, the market is witnessing innovation in functionality, with tools incorporating features like competitor analysis, keyword research integration, and advanced reporting capabilities, enhancing their value proposition. This is further supported by a highly competitive landscape populated by established players like SEMrush and Ahrefs alongside emerging niche solutions, fostering innovation and driving market expansion. However, the market also faces certain challenges. The high cost of advanced features in some premium solutions can be a barrier for smaller businesses. Furthermore, the ever-changing landscape of search engine algorithms requires continuous software updates and adaptation, demanding substantial resources from developers. Despite these hurdles, the market's growth trajectory remains positive, particularly propelled by the rising adoption of cloud-based solutions and an increasing demand for data-driven SEO strategies. Market segmentation reveals a strong preference for tools offering comprehensive features, robust reporting, and user-friendly interfaces. The geographical distribution of market share is expected to be relatively concentrated in regions like North America and Europe initially, with emerging markets gradually gaining traction in the coming years as digitalization accelerates.

This statistic shows the frequency of students visiting colleges' own websites during their college search in the United States in 2015. In 2015, about ** percent of respondents stated that they visited college websites a few times a week during their search for a college.

The MUTCD Official Rulings is a resource that allows web site visitors to obtain information about requests for changes, experiments, and interpretations related to the MUTCD that have been received by the FHWA. Copies of various documents (such as incoming request letters, response letters from the FHWA, progress reports, and final reports) that are available in both pdf and html formats may be viewed on this web site. The current status of experiments, as well as any contact information for the requestor that has been made a part of the public record, is also available.

Introduction

SEO Statistics: SEO is termed as Search Engine Optimization, which is the process of improving the quality and quantity of website traffic to a website or a web page from search engines. They aim to drive more qualified traffic to a site mostly targeting original traffic (unpaid search) rather than direct, referral, social, or paid traffic. SEO also helps in improving user experience and increasing conversion rates.

SEO brings together technical skills, smart content planning, and regular tracking to keep up with the changes in search engine rules, especially Google's. This article includes several different current analyses from different insights that will guide you in understanding the topic better.

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.

The pNEUMA Vision dataset is the drone traffic imagery dataset that contains images of frame and vehicle annotations as positions. This dataset is the expansion of the pNEUMA, the urban trajectory dataset collected by swarms of drones in Athens.

For more details about pNEUMA and pNEUMA Vision, please check our website at https://open-traffic.epfl.ch and github.

**The Leeds City Council UTMC has recently decommissioned live car park data collection at many sites due to cost saving measures. **

The Leeds City Council UTMC is currently reviewing an alternative method of collecting this data and will publish the relevant data on Data Mill North as and when it becomes available.

Additional information

• Check the Leeds Travel info website: http://www.leedstravel.info/

In May 2020, YouTube generated over 5.3 billion global visits via organic search traffic. Second-ranked Wikipedia accumulated less than half of that, claiming 2.2 billion organic search visits. Social network Facebook rounded off the top properties with more than a billion organic search visits during the measured period.

In 2025, Google was the most used search engine in Morocco, accounting for nearly ** percent of the web traffic. The next most used search engine was Bing, which made up over *** percent of web traffic in Morocco. The number of people using the internet in Morocco stood at **** million in 2025, the fifth highest amount of internet users in Africa.