Données de trafic routier issues des capteurs permanents sur 13 mois glissants en J-1Sur le réseau parisien, la mesure du trafic s’effectue majoritairement par le biais de boucles électromagnétiques implantés dans la chaussée.Les données historiques sont disponibles dans le jeu de données Comptage routier - Historique - Données trafic issues des capteurs permanentsLa donnée est produite par la Direction de la Voirie et des déplacements - Service des Déplacements - Poste Central d'Exploitation Lutèce.La donnée et les visualisations associées (Tableau, Carte et Dataviz) sont brutes sans aucune interprétation ou analyse. Elles donnent à voir la donnée telle qu'elle est publiée quotidiennement. Elles donnent un aperçu du taux d'occupation et du débit sur plus de 3000 tronçons de voies. A elles seules, elles ne permettent pas de caractériser la complexité de la circulation à Paris.Deux types de données sont ainsi élaborés : le taux d’occupation, qui correspond au temps de présence de véhicules sur la boucle en pourcentage d’un intervalle de temps fixe (une heure pour les données fournies). Ainsi, 25% de taux d’occupation sur une heure signifie que des véhicules ont été présents sur la boucle pendant 15 minutes. Le taux fournit une information sur la congestion routière. L’implantation des boucles est pensée de manière à pouvoir déduire, d’une mesure ponctuelle, l’état du trafic sur un arc. le débit est le nombre de véhicules ayant passé le point de comptage pendant un intervalle de temps fixe (une heure pour les données fournies). L'horodate horaire est effectué en fin de période d'élaboration. Par exemple, l’horodate « 2019-01-01 01:00:00 » désigne la période du 1er janvier 2019 à 00h00 au 1er janvier 2019 à 01h00.Ainsi, l’observation couplée en un point du taux d’occupation et du débit permet de caractériser le trafic. Cela constitue l’un des fondements de l’ingénierie du trafic, et l’on nomme d’ailleurs cela le « diagramme fondamental ».Un débit peut correspondre à deux situations de trafic : fluide ou saturée, d’où la nécessité du taux d’occupation. A titre d’exemple : sur une heure, un débit de 100 véhicules par heure sur un axe habituellement très chargé peut se rencontrer de nuit (trafic fluide) ou bien en heure de pointe (trafic saturé).L’équipement du réseau parisien :Les principaux axes de la Ville de Paris sont équipés de stations de comptage des véhicules et de mesure du taux d’occupation, à des fins à la fois de régulation du trafic et des transports en commun, d’information aux usagers (diffusion sur le site Sytadin), et d’étude.Il existe deux types de stations sur le réseau : les stations de mesure du taux d’occupation seul, et des stations à la fois de mesure du taux et de comptage des véhicules.Les stations de mesure du taux sont implantées très régulièrement : elles permettent une connaissance fine des conditions de circulation.Les stations de débit sont moins nombreuses, et généralement implantées entre les principales intersections. En effet, le débit se conserve généralement sur une section entre deux grands carrefours.Le référentiel :Le référentiel est disponible sur ce jeu de données «Comptage routier - Référentiel géographique » avec les caractéristiques suivantes : Encodage : UTF-8 Projection : EPSG:2154 (Lambert 93 – RGF93) Les champs attributaires sont : voir le modèle de données ci-dessous et la notice en pièce jointe.La donnée de trafic :Les champs attributaires sont : voir le modèle de données ci-dessous et la notice en pièce jointe.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

Snapshot img

Global network traffic analytics Industry Overview

Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

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See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

Companies covered

The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

The report offers a complete analysis of a number of companies including:

Allot
Cisco Systems
IBM
Juniper Networks
Microsoft
Symantec

Network traffic analytics market growth based on geographic regions

Americas
APAC
EMEA

With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

Network traffic analytics market growth based on end-user

Telecom
BFSI
Healthcare
Media and entertainment

According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors such as increasing use of network traffic analytics solutions and increasing use of mobile devices at workplaces will contribute to the growth of the market shares of the telecom industry in the network traffic analytics market.

Key highlights of the global network traffic analytics market for the forecast years 2018-2022:

CAGR of the market during the forecast period 2018-2022
Detailed information on factors that will accelerate the growth of the network traffic analytics market during the next five years
Precise estimation of the global network traffic analytics market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
Growth of the network traffic analytics industry across various geographies such as the Americas, APAC, and EMEA
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive information about factors that will challenge the growth of network traffic analytics companies

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This market research report analyzes the market outlook and provides a list of key trends, drivers, and challenges that are anticipated to impact the global network traffic analytics market and its stakeholders over the forecast years.

The global network traffic analytics market analysts at Technavio have also considered how the performance of other related markets in the vertical will impact the size of this market till 2022. Some of the markets most likely to influence the growth of the network traffic analytics market over the coming years are the Global Network as a Service Market and the Global Data Analytics Outsourcing Market.

Technavio’s collection of market research reports offer insights into the growth of markets across various industries. Additionally, we also provide customized reports based on the specific requirement of our clients.

Polygon layer visualizing the traffic zone system for the Ministry's Greater Golden Horseshoe Model (GGHM) version 4.Creator: Naznin DaisyCouche surfacique pour la visualisation des zones d’analyse de trafic du Modèle de prévision pour la région élargie du Golden Horseshoe du ministère, version 4.

Analysis of ‘Daily traffic indicators France and Regions, COVID-19 ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/615eed271fd73f6703435810 on 12 January 2022.

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

Daily Road Traffic Indicators make it possible to compare the road traffic of all vehicles (ITV or All Vehicles Index) or only heavy goods vehicles (IPL or Index Poids Lourds) with a situation before the COVID-19 crisis.

# # Calculation method

They are constructed by comparing current traffic with pre-crisis traffic based on average daily flow rates between 13 January and 2 February 2020.

This period was chosen in order to avoid the effects of school holidays.

‘0’ therefore represents a ‘pre-crisis’ situation and the curves directly give the observed falls (negative index) or traffic increases (positive index).

Traffic indicators at the level of France and regions are calculated on the basis of traffic data of more than 1200 counting stations spread across the unlicensed national road network and 450 stations spread across the national road network as a whole.

# # Dictionary of variables

— ‘Zone’: Geographical area (e.g. Bourgogne-Franche-Comté, France, etc.) — ‘ITV’: All Vehicles Index (between -1 and 1) — ‘IPL’: Weight Lourds index (between -1 and 1) — ‘MGL_ITV’: Rolling average All vehicles (between -1 and 1) — ‘MGL_IPL’: Rolling Average Weight Lourds (between -1 and 1)

# Data-visualisation

image of DataViz

The [dataviz.cerema.fr/trafic-routier] platform (https://dataviz.cerema.fr/trafic-routier) allows you to explore, visualise and analyse the state of traffic in France, day after day

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

Traffic flow analysis and management are the most effective ways to improve traffic and mitigate its unfortunate consequences. In the field of traffic engineering, traffic and its various aspects are defined by analyzing variables such as quantity, speed, and density. This article addresses the challenge of appropriately dealing with the uncertainty of traffic variables and converting traffic data into understandable verbal expressions for drivers and urban planners. The study utilizes a clustering approach to analyze traffic variables and determine the traffic condition. A new fuzzy clustering method has been developed to enhance the performance of clustering methods, which is then used to detect abnormal traffic conditions on a route based on the value of traffic variables. The algorithm and proposed method have been evaluated on the traffic dataset of a high-traffic route in Tehran, the capital of Iran. The implementation results demonstrate the traffic condition on the selected route divided into six clusters.

Context

Well,the data is taken form the machine hack site.It leads us to the problem of finding the traffic problems in the metro cities. It is also about how to regulate the movement of the cabs so as to get control over the traffic problems.

Content

Modern cities are changing. The rise of vehicular traffic has been changing the design of our cities. It is very important to know how traffic moves in a city and how it changes during different times in a week. Hence it is very important to analyse and gain insights from traffic data. We invite data scientists, analysts and people from all technical interests to analyse the traffic data from Bengaluru. The data gives us some information about how traffic moves from source to destination under various circumstances. The data is sourced from Uber Movement. Uber Movement provides anonymized data from over two billion trips to help urban planning around the world.

Acknowledgements

1. Machine Hack

Inspiration

1. How can we manage day to day traffic ? 2 .How the moments of cabs to be regulated ?
2. Awareness about the Use of public transport.

Dear Scientist!This database contains data collected due to conducting study: "Analysis of the route safety of abnormal vehicle from the perspective of traffic parameters and infrastructure characteristics with the use of web technologies and machine learning" funded by National Science Centre Poland (Grant reference 2021/05/X/ST8/01669). The structure of files is arising from the aims of the study and numerous of sources needed to tailor suitable data possible to use as an input layer for neural network. You can find a following folders and files:1. Road_Parameters_Data (.csv) - which is data colleced by author before the study (2021). Here you can find information about technical quality and types of main roads located in Mazovia province (Poland). The source of data was Polish General Directorate for National Roads and Motorways. 2. Google_Maps_Data (.json) - here you can find the data, which was collected using the authors’ webservice created using the Python language, which downloaded the said data in the Distance Matrix API service on Google Maps at two-hour intervals from 25 May 2022 to 22 June 2022. The application retrieved the TRAFFIC FACTOR parameter, which was a ratio of actual time of travel divided by historical time of travel for particular roads.3. Geocoding_Roads_Data (.json) - in this folder you can find data gained from reverse geocoding approach based on geographical coordinates and the request parameter latlng were employed. As a result, Google Maps returned a response containing the postal code for the field types defined as postal_code and the name of the lowest possible level of the territorial unit for the field administrative_area_level. 4. Population_Density_Data (.csv) - here you can find date for territorial units, which were assigned to individual records were used to search the database of the Polish Postal Service using the authors' original web service written in the Python programming language. The records which contained a postal code were assigned the name of the municipality which corresponded to it. Finally, postal codes and names of territorial units were compared with the database of the Statistics Poland (GUS) containing information on population density for individual municipalities and assigned to existing records from the database.5. Roads_Incidents_Data (.json) - in this folder you can find a data collected by a webservice, which was programmed in the Python language and used for analysing the reported obstructions available on the website of the General Directorate for National Roads and Motorways. In the event of traffic obstruction emergence in the Mazovia Province, the application, on the basis of the number and kilometre of the road on which it occurred, could associate it later with appropriate records based on the links parameters. The data was colleced from 26 May to 22 June 2022.6. Weather_For_Roads_Data (.json) - here you can find the data concerning the weather conditions on the roads occurring at days of the study. To make this feasible, a webservice was programmed in the Python language, by means of which the selected items from the response returned by the www.timeanddate.com server for the corresponding input parameters were retrieved – geographical coordinates of the midpoint between the nodes of the particular roads. The data was colleced for day between 27 May and 22 June 2022.7. data_v_1 (.csv) - collected only data for road parameters8. data_v_2 (.csv) - collected data for road parameters + population density9. data_v_3 (.json) - collected data for road parameters + population density + traffic10. data_v_4 (.json) - collected data for road parameters + population density + traffic + weather + road incidents11. data_v_5 (.csv) - collected VALIDATED and cleaned data for road parameters + population density + traffic + weather + road incidents. At this stage, the road sections for which the parameter traffic factor was assessed to have been estimated incorrectly were eliminated. These were combinations for which the value of the traffic factor remained the same regardless the time of day or which took several of the same values during the course of the whole study. Moreover, it was also assumed that the final database should consist of road sections for traffic factor less than 1.2 constitute at least 10% of all results. Thus, the sections with no tendency to become congested and characterized by a small number of road traffic users were eliminated.Good luck with your research!Igor Betkier, PhD

Do you want to start using Survey 123 but don't feel ready to build your own form? This off the shelf fieldwork survey is for you.This survey allows you to collect and analyse located data for traffic counts.

[Rapport de plus de 197 pages] La taille et la part du marché mondial de l'analyse des transports devraient atteindre 25.5 milliards USD d'ici 2026 et croître à un TCAC de 20.7 % au cours de la période de prévision 2021-2026.

Контролен списък/списъка за социално-демографски, туристически, транспортни и предприемачески набори от отворени данни, използвани в проекта VertiNet за анализ на нуждите на Vertiports — използване и за други въпроси, свързани с мобилността

Възможно е да се използват източниците на данни, включени в контролния списък, в съответствие с „Ръководство за определяне на нуждите от вертипорт в административните зони“

This is the 27th study in a series of annual surveys on road safety conducted since 1981. The study is conducted by Statistics Sweden on behalf of the the Swedish Transport Administration and aims to identify and analyse traffic safety statistics.

The study is is done by a questionnaire directed to a stratified sample of the population. The study from 2008 had a 62,4 percent response rate and a sample of 9 600 randomly selected people aged between 15 and 84 years.

The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 26 questions are used, including descriptive questions, estimation questions and attitude questions.

Purpose:

The survey aims to identify and analyse traffic safety statistics.

This is the 27th study in a series of annual surveys on road safety conducted since 1981. The study is conducted by Statistics Sweden on behalf of the the Swedish Transport Administration and aims to identify and analyse traffic safety statistics.

The study is is done by a questionnaire directed to a stratified sample of the population. The study from 2008 had a 62,4 percent response rate and a sample of 9 600 randomly selected people aged between 15 and 84 years.

The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 26 questions are used, including descriptive questions, estimation questions and attitude questions.

Purpose:

The survey aims to identify and analyse traffic safety statistics.

Réseau linéaire représentant les débits de circulations estimés pour les routes et autoroutes dont la gestion incombe au ministère des Transports et de la Mobilité durable (MTMD). Ces débits sont obtenus selon une méthode statistique d’estimation appliquée sur des données provenant de plus de 4500 sites de collecte répartis sur les principales routes du Québec.

On y retrouve les DJMA (débit journalier moyen annuel), DJME (débit journalier moyen estival (juin, juillet, août, septembre) et DJMH (débit journalier moyen hivernal (décembre, janvier, février, mars) ainsi que d'autres données de trafic. Il est important de noter que ces valeurs sont calculées pour le total des directions de circulation.

Carte interactive :
Certains fichiers sont accessibles en interrogeant une section de trafic à la carte par un clic (les liens des fichiers s’affichent dans le tableau descriptif qui se déploie au clic) :
• Données agrégées historique (PDF)
• Rapports annuels pour les sites permanents (PDF et Excel)
• Données horaires (moyenne horaire par jour de semaine par mois) (Excel)

This is the 20th study in a series of annual surveys on road safety conducted since 1981. The study is conducted by Statistics Sweden on behalf of the Swedish National Road Administration, which had the overall responsibility for road safety in Sweden in 2000. Today the study is conducted on behalf of the Swedish Transport Administration.

The study aims to identify and analyse traffic safety statistics. This is done by a written questionnaire directed to a stratified sample of the population. The study from 2000 had a 71 percent response rate and a sample of 13 044 randomly selected people aged between 15 and 84 years.

The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 24 questions are used, including descriptive questions, estimation questions and attitude questions.

Purpose:

The survey aims to identify and analyse traffic safety statistics.

This is the first study in a series of annual surveys on road safety. The study is conducted by Statistics Sweden on behalf of the Traffic Safety Board, which had the overall responsibility for road safety in Sweden in 1981. The study aims to identify and analyse traffic safety statistics. This is done by a written questionnaire directed to a stratified sample of the population. The study from 1981 had a 79.4 percent response rate and a sample of 3 060 randomly selected people aged between 15 and 74 years. The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 22 questions are used, including descriptive questions, estimation questions and attitude questions. Purpose: The survey aims to identify and analyse traffic safety statistics.

This is the fifth study in a series of annual surveys on road safety conducted since 1981. The study is conducted by Statistics Sweden on behalf of the Traffic Safety Board, which had the overall responsibility for road safety in Sweden in 1985. Today the study is conducted on behalf of the Swedish Transport Administration. The study aims to identify and analyse traffic safety statistics. This is done by a written questionnaire directed to a stratified sample of the population. The study from 1985 had a 81,1 percent response rate and a sample of 4 737 randomly selected people aged between 15 and 84 years. Previous years the age group -75 to 84 years were not included, this group will return in future studies with four to five years. The questions in the study include, for example, traffic behaviour and traffic patterns; attitudes to road safety and traffic rules, the relation between alcohol and traffic and child safety in traffic. Both motor traffic, bicycle and foot traffic is concerned. A total of 30 questions are used, including descriptive questions, estimation questions and attitude questions. Purpose: The survey aims to identify and analyse traffic safety statistics.

Un compteur automatique détecte et enregistre les variations de pression dans un tube creux en caoutchouc qui traverse la chaussée. Ces variations sont dues au passage d’essieux écrasant le tube. Il détermine donc le nombre d’essieux qui passe sur la chaussée. Selon l’écartement entre les essieux, le compteur peut estimer la catégorie (VL ou PL) du véhicule ainsi que la vitesse de passage.Les informations sont enregistrées sous format électronique et se téléchargent ensuite dans des logiciels spécialisés d’analyse du trafic.Les compteurs automatiques sont toujours posés en section courante pendant une période de 2 semaines. En 2019, ils ont été posés entre le 12 juillet 0h et le 25 juillet 24h.Source à citer : Gouvernement de la Nouvelle-Calédonie / D.I.T.T.T.Plus d'informations :Site internet de la DITTT présentant la carte : https://dittt.gouv.nc/reseau-routier/reseau-territorialTéléchargement des données : https://georep-dtsi-sgt.opendata.arcgis.com/datasets/ce95d6b409934a638856baf48735470f/aboutCe jeu de données provient de la Plateforme de téléchargement Géorep.Pour télécharger directement les données au format souhaité, veuillez cliquer sur les liens ci-dessous:Comptage trafic routier - Fiche détaillée Géorep