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.

Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated.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.

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.

This Mobile Location Data product provides extensive coverage of North America with precise timestamped GPS coordinates from opted in mobile devices. Data is available both in real time and historically, enabling detailed analysis of movement patterns, foot traffic trends and location based behavior.

The dataset is sourced via partnerships with established app publishers, ensuring accuracy, scale and full privacy compliance. Each record contains latitude, longitude, event timestamp and optional device metadata, making it adaptable for operational monitoring and strategic market research.

Included attributes: Latitude & Longitude coordinates Event timestamp (epoch & date) Mobile Advertising ID (IDFA/GAID) Horizontal accuracy (~85% fill rate) Country code (ISO3) Optional metadata: IP address, carrier, device model Access & Delivery Delivered via API with polygon queries (up to 10,000 tiles)

Formats: JSON, CSV, Parquet Supports API, AWS S3, or Google Cloud Storage delivery Hourly or daily refresh options Historical coverage starting September 2024 Flexible, credit-based query pricing Privacy & Compliance Fully compliant with GDPR and CCPA Clear privacy notices with every data source Robust opt-in/opt-out user controls

Use Cases Retail expansion & site selection Audience segmentation & behavioral analysis Urban mobility planning & infrastructure optimization DOOH / OOH campaign performance measurement Geofencing for targeted marketing campaigns Tourism & event attendance mapping

This webmap shows average traffic speed of major roads in Hong Kong. It is made available by the Transport Department under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XML web service and been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.

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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

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.

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

According to market research, the global Transportation Predictive Analytics Market is expected to reach USD XX Billion in 2024 and is projected to grow to USD XX Billion by 2033, expanding at a CAGR of XX% during the forecast period. MARKET DYNAMICS: KEY DRIVERS

Advancements in AI-Driven Traffic Management

One of the primary drivers of the transportation predictive analytics market is the integration of artificial intelligence (AI) into traffic management systems. With the rise in urban congestion, cities are adopting AI-driven predictive analytics to enhance traffic flow, reduce travel time, and minimize accidents. By leveraging historical data, sensor inputs, and machine learning algorithms, these systems can anticipate traffic patterns and suggest real-time route modifications. For instance, Singapore's Land Transport Authority has implemented AI-based predictive analytics to optimize traffic signals dynamically. The system continuously assesses traffic density and adjusts signal timings to alleviate congestion, leading to smoother commutes and improved road efficiency. Additionally, companies like Google, through its AI-powered navigation tool Google Maps, use predictive models to suggest alternative routes based on real-time traffic conditions, further demonstrating the impact of AI in transportation.

Restraints of Transportation Predictive Analytics Market

Data Privacy and Cybersecurity Concerns

One of the key challenges facing the adoption of predictive analytics in transportation is data privacy and cybersecurity risks. Since predictive analytics relies on vast amounts of real-time data from vehicles, passengers, and infrastructure, the risk of data breaches and unauthorized access remains a major concern. For instance, in 2021, a cyberattack on the Colonial Pipeline in the United States highlighted the vulnerability of transportation infrastructure to security threats. The attack disrupted fuel supply chains, showcasing how transportation networks can be significantly impacted by cybersecurity incidents. With increasing concerns over data misuse, regulatory bodies such as the European Union’s General Data Protection Regulation (GDPR) have implemented strict guidelines on how transportation companies handle and store data. As a result, transportation firms must invest heavily in cybersecurity solutions to ensure data protection and compliance, which could slow down market adoption.

Introduction of Transportation Predictive Analytics Market

Transportation predictive analytics utilizes machine learning, big data, and artificial intelligence to optimize transportation systems, enhance safety, reduce costs, and improve operational efficiency. The growing adoption of IoT, smart transportation initiatives, and increasing urbanization are key factors driving the market growth. Predictive analytics enables transportation authorities and companies to make data-driven decisions, improving overall performance and sustainability.

Used within the Travellers Road Information Portal Interactive Map to convey transportation related information in both official languages. Camera images are available in real time on certain highways within Central, Eastern & West Ontario. This data is best viewed using Google Earth or similar Keyhole Markup Language (KML) compatible software. For instructions on how to use Google Earth, read the Google Earth tutorial . This data set is now available via the Ontario 511 Developer API at *[KML]: Keyhole Markup Language

The Geospatial Analytics Market size was valued at USD 79.06 USD billion in 2023 and is projected to reach USD 202.74 USD billion by 2032, exhibiting a CAGR of 14.4 % during the forecast period. The growing adoption of location-based technologies and the increasing need for data-driven decision-making in various industries are key factors driving market growth. Geospatial analytics captures, produces and displays GIS (geographic information system)-maps and pictures that may be weather maps, GPS or satellite photos. The geospatial analysis as a tool works with state of art technology in every formats namely; the GPS, sensors that locates, social media, mobile devices, multi of the satellite imagery to produce data visualizations that are facilitating trend-finding in complex relations between people and places as well are the situations' understanding. Visualizations are depicted through the use of maps, graphs, figures, and cartograms that illustrate the entire historical picture as well as a current changing trend. This is why the forecast becomes more confident and the situation is anticipated better. Recent developments include: February 2024: Placer.ai and Esri, a Geographic Information System (GIS) technology provider, partnered to empower customers with enhanced analytics capabilities, integrating consumer behavior analysis. Additionally, the agreement will foster collaborations to unlock further features by synergizing our respective product offerings., December 2023: CKS and Esri India Technologies Pvt Ltd teamed up to introduce the 'MMGEIS' program, focusing on students from 8th grade to undergraduates, to position India as a global leader in geospatial technology through skill development and innovation., December 2023: In collaboration with Bayanat, the UAE Space Agency revealed the initiation of the operational phase of the Geospatial Analytics Platform during its participation in organizing the Space at COP28 initiatives., November 2023: USAID unveiled its inaugural Geospatial Strategy, designed to harness geospatial data and technology for more targeted international program delivery. The strategy foresees a future where geographic methods enhance the effectiveness of USAID's efforts by pinpointing development needs, monitoring program implementation, and evaluating outcomes based on location., May 2023: TomTom International BV, a geolocation technology specialist, expanded its partnership with Alteryx, Inc. Through this partnership, Alteryx will use TomTom’s Maps APIs and location data to integrate spatial data into Alteryx’s products and location insights packages, such as Alteryx Designer., May 2023: Oracle Corporation announced the launch of Oracle Spatial Studio 23.1, available in the Oracle Cloud Infrastructure (OCI) marketplace and for on-premises deployment. Users can browse, explore, and analyze geographic data stored in and managed by Oracle using a no-code mapping tool., May 2023: CAPE Analytics, a property intelligence company, announced an enhanced insurance offering by leveraging Google geospatial data. Google’s geospatial data can help CAPE create appropriate solutions for insurance carriers., February 2023: HERE Global B.V. announced a collaboration with Cognizant, an information technology, services, and consulting company, to offer digital customer experience using location data. In this partnership, Cognizant will utilize the HERE location platform’s real-time traffic data, weather, and road attribute data to develop spatial intelligent solutions for its customers., July 2022: Athenium Analytics, a climate risk analytics company, launched a comprehensive tornado data set on the Esri ArcGIS Marketplace. This offering, which included the last 25 years of tornado insights from Athenium Analytics, would extend its Bronze partner relationship with Esri. . Key drivers for this market are: Advancements in Technologies to Fuel Market Growth. Potential restraints include: Lack of Standardization Coupled with Shortage of Skilled Workforce to Limit Market Growth. Notable trends are: Rise of Web-based GIS Platforms Will Transform Market.

Traffic census for the Queensland state-declared road network showing annual average traffic counts and heavy vehicle counts.

The Digital Moving Map System (DMMS) market is experiencing robust growth, driven by increasing demand across diverse sectors like aviation, automotive, and maritime. The market's expansion is fueled by advancements in GPS technology, enhanced map data visualization, and the integration of sophisticated features like real-time traffic updates, weather overlays, and obstacle avoidance systems. The integration of DMMS into autonomous vehicles and advanced driver-assistance systems (ADAS) is a significant growth catalyst. Furthermore, the rising adoption of DMMS in military and defense applications for enhanced situational awareness is contributing to market expansion. While the precise market size for 2025 is not provided, a reasonable estimation, considering a projected CAGR (let's assume a conservative 8% based on industry trends) and a value unit in millions, would place the market value in the range of $2.5 to $3 billion. This signifies substantial growth from the historical period (2019-2024). The segmentation by application (aircraft, cars, ships, others) and type (2D, 3D) reflects diverse user needs and technological advancements, with 3D systems anticipated to witness faster adoption due to their improved spatial representation. Geographic segmentation reveals strong growth in North America and Asia Pacific regions, driven by technological advancements and infrastructure development. However, factors such as high initial investment costs and potential cybersecurity vulnerabilities can act as restraints on wider market penetration. The competitive landscape is characterized by a mix of established players like Becker Avionics, L3Harris, and HENSOLDT, along with emerging technology companies like Google, Baidu, and Alibaba, integrating DMMS into their broader ecosystem. This competition is driving innovation and reducing prices, making DMMS technology more accessible across various sectors. Future growth will hinge on technological breakthroughs such as augmented reality (AR) integration, improved data processing capabilities, and the development of more resilient and secure systems. The market is expected to continue its upward trajectory throughout the forecast period (2025-2033), driven by increased adoption in both commercial and military applications. The forecast period's market size should see considerable growth, potentially reaching $5 - $7 billion by 2033, based on the aforementioned CAGR and market dynamics.

Vector polygon map data of highway mileposts from Oregon, containing 7696 features.

Mile marker GIS data consists of points along a linear feature, such as roads or railways. They serve as reference points to measure distances along these features. Mile markers are often labeled with numbers indicating their distance from a starting point, such as a highway's origin or a railway station.

These markers are invaluable for navigation, route planning, emergency response, and data collection. For example, they help drivers and emergency services identify their location precisely on a road. In transportation planning, mile markers aid in analyzing traffic patterns, determining optimal routes, and estimating travel times. Additionally, they facilitate maintenance activities by providing clear reference points for inspecting and repairing infrastructure.

This data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.