This statistic shows a ranking of online traffic sources towards the website of HelloFresh from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as 45 percent of visits towards the site came in this way. Organic search results were less important: these made up 33 percent of total desktop traffic.

Perhaps unsurprisingly, the main traffic source for false information online is social media, which generates 42 percent of fake news traffic. The nature of social networks, most notably the ease of sharing content, allows fake news to spread at a rapid rate – an issue further exacerbated by the fact that many U.S. adults sometimes believe fake news to be real.

Fake news: an ongoing problem

The presence of fake news would be less of an issue if users were more aware of how to identify it and were aware of the risks of sharing such content. Many U.S. news consumers have shared fake news online, and worryingly, ten percent did so deliberately. Adults who are part of that ten percent are just a small portion of people in the United States, and elsewhere in the world, who are responsible for spreading false information. More than 30 percent of U.S. children and teenagers have shared a fake news story online, and over 50 percent of adults in selected countries worldwide have wrongly believed a fake news story.

The result of adults and young consumers alike not only believing fake news, but actively sharing it, is that small, illegitimate websites producing such content are able to grow more successful. Such websites have the potential to tarnish or seriously damage the reputation of any persons mentioned within a fake news article, promote events or policies which do not exist, and mislead readers about important topics they are trying to keep up with. A 2019 survey revealed that most adults believe that fake news and misinformation will get worse in the next five years, and the sad truth is that this will likely be the case unless news consumers grow more discerning about what they post and share online.

This statistic shows a ranking of online traffic sources towards the website of Domino's Pizza from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as 49 percent of visits towards the site came in this way. Organic search results were less important: these made up 27 percent of total desktop traffic.

Real Time Traffic Data Market Outlook

The global real-time traffic data market size is anticipated to reach USD 15.3 billion by 2032 from an estimated USD 6.5 billion in 2023, exhibiting a robust CAGR of 10.1% over the forecast period. This substantial growth is driven by the increasing need for efficient traffic management systems and the rising adoption of smart city initiatives worldwide. Governments and commercial entities are investing heavily in advanced technologies to optimize traffic flow and enhance urban mobility, thus fostering market expansion.

The surge in urbanization and the consequent rise in vehicle ownership have led to severe traffic congestion issues in many metropolitan areas. This has necessitated the implementation of real-time traffic data systems that can provide accurate and timely information to manage traffic effectively. With the integration of sophisticated technologies such as IoT, AI, and big data analytics, these systems are becoming more efficient, thereby driving market growth. Furthermore, the growing emphasis on reducing carbon emissions and enhancing road safety is also propelling the adoption of real-time traffic data solutions.

Technological advancements are playing a pivotal role in shaping the real-time traffic data market. Innovations in sensor technology, the proliferation of GPS devices, and the widespread use of mobile data are providing rich sources of real-time traffic information. The ability to integrate data from multiple sources and deliver actionable insights is significantly enhancing traffic management capabilities. Additionally, the development of cloud-based solutions is enabling scalable and cost-effective deployment of traffic data systems, further contributing to market growth.

Another critical growth factor is the increasing investment in smart city projects. Governments across the globe are prioritizing the development of smart transportation infrastructure to improve urban mobility and reduce traffic-related issues. Real-time traffic data systems are integral to these initiatives, providing essential data for optimizing traffic flow, enabling route optimization, and enhancing public transport efficiency. The involvement of private sector players in these projects is also fueling market growth by introducing innovative solutions and fostering public-private partnerships.

The exponential rise in Mobile Data Traffic is another significant factor influencing the real-time traffic data market. As more people rely on smartphones and mobile applications for navigation and traffic updates, the demand for real-time data has surged. Mobile data provides a wealth of information about traffic patterns and congestion levels, enabling more accurate and timely traffic management. The integration of mobile data with other data sources, such as GPS and sensor data, enhances the overall effectiveness of traffic data systems. This trend is particularly evident in urban areas where mobile devices are ubiquitous, and the need for efficient traffic management is critical. The ability to harness mobile data for traffic insights is driving innovation and growth in the market, as companies develop new solutions to leverage this valuable resource.

Regionally, North America and Europe are leading the market due to their early adoption of advanced traffic management technologies and significant investments in smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid urbanization, increasing vehicle ownership, and growing government initiatives to develop smart transportation infrastructure. Emerging economies in Latin America and the Middle East & Africa are also showing promising growth potential, fueled by ongoing infrastructure development and increasing awareness of the benefits of real-time traffic data solutions.

Component Analysis

The real-time traffic data market by component is segmented into software, hardware, and services. Each component plays a crucial role in the overall functionality and effectiveness of traffic data systems. The software segment includes traffic management software, route optimization software, and other analytical tools that help process and analyze traffic data. The hardware segment comprises sensors, GPS devices, and other data collection tools. The services segment includes installation, maintenance, and consulting services that support the deployment and operation of traffic data systems

Road Source Noise Model The dataset contains the noise model results for the Dublin Region showing population exposure to sound from traffic sources. The noise maps show colour coded areas in Dublin based on sound levels in 5 bands. These increment in 5 decibels. The night time band starts at 50 decibels and the 24 hour band starts at 55 decibels. There are two categories of sound sources mapped all roads and major roads (roads with more than 3 million vehicle passages per year). Traffic volumes are averaged to an hourly traffic count over a typical 24 hour day. The supporting dataset Total Traffic Volumes and Road Centrelines for Dublin City is also published on Dublinked. Dublin City Council revised the first set of 2007 road source noise maps to produce the current maps for June 2012. The 2012 Revision of Noise Maps and Action Plans are available to download in kml format at http://www.dublincity.ie/WaterWasteEnvironment/NoiseMapsandActionPlans/Pages/default.aspx

Road Source Noise Model The dataset contains the noise model results for the Dublin Region showing population exposure to sound from traffic sources. The noise maps show colour coded areas in Dublin based on sound levels in 5 bands. These increment in 5 decibels. The night time band starts at 50 decibels and the 24 hour band starts at 55 decibels. There are two categories of sound sources mapped all roads and major roads (roads with more than 3 million vehicle passages per year). Traffic volumes are averaged to an hourly traffic count over a typical 24 hour day. The supporting dataset Total Traffic Volumes and Road Centrelines for Dublin City is also published on Dublinked. Dublin City Council revised the first set of 2007 road source noise maps to produce the current maps for June 2012. The 2012 Revision of Noise Maps and Action Plans are available to download in kml format at http://www.dublincity.ie/WaterWasteEnvironment/NoiseMapsandActionPlans/Pages/default.aspx

This statistic shows a ranking of online traffic sources towards the website of Wehkamp from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as 53 percent of visits towards the site came in this way. Organic search results were less important: these made up 21 percent of total desktop traffic.

Noise maps from traffic sources in DCC. Published by Dublin City Council. Available under the license cc-by (CC-BY-4.0).Road Source Noise Model The dataset contains the noise model results for the Dublin Region showing population exposure to sound from traffic sources. The noise maps show colour coded areas in Dublin based on sound levels in 5 bands. These increment in 5 decibels. The night time band starts at 50 decibels and the 24 hour band starts at 55 decibels. There are two categories of sound sources mapped all roads and major roads (roads with more than 3 million vehicle passages per year). Traffic volumes are averaged to an hourly traffic count over a typical 24 hour day. The supporting dataset Total Traffic Volumes and Road Centrelines for Dublin City is also published on Dublinked. Dublin City Council revised the first set of 2007 road source noise maps to produce the current maps for June 2012. The 2012 Revision of Noise Maps and Action Plans are available to download in kml format at http://www.dublincity.ie/WaterWasteEnvironment/NoiseMapsandActionPlans/Pages/default.aspx...

According to data from 2023, the majority of online traffic driving personal care shoppers to retailers in the United Kingdom came from search. Both organic and paid search accounted for approximately 78 percent of the purchase intent click share. Social media ranked second, with a share of 16 percent.

Road Source Noise Model The dataset contains the noise model results for the Dublin Region showing population exposure to sound from traffic sources. The noise maps show colour coded areas in Dublin based on sound levels in 5 bands. These increment in 5 decibels. The night time band starts at 50 decibels and the 24 hour band starts at 55 decibels. There are two categories of sound sources mapped all roads and major roads (roads with more than 3 million vehicle passages per year). Traffic volumes are averaged to an hourly traffic count over a typical 24 hour day. The supporting dataset Total Traffic Volumes and Road Centrelines for Dublin City is also published on Dublinked. Dublin City Council revised the first set of 2007 road source noise maps to produce the current maps for June 2012. The 2012 Revision of Noise Maps and Action Plans are available to download in kml format at http://www.dublincity.ie/WaterWasteEnvironment/NoiseMapsandActionPlans/Pages/default.aspx

Analysis of ‘Popular Website Traffic Over Time ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/popular-website-traffice on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Have you every been in a conversation and the question comes up, who uses Bing? This question comes up occasionally because people wonder if these sites have any views. For this research study, we are going to be exploring popular website traffic for many popular websites.

Methodology

The data collected originates from SimilarWeb.com.

Source

For the analysis and study, go to The Concept Center

This dataset was created by Chase Willden and contains around 0 samples along with 1/1/2017, Social Media, technical information and other features such as: - 12/1/2016 - 3/1/2017 - and more.

How to use this dataset

• Analyze 11/1/2016 in relation to 2/1/2017
• Study the influence of 4/1/2017 on 1/1/2017
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Listen to this episode: About This Episode: Traffic sources can come from a number of places, but for most companies the largest source is Google. And things can get confusing when it comes to organic traffic versus paid ads. There are a number of things that can affect organic traffic and paid traffic in Google, […]

This statistic shows a ranking of online traffic sources towards the website of online retailer Bol.com from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as 52 percent of visits towards the site came in this way. Organic search results were less important: these made up 33 percent of total desktop traffic.

As of March 2024, YouTube.com accounted for over 37.5 percent of social media referral traffic to Pinterest.com. Pinterest's second-largest social media traffic driver were web.whatsapp.com and facebook.com, each generating more than 8.41 and 8.05 percent of social media traffic to Pinterest.

Traffic information is crucial for managing transportation and city planning, but obtaining national-scale data is difficult due to privacy concerns. Consequently, most current traffic datasets have limitations in terms of time and location coverage, leading to a lack of comprehensive public access to national traffic data. To address this issue, a multi-source highway traffic dataset has been created, featuring 2042 sensors in New Zealand over a 9-year period with 15-minute intervals and accompanying metadata. The dataset includes data of both light-duty and heavy-duty vehicles, as well as weather information like temperature and precipitation. This dataset has diverse potential research applications such as traffic flow prediction and congestion management.

Analysis of ‘Traffic Studies: Speed Reports [BETA]’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b46893d2-0664-4627-ad79-0e89edde11ff on 28 January 2022.

--- Dataset description provided by original source is as follows ---

THIS DATASET IS UNDER ACTIVE DEVELOPMENT AND IS SUBJECT TO CHANGE AT ANY TIME

Traffic Study Speed Reports contain time-aggregated vehicle speed data collected from road tubes.

For more information about traffic study data, see here: https://github.com/cityofaustin/transportation-data-publishing/wiki/Traffic-Count-Data-Publishing

--- Original source retains full ownership of the source dataset ---

This statistic shows a ranking of online traffic sources towards the website of Zalando from 2017 to 2019. The numbers provided here concern desktop visits from the Netherlands. Direct traffic is deemed to be the most important source for web traffic, as 54 percent of visits towards the site came in this way. Organic search results were less important: these made up 32 percent of total desktop traffic.

Annualized, Hourly and Classification count data for the TPB modeled region. Data are collected from state DOTs and processed by TPB staff.Layers IncludedAnnualized Traffic Volumes Historic AADT by Count Station This database contains the Annual Average Daily Traffic (AADT) estimates reported at permanent and short term counting stations in the TPB modeled region. Please note: Interstates in Virginia are typically represented by two stations (one in each direction) while Interstates in the other states are represented by one station. Therefore, the AADT estimates displayed for the stations on Virginia Intestates will be around half of the total for the directional roadway. The AADT estimates for recent years in this file are based on counts taken at the actual count station locations that are indicated by the station points. The AADT estimates for earlier years are based on volumes reported along roadway segments that the station points currently represent. Specific data sources for each state are listed below:District of ColumbiaAADT estimates since 2006 are based on counts taken at the station locations in the file for purpose of Federal HPMS reporting.AADT estimates prior to 2006 are based on Traffic Volume maps produced by DDOT (Formerly DC DPW).MarylandAADT estimates since 2000 are based on counts taken at the station locations in the file and reported by MD SHA.AADT estimates prior to 2000 are based on volumes reported by MD SHA in the Highway Location Reference documents and matched to links in the COG/TPB highway network. The volumes are shown at the count locations that currently represent those network links.VirginiaAADT estimates since 1997 are based on counts taken at the station locations in the file and reported by VDOT.AADT estimates prior to 1997 are based on volumes reported by VDOT in the Average Daily Traffic Volumes documents and matched to links in the COG/TPB highway network. The volumes are shown at the count locations that currently represent those network links.West VirginiaAADT estimates since 1999 are based on counts taken at the station locations in the file and reported by WV DOT.Traffic Counts by Network LinkThis layer was created by assigning the state DOT traffic counting station locations to their corresponding COG/TPB network links. Facility names and route numbers were added to the network. AADT Average Annual Daily Traffic (2016 - 2018), AAWDT Average Annual Weekday Daily Traffic (2016 - 2018) and Count Type (2016 - 2018) are included as well as Single Unit Truck Percent AAD (2018), Combination Unit Truck Percent AADT (2018), Bus Percent AADT (2018, only available for Maryland and Virginia), K Factor (2018), Dir Factor (2018), and Count Year (last year the link was counted). Count Type denotes the source of the count. Please note: for bi-directional roads, the AADT and AAWDT values for each location were divided in two and assigned to both network links that represent the Anode-Bnode direction and the Bnode-Anode direction. Therefore, in most cases the AADT/AAWDT values associated with an individual link in this network will be half of the AADT/AAWDT values reported at the associated individual count station point. Traffic Counts by External StationThis layer was created by placing points where major facilities cross the TPB Modeled Area boundary. In some cases, the external station represents more than one facility. The facility field indicates which road or roads the station represents. AADT and AAWDT estimates at external stations are provided for 2007 through 2022. Each external station is assigned to a state DOT traffic counting station(s). An effort was made to assign stations or combinations of stations that would come closest to measuring the traffic volume on each facility as it enters/exits the region. In some cases, these volumes are measured just inside the modeled area; in other cases, the volumes are measured just outside the modeled area. The external stations around the Baltimore Beltway are exceptions to this rule. These stations all measure the traffic just south of the Baltimore Beltway in order lessen the influence of traffic specific to Baltimore. AADT Average Annual Daily Traffic (2007 – 2022) and AAWDT Average Annual Weekday Daily Traffic (2007 – 2022) are included. Count Type denotes when the location was last counted. West Virginia does not report AAWDT, so the AADT values were increased by 5% to arrive at AAWDT estimates in West Virginia.

The dataset provides 12 months (August 2016 to August 2017) of obfuscated Google Analytics 360 data from the Google Merchandise Store , a real ecommerce store that sells Google-branded merchandise, in BigQuery. It’s a great way analyze business data and learn the benefits of using BigQuery to analyze Analytics 360 data Learn more about the data The data includes The data is typical of what an ecommerce website would see and includes the following information:Traffic source data: information about where website visitors originate, including data about organic traffic, paid search traffic, and display trafficContent data: information about the behavior of users on the site, such as URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions on the Google Merchandise Store website.Limitations: All users have view access to the dataset. This means you can query the dataset and generate reports but you cannot complete administrative tasks. Data for some fields is obfuscated such as fullVisitorId, or removed such as clientId, adWordsClickInfo and geoNetwork. “Not available in demo dataset” will be returned for STRING values and “null” will be returned for INTEGER values when querying the fields containing no data.This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery

Analysis of ‘Chicago Traffic Tracker - Congestion Estimates by Regions’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ee394c64-63ee-4250-85ea-bb67295c3902 on 13 February 2022.

--- Dataset description provided by original source is as follows ---

This dataset contains the current estimated congestion for the 29 traffic regions. For a detailed description, go to: http://bitly.com/TeqrNv.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area).

There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

--- Original source retains full ownership of the source dataset ---