With approximately 122 million website visits in October 2024, sportybet.com had the most traffic of any gambling website worldwide. Headquartered in Ghana, sportybet.com had more than 10 million visits than second-placed stake.com that month.

All the sections from the BD Topo forming the network of roads with high traffic for departmental use. The main roads defined in Article L. 110-3 of the Highway Code are:(a) The national roads defined in Article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above;(b) The roads listed in the Decree in force (c) The shoulder straps connecting either two sections of highways with high traffic or a section of road with high traffic and a motorway. “Bretelle” means a route connecting two roads which cross at different levels. this is the layer of its roads with large traffic provided for in Decree No 2010-578 of 31 May 2010.

This dataset contains measures of traffic volume per census tract and ZIP code tabulation area (ZCTA) in the United States from 1963 to 2019 (primarily 1997 to 2019). High traffic volume may be used as a proxy for heavy traffic, high traffic speeds, and impediments to walking or biking. The dataset contains measures of the average, maximum, and minimum traffic volume per year or per ZCTA per year. These figures are available for all streets, highways, and non-highways. In the ZCTA dataset, data is collected intermittently across locations over time, therefore traffic volume has been interpolated for years in which no measures are available. Data Source: Traffic volume measurements are derived from Kalibrate's TrafficMetrix database accessed via Esri Demographics. Census tract boundaries come from the 2010 TIGER/Line shapefiles. ZCTA boundaries come from the 2019 TIGER/Line shapefiles.

This page provides information for the High Severity Traffic Crashes performance measure.

Dataset Description:Based on some real-world events

Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes as well as access to the data sets and any supplemental data. Click on the Showcases tab for visual representations of this data. The Engineering and Transportation Department uses this data to improve safety in Tempe.This page provides data for the High Severity Traffic Crashes performance measure. City of Tempe crash data summarized to show fatal and serious injury crashes by year.The performance measure dashboard is available at 1.08 High Severity Traffic CrashesAdditional Information Source: Arizona Department of Transportation (ADOT)Contact:  Julian DresangContact E-Mail:  Julian_Dresang@tempe.govData Source Type:  CSV files and Excel spreadsheets can be downloaded from ADOT websitePreparation Method:  Data is sorted to remove license plate numbers and other sensitive informationPublish Frequency:  MonthlyPublish Method:  ManualData Dictionary

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

This dataset represents the 2018 federal Highway Performance Monitoring System (HPMS) 2018 data for roads with traffic greater than 25,000.

N_RGCC_L_019 Assembling the sections from the Topo BD of the IGN to form the network of high-traffic roads according to Decree No 2010-578 of 31 May 2010 amending Decree No 2009-615 of 3 June 2009 establishing the list of high-traffic roads. The highways defined in Article L. 110-3 of the Highway Code are: the national roads defined in article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above; the roads listed in the annex to the decree in force the shoulder straps connecting either two sections of highways with high traffic, or a section of the highway with high traffic and one motorway. “Bretelle” means a route connecting two roads which cross at different levels.

All the sections from the BD Topo forming the network of roads with high traffic for departmental use. The highways defined in Article L. 110-3 of the Highway Code are: the national roads defined in article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above; the roads listed in the annex to the decree in force the shoulder straps connecting either two sections of highways with high traffic, or a section of the highway with high traffic and one motorway. “Bretelle” means a route connecting two roads which cross at different levels.

A high-traffic road is a road defined by Article 22 of the Law on Freedoms and Local Responsibilities of 2004: they ensure the continuity of the main routes and justify specific traffic police rules.

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.

Analysis of ‘1.08 High Severity Traffic Crashes (summary)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/cd29e892-b0a8-4b4e-8b5e-3f81a9df49a2 on 11 February 2022.

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

Fatal and serious injury crashes are not “accidents” and are preventable. The City of Tempe is committed to reducing the number of fatal and serious injury crashes to zero. This data page provides details about the performance measure related to High Severity Traffic Crashes as well as access to the data sets and any supplemental data. Click on the Showcases tab for visual representations of this data. The Engineering and Transportation Department uses this data to improve safety in Tempe.

This page provides data for the High Severity Traffic Crashes performance measure.

City of Tempe crash data summarized to show fatal and serious injury crashes by year.

The performance measure dashboard is available at 1.08 High Severity Traffic Crashes

Additional Information

Source: Arizona Department of Transportation (ADOT)

Contact:  Julian Dresang

Contact E-Mail:  Julian_Dresang@tempe.gov

Data Source Type:  CSV files and Excel spreadsheets can be downloaded from ADOT website

Preparation Method:  Data is sorted to remove license plate numbers and other sensitive information

Publish Frequency:  Monthly

Publish Method:  Manual

Data Dictionary

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

The Traffic-Net dataset, released in the version 1.0, contains 4,400 images of sparse traffic, dense traffic, accident, and fire. This dataset can be used for various computer vision tasks, including object detection, image classification, and segmentation.

The images in the dataset are of varying sizes and resolutions, and were collected from different sources, including Google Images, Bing Images, and Flickr. The dataset is divided into four classes, each with a distinct set of images and labels:

1. Sparse traffic: This class contains images of traffic signs and signals in low-traffic areas, such as rural roads and small towns.

2. Dense traffic: This class contains images of traffic signs and signals in high-traffic areas, such as urban roads and highways.

3. Accident: This class contains images of traffic accidents and related objects, such as damaged cars and emergency services.

4. Fire: This class contains images of fire-related objects, such as burning vehicles and buildings.

Researchers and developers can use the Traffic-Net dataset to train and evaluate their own models for traffic sign recognition and related tasks. The dataset can also be used to benchmark existing models and compare their performance on this specific dataset.

HCAADT represents current (most recent) Heavy Commercial Annual Average Daily Traffic on sampled road systems. This information is displayed using the Traffic Count Locs Active feature class as of the annual HPMS freeze in January. Historical HCAADT 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: John Hackett, 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 Heavy Commercial Annual Average Daily Traffic Locations Information.


Link to ESRI Feature Service:

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

All sections of roads forming the network of high-traffic roads for national use.

The highways defined in Article L. 110-3 of the Highway Code are: the national roads defined in article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above; the roads listed in the annex to the decree in force the shoulder straps connecting either two sections of highways with high traffic, or a section of the highway with high traffic and one motorway. “Bretelle” means a route connecting two roads which cross at different levels.

All the sections from the BD Topo forming the network of roads with high traffic for departmental use. The main roads defined in Article L. 110-3 of the Highway Code are:(a) The national roads defined in Article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above;(b) The roads listed in the Decree in force (c) The shoulder straps connecting either two sections of highways with high traffic or a section of road with high traffic and a motorway. “Bretelle” means a route connecting two roads which cross at different levels. this is the layer of its roads with large traffic provided for in Decree No 2010-578 of 31 May 2010.

This operations dashboard shows historic and current data related to this performance measure.The performance measure dashboard is available at 1.08 High Severity Traffic Crashes. Data Dictionary

Our dataset gives access to the most precise data thanks to the power of our advanced algorithms. We use massive, precise and representative geolocation data from mobile applications that we aggregate, standardize and couple with manual counts to offer the most reliable analysis. This data product contains footfall data as well as shopping center names, city, postal code and geographies for shopping centers in Belgium / England / France / Germany / Italy / Netherlands / Spain, over the past several years. Use Cases: Foot Traffic Analytics Foot Traffic Analytics Territory Planning Gain detailed insights into pedestrian traffic across diverse locations, such as addresses, shopping centers, and shopping areas, to make strategic decisions for your location strategy. Identify high-traffic areas to optimize site selection and expansion plans. Competition Analytics Benchmark footfall within the shopping centers of your competitors, enabling informed business decisions. Understand competitor performance and identify opportunities for market share growth by analyzing comparative traffic patterns. Marketing Targeting Enhance your marketing strategies by analyzing footfall data to identify high-traffic areas and peak times. Target your marketing and promotional efforts more effectively by understanding where and when to reach your audience, maximizing engagement and conversion rates.. Urban and Infrastructure Planning Support urban and infrastructure planning by providing data on pedestrian traffic flows. Help city planners and developers design more efficient public spaces, transportation hubs, and commercial areas by understanding how people move through different environments.

All the sections from the BD Topo forming the network of roads with high traffic for departmental use. The main roads defined in Article L. 110-3 of the Highway Code are:(a) The national roads defined in Article L. 123-1 of the Highways Code and referred to by the Decree of 5 December 2005 referred to above;(b) The roads listed in the Decree in force (c) The shoulder straps connecting either two sections of highways with high traffic or a section of road with high traffic and a motorway. “Bretelle” means a route connecting two roads which cross at different levels. this is the layer of its roads with large traffic provided for in Decree No 2010-578 of 31 May 2010.