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Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated.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

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/

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.

This map data product delivers high-quality, real-time, and historical GPS datasets for the Oceania region, covering Australia, New Zealand, and key Pacific territories. Designed for geospatial professionals, mobility analysts, and commercial decision-makers, this dataset provides unmatched accuracy for mapping, infrastructure planning, and location intelligence.

Data Attributes Latitude & longitude coordinates Timestamp (epoch & formatted date) Device ID (MAID: IDFA/GAID) Country code (ISO3) Horizontal accuracy (85% fill rate) Optional: IP address, device model, mobile carrier

Access & Delivery Query Method: Polygon queries (up to 10,000 tiles) for granular geographic targeting Delivery Cadence: Hourly or daily Formats: JSON, CSV, Parquet (Snappy/GZIP compression) Endpoints: AWS S3, Google Cloud, or API Historical Coverage: Since September 2024 Freshness: 95% of events delivered within 3 days

Compliance & Customization Fully GDPR and CCPA compliant Clear privacy notices and user opt-in/out controls Credit-based pricing to support flexible scaling Optional custom schema mapping and folder structures

Applications High-accuracy map creation and POI mapping Urban development and transport network planning Retail site analysis and catchment optimization Environmental and disaster response mapping Logistics, fleet management, and supply chain planning Telecom tower placement and coverage optimization

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.

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.

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

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