Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated.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

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 information is updated almost in real time, with a frequency of about 5 minutes, which is the minimum time of several traffic light cycles, necessary to give a real measurement, and that the measurement is not affected in case the traffic light is open or closed. There are other related data sets such as: Oh, traffic. Map of traffic intensity frames, with the same information in KML format, and with the possibility of seeing it in Google Maps or Google Earth. Oh, traffic. Location of traffic measurement points. Traffic data history since 2013 NOTICE: The data structure of the file has been changed by incorporating date, time and coordinates x e and of the measurement. You can view all the traffic information of the City on the Madrid mobility information website, Report: http://informo.munimadrid.es

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.

Crowdsourced Traffic Data Market Outlook

According to our latest research, the global crowdsourced traffic data market size reached USD 3.12 billion in 2024, driven by the rapid digitalization of transportation systems and rising demand for real-time traffic intelligence. The market is expected to grow at a robust CAGR of 15.7% from 2025 to 2033, projecting a market value of USD 10.61 billion by 2033. This impressive growth is primarily attributed to increasing urbanization, the proliferation of connected devices, and the growing emphasis on smart city initiatives worldwide. As per the latest research, the need for efficient traffic management and improved commuter experiences is fueling widespread adoption of crowdsourced traffic data solutions across various sectors.

The expansion of the crowdsourced traffic data market is significantly propelled by the integration of advanced technologies such as artificial intelligence, machine learning, and big data analytics into traffic data platforms. These technologies enable the efficient aggregation and analysis of vast volumes of real-time data sourced from millions of connected devices, including smartphones, GPS units, and vehicle sensors. The continuous evolution of mobile applications and the widespread usage of navigation apps like Google Maps and Waze have contributed to an exponential increase in data points, enhancing the accuracy and reliability of traffic predictions and congestion alerts. As urban populations grow and road networks become more complex, the demand for dynamic, real-time traffic information continues to surge, making crowdsourced data an indispensable asset for both public and private sector stakeholders.

Another critical growth factor is the increasing collaboration between public agencies and private technology providers. Governments and transportation authorities are recognizing the value of crowdsourced data in optimizing traffic flow, reducing congestion, and improving road safety. By leveraging data from diverse sources, these entities can make more informed decisions regarding infrastructure investments, emergency response, and urban planning. The shift towards data-driven governance is further supported by policy frameworks that encourage open data sharing and public-private partnerships. This collaborative ecosystem not only accelerates innovation but also ensures that traffic management solutions are scalable, adaptable, and responsive to evolving urban mobility needs.

The proliferation of Internet of Things (IoT) devices and the deployment of 5G networks are also playing a pivotal role in expanding the market. High-speed connectivity and ubiquitous sensor networks enable the seamless transmission and integration of traffic data from various sources, including vehicles, roadside sensors, and wearable devices. This interconnected infrastructure supports the development of intelligent transportation systems (ITS) capable of real-time monitoring, predictive analytics, and automated incident detection. As cities worldwide invest in smart infrastructure and digital transformation, the adoption of crowdsourced traffic data solutions is expected to become even more widespread, driving sustained market growth over the forecast period.

From a regional perspective, North America currently dominates the global crowdsourced traffic data market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high penetration of smartphones, advanced transportation networks, and supportive regulatory environments in these regions have contributed to the early adoption and rapid expansion of crowdsourced traffic data solutions. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing accelerated growth due to urbanization, increased investments in smart city projects, and rising demand for efficient traffic management systems. The Middle East and Africa are also showing promising potential as governments prioritize digital transformation and infrastructure modernization initiatives.

Data Source Analysis

The data source segment is a cornerstone of the crowdsourced traffic data market, encompassing mobile applications, GPS devices, social media, sensors, and other emerging technologies. Mobile applications represent the largest and most dynamic data source, primarily due to the ubiquity of smartphones and the popularity of navigation and ride-sharing apps. These applications continuously gather

The digital map market, currently valued at $25.55 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 13.39% from 2025 to 2033. This expansion is fueled by several key drivers. The increasing adoption of location-based services (LBS) across diverse sectors like automotive, logistics, and smart city initiatives is a primary catalyst. Furthermore, advancements in technologies such as AI, machine learning, and high-resolution satellite imagery are enabling the creation of more accurate, detailed, and feature-rich digital maps. The shift towards cloud-based deployment models offers scalability and cost-effectiveness, further accelerating market growth. While data privacy concerns and the high initial investment costs for sophisticated mapping technologies present some challenges, the overall market outlook remains overwhelmingly positive. The competitive landscape is dynamic, with established players like Google, TomTom, and ESRI vying for market share alongside innovative startups offering specialized solutions. The segmentation of the market by solution (software and services), deployment (on-premise and cloud), and industry reveals significant opportunities for growth in sectors like automotive navigation, autonomous vehicle development, and precision agriculture, where real-time, accurate mapping data is crucial. The Asia-Pacific region, driven by rapid urbanization and technological advancements in countries like China and India, is expected to witness particularly strong growth. The market's future hinges on continuous innovation. We anticipate a rise in the demand for 3D maps, real-time updates, and integration with other technologies like the Internet of Things (IoT) and augmented reality (AR). Companies are focusing on enhancing the accuracy and detail of their maps, incorporating real-time traffic data, and developing tailored solutions for specific industry needs. The increasing adoption of 5G technology promises to further boost the market by enabling faster data transmission and real-time updates crucial for applications like autonomous driving and drone delivery. The development of high-precision mapping solutions catering to specialized sectors like infrastructure management and disaster response will also fuel future growth. Ultimately, the digital map market is poised for continued expansion, driven by technological advancements and increased reliance on location-based services across a wide spectrum of industries. Recent developments include: December 2022 - The Linux Foundation has partnered with some of the biggest technology companies in the world to build interoperable and open map data in what is an apparent move t. The Overture Maps Foundation, as the new effort is called, is officially hosted by the Linux Foundation. The ultimate aim of the Overture Maps Foundation is to power new map products through openly available datasets that can be used and reused across applications and businesses, with each member throwing their data and resources into the mix., July 27, 2022 - Google declared the launch of its Street View experience in India in collaboration with Genesys International, an advanced mapping solutions company, and Tech Mahindra, a provider of digital transformation, consulting, and business re-engineering solutions and services. Google, Tech Mahindra, and Genesys International also plan to extend this to more than around 50 cities by the end of the year 2022.. Key drivers for this market are: Growth in Application for Advanced Navigation System in Automotive Industry, Surge in Demand for Geographic Information System (GIS); Increased Adoption of Connected Devices and Internet. Potential restraints include: Complexity in Integration of Traditional Maps with Modern GIS System. Notable trends are: Surge in Demand for GIS and GNSS to Influence the Adoption of Digital Map Technology.

Discover the booming map navigation service market! This comprehensive analysis reveals key trends, growth drivers, and competitive landscapes, projecting a 15% CAGR through 2033. Learn about leading players, regional market shares, and the impact of emerging technologies like autonomous driving.

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/

Problem statement 1 - Evaluating the traffic congestion shown by map engine services against the calculated actual congestion using the Drone. Expected Solution - Map engine services like Google, Tom Tom have traffic congestion data which is based on the number of GPS devices used. Using Drone for geo fenced area or a corridor to calculate the actual congestion. Evaluate the calculated drone based congestion against the congestion shown by the map engine services and suggesting the level of accuracy. Data to be given – 1. Geo fence area or a corridor. 2. Map engine services congestion data. 3. Drone footage for the area or corridor. Problem statement 2 - Mechanism to measure the traffic flow optimization at each junction once signal time optimization is achieved. Expected Solution - Based on Vehicle actuated signals / Queue length using camera or radars, the signal optimal time is calculated. The generated Green time (optimal signal time) from the junction handles how many vehicles during the cycle time? How many vehicles it could handle in an ideal situation when traffic discipline is followed in the same Green time? Comparison and analysis of both the outcomes to provide actionable inputs on achieving the traffic flow optimization vis-a-vis signal optimal time.

Data to be given – 1. Video footage of a junction. 2. Vehicle actuated signal / queue length using cameras or radars. Problem statement - Detecting any bottlenecks like parking /encroachment of road & footpath / road condition (pothole, bad road, road marking) in vicinity of a junction which result in congestion and reduce the traffic flow.
Expected Solution - Physical identification of all such bottleneck surrounding 100 Mts of a junction and generating an alert for communicating to control room, concern PS and nearest junction officials. Evaluating the corrective measure taken on each alert generated surrounding the junction on the predefined time intervals. The predefined interval varies from type of bottlenecks. Data to be given – 1. Video footage of the junction. 2. Predefined intervals for each bottleneck type like a. Parking – Every 15 mins b. Encroachment on road/footpath – Every 30 mins c. Potholes / road markings – Every 24 hrs d. Bad road – Every 1 week.

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.

This dataset was developed for the project of analyzing the transport network in the Mazowieckie Voivodeship and comprises a wide range of traffic-related information. The data were collected from various sources, including road technical quality and road incident data from the Polish General Directorate for National Roads and Motorways, travel time information from Google Maps, data obtained from reverse geocoding, population density data from the GUS database, and specific weather conditions for roads.

Key Features of the Dataset:

Multidimensional Information: The dataset includes information on the date, days of the week, holidays, time (in minutes), and various temporal parameters (T1 - T24).

Road and Node Identifiers: Each record contains identifiers for the road (roadId), the start node (start_node), and the end node (end_node).

Traffic Factors: It includes key traffic information such as the traffic factor (trafficFactor), midlongitude and midlatitude of the road segment (midLongitude, midLatitude), and details about the number of lanes, road width, presence of two-way traffic (two_ways), and traffic density (density).

Weather Conditions: The dataset accounts for various weather conditions, including heavy rain, partial rain, no rain, partial clouds, heavy clouds, clear sky, storms, and fog.

Prediction Outcomes: Data include results on traffic speed (result_speed) and conditions such as shuttle traffic (result_shuttle), full (result_fullyclosed) and partial (result_partiallyclosed) road closures, and the presence of traffic lights (result_trafficlight).

Data Collection Period:

Traffic data were collected from May 25, 2022, to June 22, 2022, providing a comprehensive view of traffic conditions over a nearly one-month period. Data Preparation Process:

The collected data were unified and processed to create one large CSV file. This file was then divided into 384 smaller files, each representing the state of the transport network at a specific moment. This dataset forms a comprehensive basis for analyzing and forecasting traffic conditions, offering extensive possibilities for use in machine learning models.

All 311 Service Request from 2010 to Present. Starting in November 2011 the data will be updated on a daily basis.

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.

1. This dataset describes the spatial locations of river flow observation stations managed by the Water Resources Agency, and is provided for use by government agencies, private organizations, groups commissioned by government agencies, and academic units. The field is displayed as the name of the discontinued observation station, and the geographic spatial data is in point form, with a total of 339 records. 2. All river flow stations under the Water Resources Agency have adopted automatic recording to observe water levels, and then converted them into flow rates according to the water level-flow rate curve. 3. This data only includes basic information of the observation stations, excluding the actual observation results of time series data. 4. When opening this file using Google Earth software, clicking on the point information will provide a link to the flow data, opening a separate browser window to the Water Resources Agency Geographic Information Repository website, which provides more functionality and queries, including spatial information, time series data, and environmental analysis. 5. Due to the base map imagery provided by Google not undergoing precise orthorectification, some errors may occur during layer stacking.

This data was collected for an academic project to analyze the performance of an existing Convolutional Neural Network that is able to identify German Traffic Signs (https://data-flair.training/blogs/python-project-traffic-signs-recognition/). 6 traffic signs recognized in British Columbia were added to the CNN. All data was collected through screenshotting Google Maps Street View images for Richmond, Surrey, Delta, North Vancouver, and West Vancouver. Greenshot was used in taking the screenshots.

6 Traffic Signs Include (at least 270+ images)

• Merging Traffic Ahead
• Pedestrian Crossing
• Maximum 50km/h
• Obstruction - Keep Right or Left*
• Obstruction - Keep Right*
• Obstruction - Keep Left*

*Data Augmentation was performed. "Obstruction - Keep Right or Left" images were copied and mirrored. "Obstruction - Keep Right" and "Obstruction - Keep Left" were copied, mirrored, and added to the other's folders.

2 Traffic Signs that were not included in the project, but uploaded (fewer images):

• Maximum 30km/h
• Playground nearby

Spatial dispersion regression results.

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

Crowdsourced Speed Limit Data Market Outlook

According to our latest research, the global Crowdsourced Speed Limit Data market size stands at USD 1.32 billion in 2024, with a robust compound annual growth rate (CAGR) of 17.8% projected from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 6.51 billion. This impressive growth is primarily driven by the increasing adoption of connected vehicles, advancements in real-time navigation systems, and the rising demand for accurate road and traffic data across various sectors.

One of the primary growth factors fueling the expansion of the Crowdsourced Speed Limit Data market is the proliferation of mobile devices and navigation applications. The widespread usage of smartphones equipped with GPS and location-based services has made it easier than ever to collect and share speed limit data in real time. This democratization of data collection not only enhances the accuracy of mapping platforms but also supports a dynamic ecosystem where users contribute to and benefit from up-to-date road information. Furthermore, the integration of crowdsourced data into popular navigation apps such as Google Maps and Waze has set new standards for user expectations, pushing other industry players to adopt similar approaches and fueling further market growth.

Another significant driver is the rapid development of autonomous and connected vehicles. For autonomous vehicles to operate safely and efficiently, they require access to highly accurate and current speed limit information. Crowdsourced speed limit data, constantly updated by millions of users and vehicles, offers a scalable solution that traditional mapping methods cannot match. Automotive OEMs are increasingly integrating this data into their advanced driver-assistance systems (ADAS) and infotainment platforms, enhancing both safety and user experience. The synergy between automotive innovation and crowdsourced data is expected to remain a key catalyst for market expansion through the forecast period.

In addition, the growing emphasis on traffic management and road safety initiatives by government agencies worldwide is propelling the Crowdsourced Speed Limit Data market. Authorities are leveraging crowdsourced data to enhance their traffic monitoring capabilities, optimize traffic flow, and reduce accident rates. The ability to gather granular, real-time speed limit information from a diverse pool of contributors enables more responsive and data-driven policy decisions. As governments increasingly collaborate with technology providers and automotive OEMs, the adoption of crowdsourced speed limit data is anticipated to accelerate, further strengthening the market’s growth trajectory.

From a regional perspective, North America currently leads the market, closely followed by Europe and the Asia Pacific. The presence of major technology companies, high smartphone penetration, and advanced transportation infrastructure have positioned North America at the forefront of this market. Meanwhile, Europe’s strict regulatory environment and focus on road safety have driven significant adoption across the continent. The Asia Pacific region is emerging as a high-growth market due to rapid urbanization, increasing vehicle ownership, and government investments in smart transportation systems. As these regions continue to innovate and expand their digital ecosystems, their contributions to the global crowdsourced speed limit data market will become even more pronounced.

Data Source Analysis

The Data Source segment is a cornerstone of the Crowdsourced Speed Limit Data market, encompassing mobile applications, navigation devices, automotive OEMs, government platforms, and other sources. Mobile applications represent the largest and fastest-growing sub-segment, thanks to the ubiquity of smartphones and the widespread adoption of GPS-enabled apps. These applications allow users to report and validate speed limits, feeding real-time information into

The real-time maps market is experiencing robust growth, driven by the increasing adoption of connected vehicles, the proliferation of smartphones with advanced location services, and the rising demand for precise navigation and location-based services across various sectors. The market, estimated at $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $50 billion by 2033. Key growth drivers include the integration of real-time maps into autonomous driving systems, the expansion of smart city initiatives reliant on accurate location data, and the growing popularity of location-based mobile applications. Companies like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Sanborn, and Baidu are key players in this dynamic market, continually innovating to provide enhanced map features and data accuracy. Competitive pressures are high, with a focus on data quality, coverage, and the integration of advanced technologies like AI and machine learning for improved traffic prediction and route optimization. While the market presents significant opportunities, challenges remain. Data security and privacy concerns, the need for continuous map updates to account for dynamic road conditions, and the high infrastructure costs associated with data collection and processing are some of the key restraints. Market segmentation is primarily based on technology (cloud-based vs. on-premise), application (automotive, navigation, logistics), and geography. North America and Europe currently hold a significant market share, but the Asia-Pacific region is poised for rapid growth fueled by increased smartphone penetration and burgeoning e-commerce activities that heavily rely on accurate location data. The future of the real-time maps market hinges on the continuous improvement of map accuracy, the integration of advanced technologies, and the effective addressal of data privacy and security concerns.