Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

Brazil Highways Statistics: Traffic Tolled: Total Traffic data was reported at 1,834.949 Unit in 2017. This records an increase from the previous number of 1,782.727 Unit for 2016. Brazil Highways Statistics: Traffic Tolled: Total Traffic data is updated yearly, averaging 681.920 Unit from Dec 1996 (Median) to 2017, with 22 observations. The data reached an all-time high of 1,834.949 Unit in 2017 and a record low of 19.564 Unit in 1996. Brazil Highways Statistics: Traffic Tolled: Total Traffic data remains active status in CEIC and is reported by Brazilian Association of Highway Concessionaires. The data is categorized under Brazil Premium Database’s Automobile Sector – Table BR.RAW003: Highways Statistics: Traffic Tolled. The Brazilian Association of Highway Concessionaires-ABCR represents the highway concession sector.

Feature layer containing authoritative traffic count points for Sioux Falls, South Dakota.The traffic counts listed are 24-hour, weekday, two-directional counts. Traffic counts are normally collected during the summer months, but may be taken any season, as weather permits. The traffic counts are factored by the day of the week as well as by the month of the year to become an Average Annual Daily Total (AADT). Traffic volumes (i.e. count data) can fluctuate depending on the month, week, day of collection; the weather, type of road surface, nearby construction, etc. All of the historical data should be averaged to reflect the "normal" traffic count. More specific count data (time, date, hourly volume) can be obtained from the Sioux Falls Engineering Division at 367-8601.

Aurora:GeoStudio® is a premier geospatial analysis platform that excels in supporting foot traffic data through its sophisticated Population Dynamics® analytic. Foot traffic data encompasses information about the number of people visiting specific locations or establishments, providing deep insights into customer behavior, patterns, and trends. This data is crucial for businesses looking to understand their audience and make data-driven decisions.

Core Features:

  1.    Data Collection Methods:
  •    Passive Sensors: Aurora:GeoStudio® integrates data collected from passive sensors deployed at various locations. These devices count the number of visitors, track their movement paths, and record the duration of their visits.
  •    Mobile Devices: The platform also leverages data from mobile devices, providing additional insights into foot traffic patterns through location-based services and applications.
  2.    Population Dynamics® Analytic:
  •    Aurora:GeoStudio®’s Population Dynamics® analytic processes foot traffic data to deliver comprehensive insights. This analytic tool helps visualize and understand visitor behavior, peak visiting times, and movement trends within specific areas.
  3.    Visualization and Mapping:
  •    The platform offers advanced visualization capabilities, displaying foot traffic data on customizable maps from providers like Google, Esri, Open, and Stamen. These visualizations help users understand spatial patterns and relationships, facilitating informed decision-making.

Applications:

  1.    Customer Behavior Analysis:
  •    Businesses can analyze foot traffic data to understand customer behavior, such as the number of visitors, the duration of their visits, and the paths they take within an establishment. This information is crucial for tailoring services and improving customer satisfaction.
  2.    Store Layout Optimization:
  •    Foot traffic data helps businesses optimize store layouts by identifying high-traffic areas and bottlenecks. By understanding how customers move through a space, businesses can rearrange products and displays to enhance flow and maximize sales opportunities.
  3.    Marketing Strategy Enhancement:
  •    Aurora:GeoStudio® enables businesses to refine their marketing strategies by providing insights into peak visiting times and customer demographics. This data supports targeted marketing campaigns, ensuring promotions reach the right audience at the right time.
  4.    Operational Efficiency:
  •    Understanding foot traffic patterns allows businesses to optimize staffing levels, manage inventory more effectively, and improve overall operational efficiency. By aligning resources with actual customer demand, businesses can enhance service delivery and reduce costs.
  5.    Urban Planning and Public Spaces:
  •    Foot traffic data is invaluable for urban planners and managers of public spaces. It helps in designing public areas that accommodate pedestrian flow efficiently and ensures that amenities are accessible and well-placed.

Aurora:GeoStudio®’s support for foot traffic data through the Population Dynamics® analytic offers businesses and urban planners a powerful tool for understanding and optimizing visitor behavior. By leveraging data from sensors, cameras, and mobile devices, the platform provides detailed insights into customer movements and trends. These insights enable businesses to enhance their marketing strategies, optimize store layouts, and improve operational efficiency. For urban planners, foot traffic data facilitates the design of more effective and accessible public spaces. Aurora:GeoStudio®’s advanced features empower users to make informed decisions and achieve a comprehensive understanding of foot traffic dynamics, leading to better strategic outcomes.

Here are a few use cases for this project:

1. Autonomous Vehicles Navigation: The "Carla traffic dataset" can be used to develop and improve algorithms for autonomous vehicles, enabling them to effectively identify other road users, traffic lights, and various traffic signs, improving the cars’ ability to navigate safely in different weather conditions including fog.

2. Traffic Management Systems: The dataset could be leveraged to create advanced traffic management systems, identifying car, bike, or pedestrian movement, detecting traffic light states, and understanding if road users respect speed limits (30, 60, 90 km/h signs). This could improve urban traffic flow and increase overall road safety.

3. Driver Assistance Systems: The dataset could be used to develop advanced driver assistance systems (ADAS) that could alert drivers of pedestrians, other vehicles, traffic signs, and the status of traffic lights, particularly in foggy or difficult conditions.

4. Safety Testing for Vehicle Manufacturers: Companies manufacturing cars, bikes, or motorbikes could use the data to carry out safety testing under different situations, including different weather conditions and traffic light changes.

5. Virtual Driving Simulation: Game developers or driving schools could use this model to develop realistic driving simulations. The players or trainee drivers would need to respond correctly and promptly to real-world traffic situations like recognizing speed signs, traffic lights, and other road users.

This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.

Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.

Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.

The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.

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.

Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

As of December 2023, 5G data traffic in South Korea was in excess of 878 petabytes. As the user numbers of 5G increased, the traffic amount also rose accordingly.

From February to July 2024, February was the month that had the most website traffic to ebay.com. The consumer-to-consumer (C2C) e-commerce website reached a total of over 693 million visits in that month, with the majority being from mobile devices. Popularity on multiple fronts Although eBay is popular on mobile devices, monthly downloads of its mobile app have been trending in the wrong direction since peaking in June 2020 at 5.16 million. Still, in April 2023, ebay.com was the second most popular e-commerce and shopping website worldwide, accounting for more than three percent of visits to sites in this category. Big numbers declining In the second quarter of 2023, eBay’s gross merchandise volume (GMV) amounted to nearly 18.2 billion U.S. dollars. That is no small number, but is only a small increase compared to the lowest GMV recorded by the company since the first quarter of 2020 - 17.7 billion U.S. dollars in the third quarter of 2022 - and that’s not the only figure on the decline for eBay. The e-commerce platform had approximately 138 million active buyers in the second quarter of 2022, and a year later that number was down 4.3 percent to 132 million.

Facebook is a web traffic powerhouse: in March 2024 approximately 16.6 billion visits were measured to the Facebook.com, making it one of the most-visited websites online. In the third quarter of 2023, Facebook had nearly three billion monthly active users.

3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

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.

Traffic Volumes data across Dublin City from the scats traffic management system. The Sydney Coordinated Adaptive Traffic System (scats) is an intelligent transportation system used to manage timing of signal Phases at traffic signals. Scats uses SENSORS at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle SENSORS are Generally Inductive Loops installed within the road. 3 resources are provided: Scats Traffic Volumes Data (Monthly) Contained in this report are traffic Counts taken from the scats traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic Volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Set the traffic volume Counts here are best used to Represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc.). Site: this can be matched with the scats Sites file to show location Detector: the detectors/SENSORS at each site are numbered Sum volume: total traffic Volumes in preceding hour AVG volume: average traffic Volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. Scats Site Location Data Contained in this report, the location data for the scats sites is provided. The meta data provided includes the following; Site id — This is a unique identifier for each junction on scats Site description(CAP) — Descriptive location of the junction containing street name(s) intersecting street streets Site description (lower) — – Descriptive location of the junction containing street name(s) intersecting street streets Region — The area of the city, adjoining local authority, region that the site is located Lat/LONG — Coordinates Disclaimer: the location files are regularly updated to Represent the locations of scats sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or Faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

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).**

Railway: Freight Total Traffic: Zhejiang data was reported at 55.653 Ton mn in 2023. This records an increase from the previous number of 54.530 Ton mn for 2022. Railway: Freight Total Traffic: Zhejiang data is updated yearly, averaging 30.190 Ton mn from Dec 1981 (Median) to 2023, with 43 observations. The data reached an all-time high of 55.653 Ton mn in 2023 and a record low of 14.950 Ton mn in 1981. Railway: Freight Total Traffic: Zhejiang data remains active status in CEIC and is reported by China Railway Corporation, National Railway Administration. The data is categorized under China Premium Database’s Transportation and Storage Sector – Table CN.TC: Railway: Freight Traffic.

Traffic Volumes from SCATS Traffic Management System 2023. Published by Dún Laoghaire-Rathdown County Council. Available under the license cc-by (CC-BY-4.0).Traffic volumes data across Dun Laoghaire Rathdown from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site.

SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows.

Please note this data is for information purposes only and may not be an exact representation of the infrastructure. Changes and upgrades occurring since then may not be represented....

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

This is an urban traffic speed dataset which consists of 214 anonymous road segments (mainly consist of urban expressways and arterials) within two months (i.e., 61 days from August 1, 2016 to September 30, 2016) at 10-minute interval, and the speed observations were collected in Guangzhou, China. In practice, it can be used to conduct missing data imputation, short-term traffic prediction, and traffic pattern discovery experiments.

According to the spatial and temporal attributes, we can easily derive a third-order tensor as \(\mathcal{X}\in\mathbb{R}^{214\times 61\times 144}\) and its dimensions include road segment, day and time window (see the file tensor.mat). The total number of speed observations (or non-zero entries of the tensor \(\mathcal{X}\)) is \(1,855,589\). If the dataset is complete, then we have \(214\times 61\times 144=1,879,776\) observations, therefore, the original missing rate of this dataset is \(1.29\%\).

Note that the file traffic_speed_data.csv is the original traffic speed data with four columns including road segment attribute, day attribute, time window attribute, and traffic speed value. The file day_information_table.csv is a table referring to the specific date, and the file time_information_table.csv is a table expressing time window with start time and end time information.

Feel free to email me with any questions: chenxy346@mail2.sysu.edu.cn (author: Xinyu Chen).

Acknowledgement: Mr. Weiwei Sun (affiliated with Sun Yat-Sen University) also provided insightful suggestion and help for publishing this data set. Thank you!

The total mobile data traffic in the Middle East and North Africa (MENA) region was forecast to increase from 9.1 exabytes per month in 2024 to about 28 exabytes per month by 2030. This represents a triple expansion over the next six years.