The number of unique visitors to the Smithsonian Institution's websites declined by 11.6 percent in 2024 over the previous fiscal year. In the fiscal year ending September 30, 2024, the combined number of visits across all Smithsonian websites totaled almost 150 million.

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.

Weather Channel had 285.6 million average visitors to its website in the 12 months running to May 2024, making it the leading global news brand worldwide in this respect. Following in second place was the New York Times with 113 million web visitors.

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Deep Dive into Web Traffic Data At the core of our solution is the sophisticated analysis of "Web Traffic Data." Our system meticulously collects and processes every interaction on your site, from page views to time spent on each section. This data, once anonymous and perhaps seen as abstract numbers, is transformed into a detailed ledger of potential leads and customer insights. By understanding who visits your site, their interests, and their contact information, your business is equipped to tailor marketing efforts, personalize customer experiences, and streamline sales processes like never before.

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Tag Deployment: Implement our unique tag on your website with simple instructions. This tag is lightweight and does not impact your site's loading speed or user experience.

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Security and Privacy: Our Commitment Understanding the sensitivity of web traffic data and contact information, our solution is built with security and privacy at its core. We adhere to strict data protection regulat...

A complete list of live websites using the Visitors Traffic Real Time Statistics technology, compiled through global website indexing conducted by WebTechSurvey.

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.

This dataset provides the number of users to the Valuation Office website.

A complete list of live websites using the WP Live Visitor Counter technology, compiled through global website indexing conducted by WebTechSurvey.

Monthly statistics for pages viewed by visitors to the Queensland Government website—Your rights, crime and the law franchise. Source: Google Analytics

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

Number of Visitors: Um Qais data was reported at 2,403.000 Person in Dec 2017. This records a decrease from the previous number of 3,319.000 Person for Nov 2017. Number of Visitors: Um Qais data is updated monthly, averaging 6,275.500 Person from Jan 2004 (Median) to Dec 2017, with 168 observations. The data reached an all-time high of 105,218.000 Person in Apr 2012 and a record low of 1,246.000 Person in Jun 2017. Number of Visitors: Um Qais data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

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.

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.

Number of Visitors: Salt Museum: Residents data was reported at 85.000 Person in Dec 2017. This records a decrease from the previous number of 96.000 Person for Nov 2017. Number of Visitors: Salt Museum: Residents data is updated monthly, averaging 60.000 Person from Jan 2004 (Median) to Dec 2017, with 168 observations. The data reached an all-time high of 1,255.000 Person in Apr 2007 and a record low of 0.000 Person in Mar 2008. Number of Visitors: Salt Museum: Residents data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

Number of Visitors: Um Aljmal data was reported at 140.000 Person in Dec 2017. This records a decrease from the previous number of 224.000 Person for Nov 2017. Number of Visitors: Um Aljmal data is updated monthly, averaging 126.000 Person from Jan 2004 (Median) to Dec 2017, with 168 observations. The data reached an all-time high of 1,166.000 Person in Jun 2014 and a record low of 17.000 Person in Jul 2010. Number of Visitors: Um Aljmal data remains active status in CEIC and is reported by Ministry of Tourism and Antiquities. The data is categorized under Global Database’s Jordan – Table JO.Q009: Number of Visitors: by Tourist Sites.

For the purpose of monitoring its visitor centres, National Nature Reserves (NNR) and bike trails, Natural Resources Wales contracted Linetop LTD to provide data and reporting on visitor numbers.

Reports are produced quarterly and provide a breakdown of visitor numbers in each site and reserve. This data includes:

Site cards - which provide details of counter placements and specification.
Raw Count data - raw counts from sensors. Reports - summary data provided as MS Word documents at regular intervals.

This metadata entry also covers the Grafana Chambers Electronics downloadable visitor counter data which is time stamp and number count data, which is affectively counting the same thing as the Linetop data.

User guide for the ArcGIS Online Statewide Traffic Count AppThe guide covers essential aspects, including:Map Functions Overview: This section details the basic interactive functions of the map, including zooming, panning, and identifying features. It will explain how to navigate the map interface effectively, find specific locations, and understand the map's overall layout and controls. Turn Layers On and Off: This portion of the guide will teach users how to control the visibility of different data layers within the map. Users will learn how to toggle layers on and off to customize the map display, focusing on specific traffic count data or related information. This allows for a more focused analysis of the data. Attribute Table and Export Data: This section explains how to access and utilize the attribute table associated with the traffic count data. Users will learn how to view detailed information about each traffic count location, including specific count values, dates, and other relevant attributes. Furthermore, this section will instruct how to export the attribute table data into formats like CSV or Excel for further analysis outside of the online application. Downloading Data: This portion of the guide will explain how to download the traffic count data. It will explain what file types are available for download, and any restrictions that are placed on the data.

XML Authority services statistics, digitization and completion rate for the year 2023 - the first quarter (metadata)

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

Monthly statistics for pages viewed by visitors to the Queensland Government website—Parents and families franchise. Source: Google Analytics