This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

APISCRAPY, your premier provider of Map Data solutions. Map Data encompasses various information related to geographic locations, including Google Map Data, Location Data, Address Data, and Business Location Data. Our advanced Google Map Data Scraper sets us apart by extracting comprehensive and accurate data from Google Maps and other platforms.

What sets APISCRAPY's Map Data apart are its key benefits:

1. Accuracy: Our scraping technology ensures the highest level of accuracy, providing reliable data for informed decision-making. We employ advanced algorithms to filter out irrelevant or outdated information, ensuring that you receive only the most relevant and up-to-date data.

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3. Customization: We understand that every business has unique needs and requirements. That's why we offer tailored solutions to meet specific business needs. Whether you need data for a one-time project or ongoing monitoring, we can customize our services to suit your needs. Our team of experts is always available to provide support and guidance, ensuring that you get the most out of our Map Data solutions.

Our Map Data solutions cater to various use cases:

1. B2B Marketing: Gain insights into customer demographics and behavior for targeted advertising and personalized messaging. Identify potential customers based on their geographic location, interests, and purchasing behavior.

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Experience the power of APISCRAPY's Map Data solutions today and unlock new opportunities for your business. With our accurate and accessible data, you can make informed decisions, drive growth, and stay ahead of the competition.

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Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated. Data are offered from 30-03.2020 to 9-08-2020. There is another set of data in this portal with the historical series: Traffic. History of traffic data since 2013 In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

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E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

Historical data of traffic measurement points. Each month the data of the previous month are incorporated. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

In 2023, Google Maps was the most downloaded map and navigation app in the United States, despite being a standard pre-installed app on Android smartphones. Waze followed, with 9.89 million downloads in the examined period. The app, which comes with maps and the possibility to access information on traffic via users reports, was developed in 2006 by the homonymous Waze company, acquired by Google in 2013.

Usage of navigation apps in the U.S. As of 2021, less than two in 10 U.S. adults were using a voice assistant in their cars, in order to place voice calls or follow voice directions to a destination. Navigation apps generally offer the possibility for users to download maps to access when offline. Native iOS app Apple Maps, which does not offer this possibility, was by far the navigation app with the highest data consumption, while Google-owned Waze used only 0.23 MB per 20 minutes.

Usage of navigation apps worldwide In July 2022, Google Maps was the second most popular Google-owned mobile app, with 13.35 million downloads from global users during the examined month. In China, the Gaode Map app, which is operated along with other navigation services by the Alibaba owned AutoNavi, had approximately 730 million monthly active users as of September 2022.

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

As global communities responded to COVID-19, we heard from public health officials that the same type of aggregated, anonymized insights we use in products such as Google Maps would be helpful as they made critical decisions to combat COVID-19. These Community Mobility Reports aimed to provide insights into what changed in response to policies aimed at combating COVID-19. The reports charted movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.

Worldwide Traffic Congestion Ranking [between: 19Jun2022 & 26Jun2022]

TCI, calculated only for the center of the tracked location (the city image is split in 9 equal rectangles, forming a 3x3 grid. The central rectangle is taken into consideration when calculating TCI).

Every 20 minutes, the web app saves an image for each tracked location, containing the traffic data reported by Google Maps. After a couple of minutes, the images are analyzed, and the percentages of the 4 traffic colors are calculated.

Let's call these percentages: green → P0 orange → P1 red → P2 dark red → P3

Obviously , the sum of all these percentages is 100: P0 + P1 + P2 + P3 = 100 Based on these percentages, the TCI (Traffic Congestion Index) is calculated:

TCI = (0 * P0) + (1 * P1) + (2 * P2) + (3 * P3)

So the minimum value of TCI is 0, and the maximum value of TCI is 300 (highly improbable to happen). Examples:

APD Traffic Stop data after Oct. 1 2017This layer was updated in 2020 and is the most current data. Data is updated on the first Monday of each month.Metadata: https://docs.google.com/document/d/1iufPcGi7KzqcKC09pI-DfP-pai8wr3Zl5YFvAju-S_Y/

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

Traffic flow data indicates the number of vehicles that passed through a loop detector located on Syggrou Avenue in central Athens, Greece.

Data was collected every 90 seconds over a span of 40 days for a specific location on the map, as depicted in the accompanying Google Maps image (MS225).

Data was provided by the Decentralized Administriation of Attica.

Snapshot img

Digital Map Market Size 2025-2029

The digital map market size is forecast to increase by USD 31.95 billion at a CAGR of 31.3% between 2024 and 2029.

The market is driven by the increasing adoption of intelligent Personal Digital Assistants (PDAs) and the availability of location-based services. PDAs, such as smartphones and smartwatches, are becoming increasingly integrated with digital map technologies, enabling users to navigate and access real-time information on-the-go. The integration of Internet of Things (IoT) enables remote monitoring of cars and theft recovery. Location-based services, including mapping and navigation apps, are a crucial component of this trend, offering users personalized and convenient solutions for travel and exploration. However, the market also faces significant challenges.
Ensuring the protection of sensitive user information is essential for companies operating in this market, as trust and data security are key factors in driving user adoption and retention. Additionally, the competition in the market is intense, with numerous players vying for market share. Companies must differentiate themselves through innovative features, user experience, and strong branding to stand out in this competitive landscape. Security and privacy concerns continue to be a major obstacle, as the collection and use of location data raises valid concerns among consumers.

What will be the Size of the Digital Map Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the market, cartographic generalization and thematic mapping techniques are utilized to convey complex spatial information, transforming raw data into insightful visualizations. Choropleth maps and dot density maps illustrate distribution patterns of environmental data, economic data, and demographic data, while spatial interpolation and predictive modeling enable the estimation of hydrographic data and terrain data in areas with limited information. Urban planning and land use planning benefit from these tools, facilitating network modeling and location intelligence for public safety and emergency management.

Spatial regression and spatial autocorrelation analyses provide valuable insights into urban development trends and patterns. Network analysis and shortest path algorithms optimize transportation planning and logistics management, enhancing marketing analytics and sales territory optimization. Decision support systems and fleet management incorporate 3D building models and real-time data from street view imagery, enabling effective resource management and disaster response. The market in the US is experiencing robust growth, driven by the integration of Geographic Information Systems (GIS), Global Positioning Systems (GPS), and advanced computer technology into various industries.

How is this Digital Map Industry segmented?

The digital map industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Navigation
  Geocoders
  Others


Type

  Outdoor
  Indoor


Solution

  Software
  Services


Deployment

  On-premises
  Cloud


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Indonesia
    Japan
    South Korea


  Rest of World (ROW)

By Application Insights

The navigation segment is estimated to witness significant growth during the forecast period. Digital maps play a pivotal role in various industries, particularly in automotive applications for driver assistance systems. These maps encompass raster data, aerial photography, government data, and commercial data, among others. Open-source data and proprietary data are integrated to ensure map accuracy and up-to-date information. Map production involves the use of GPS technology, map projections, and GIS software, while map maintenance and quality control ensure map accuracy. Location-based services (LBS) and route optimization are integral parts of digital maps, enabling real-time navigation and traffic data.

Data validation and map tiles ensure data security. Cloud computing facilitates map distribution and map customization, allowing users to access maps on various devices, including mobile mapping and indoor mapping. Map design, map printing, and reverse geocoding further enhance the user experience. Spatial analysis and data modeling are essential for data warehousing and real-time navigation. The automotive industry's increasing adoption of connected cars and long-term evolution (LTE) technologies have fueled the demand for digital maps. These maps enable driver assistance app

Spatial dispersion regression results.

Congestion state level.

1st July 2016 Update

WebTRIS Phase 1 is now available and can be accessed at http://webtris.highwaysengland.co.uk

We are in the process of updating the way that traffic flow data is made available to our external users to replace the old TRADS website. The new platform will deliver a more modern experience, utilising Google Maps with count site overlays and bespoke downloadable reporting capabilities. This new service will be referred to as ‘WebTRIS’.

The new development will contain all of the elements users are already familiar with; searching on Site ID’s and reviewing reports based on Site ID’s etc. but will also modernise the look and feel of the product and allow users to select an area of interest by clicking on a map.

Development began in early February 2016 and is expected to be complete in July 2016.

This is a Phase 1 release. A Phase 2 development is planned to take into account user feedback.

On-going updates will be released here with videos showing the product as it grows. There will also be live demonstrations as the product nears go-live and opportunities to take part in User Acceptance Testing and feedback sessions.

We are working hard to improve the level of service that we provide and thank you for your patience while we do so. We will keep you informed on progress with the next update due in May.

This data series provides average journey time, speed and traffic flow information for 15-minute periods since April 2015 on all motorways and 'A' roads managed by Highways England, known as the Strategic Road Network, in England.

Journey times and speeds are estimated using a combination of sources, including Automatic Number Plate Recognition (ANPR) cameras, in-vehicle Global Positioning Systems (GPS) and inductive loops built into the road surface.

Please note that journey times are derived from real vehicle observations and imputed using adjacent time periods or the same time period on different days. Further information is available in 'Field Descriptions' at the bottom of this page.

This data replaces the data previously made available via the Hatris and Trads websites.

Please note that Traffic Flow and Journey Time data prior to April 2015 is still available on the HA Traffic Information (HATRIS) website which can be found at https://www.hatris.co.uk/

Estimated parameters from the GLM.

Context

I wanted to find a better way to provide live traffic updates. We dont all have access to the data from traffic monitoring sensors or whatever gets uploaded from people's smart phones to Apple, Google etc plus I question how accurate the traffic congestion is on Google Maps or other apps. So I figured that since buses are also in the same traffic and many buses stream their GPS location and other data live, that would be an ideal source for traffic data. I investigated the data streams available from many bus companies around the world and found MTA in NYC to be very reliable.

Content

This dataset is from the NYC MTA buses data stream service. In roughly 10 minute increments the bus location, route, bus stop and more is included in each row. The scheduled arrival time from the bus schedule is also included, to give an indication of where the bus should be (how much behind schedule, or on time, or even ahead of schedule).

Acknowledgements

Data is recorded from the MTA SIRI Real Time data feed and the MTA GTFS Schedule data.

Inspiration

I want to see what exploratory & discovery people come up with from this data. Feel free to download this dataset for your own use however I would appreciate as many Kernals included on Kaggle as we can get.

Based on the interest this generates I plan to collect more data for subsequent months down the track.