As of October 2020, the average amount of mobile data used by Apple Maps per 20 minutes was 1.83 MB, while Google maps used only 0.73 MB. Waze, which is also owned by Google, used the least amount at 0.23 MB per 20 minutes.

This dataset provides real-time traffic and navigation data from Waze. It includes traffic alerts, accidents, road hazards, police presence, traffic jams, and driving directions. The data is sourced directly from Waze/Google in real-time, making it ideal for applications requiring current traffic conditions and route optimization. The dataset is delivered in a JSON format via REST API.

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.

The dataset was created in partnership with Waze for the Google Advanced Data Analytics Professional Certicate Portfolio Project.

The original dataset’s provenance can be traced back to the Professional Certificate itself on Coursera.

The dataset consists of 1 .csv file, including 15,000 observations and 13 distinct variables. The data includes information regarding the activity of individual users of the Waze application, such as number of sessions, drives, kilometers driven, and other activity variables, both in total and on a given month.

The data consists of 13 variables: 8 integer variables, 2 string variables, and 3 float variables.

Abstract This article describes Smart Mobility’s methodology, a project responsible for the implementation of Waze for Cities Data. Developed by the Secretaria de Planejamento Urbano e Desenvolvimento Sustentável (Sepud) of the city of Joinville (SC), the project’s methodology has its mobility plan based on traffic data collected and provided by Waze. Grounded in the concept of Smart Cities, the study’s theoretical framework analyzes the application of Big Data in data-driven urban planning, as well as the importance of citizen's participation in technology projects. When it comes to the study’s methodology, a qualitative exploratory case study is presented, in which a systematic/non-participant observation was used as data collection instrument. The study also presents a brief historical background of Waze and the bidirectional data sharing program Waze for Cities Data. Results demonstrate that the use of data collected by technology platforms can contribute to the optimization of urban management through solutions that are closer to the population's reality; what could generate benefits in time and productivity. The project also positioned Brazil as a world reference in the adoption of data collected by Waze, reinforcing, then, the need of similar initiatives that are less technocratic and more citizen-centered.

The Connected Citizens Program (CCP) is proof that we already have the answers to some of today’s mobility challenges. The Waze map strengthens with every data point contributed across our vast community of everyday drivers and volunteer map editors. Via the CCP, hundreds of international cities, departments of transportation and first responders have built meaningful relationships and regularly knowledge share to identify creative solutions. From road management to measurable congestion reduction, these are the initiatives building cities of tomorrow.By signing up for CCP, you will get access to the Waze Live Alerts Layer which contains up to date information as reported by more than 100 Million monthly active users, who report issues in the Waze application and into your maps.

Dallas' partnership with WAZE allows for the posting of WAZE data in the vicinity of Dallas.Waze for Cities is proof that we already have the answers to some of today’s mobility challenges. The Waze map strengthens with every data point contributed across our vast community of everyday drivers and volunteer map editors. Through Waze for Cities, hundreds of international cities, departments of transportation and first responders have built meaningful relationships and regularly knowledge share to identify creative solutions. From road management to measurable congestion reduction, these are the initiatives building cities of tomorrow. By becoming a member of Waze for Cities, you will get access to the Waze Live Alerts Layer which contains up to date information as reported by more than 100 Million monthly active users, who report issues in the Waze application and into your maps.WAZE Data: is a crowd sourced dynamic dataset of reports of traffic issues within the proximity of Dallas TX. It comes with no guarantee and is for informational use onlyWAZE.COM

Current road blocks or road closures in Jefferson County with location affected. This data is syndicated by Waze, and populated to the Waze mobile app, allowing Jefferson County citizens that utilize Waze to get daily updates on road closures or construction.

This data, in conjunction with Waze traffic information can be viewed here: https://www.waze.com/livemap/?zoom=12&lat=38.20824&lon=-85.66933

Waze maps provide information about specific routes to assist motorists in avoiding traffic jams. Waze provides information about traffic jams and events that affect road conditions, either from drivers using Waze or from external sources. MTO has partnered with Waze through their Connected Citizen Program to publish Waze reported information on the Ontario 511 website. MTO iCorridor leverages such traffic jams to generate summaries of delay, duration, speed, and length by weekly, monthly, time of day, and day of week. The objective is to determine the pattern of congestion on all provincial highway corridors. The delay analysis was comprised of the followings:Estimation of corridor delay;Estimation of duration and length of congestion; andIdentification of true peak period."It must be noted that a “no congestion scenario” does not necessarily imply that there is no traffic on a specific road. Even when congestion is reduced to zero there may still be vehicles driving on the road. Waze creates “jam lines” that indicate continuous portions of streets where speed has slowed. Waze data provides the exact geographic location, length, speed, and time delay for these jam lines compared to the time it would normally take to transverse the jam line by car. A categorization for the severity of the jam is also provided.the jam data is composed of jam lines (which can change over time) measured at different time intervals. Given the crowd-sourced nature of the data, it cannot be determined if fluctuations in jam line activity are due to actual changes in traffic conditions or due to fluctuations in the number of active Wazers. Evidence from on-the-ground measures supports the notion that changes in jam activity are generally due to actual changes in traffic conditions"ElementValueDescriptionpubDateTimePublication date.linqmap:typeStringTRAFFIC_JAM.georss:lineList of longitude and latitude coordinatesTraffic jam line string (supplied when available).linqmap:speedFloatCurrent average speed on jammed segments in meter/second.linqmap:lengthIntegerJam length in meters.linqmap:delayIntegerDelay of jam compared to free flow speed, in seconds (in case of block, 1).linqmap:streetStringStreet name (as is written in database, no canonical form (supplied when available).linqmap:cityStringCity and state name [City, State] in case both are available, [State] if not associated with a city (supplied when available).linqmap:countryStringAvailable on EU (world) server (see two letters codes in https://en.wikipedia.org/wiki/ISO-31661).linqmap:roadTypeIntegerRoad type (see road types table in the appendix).linqmap:startNodeStringNearest Junction/street/city to jam start (supplied when available).linqmap:endNodeStringNearest Junction/street/city to jam end (supplied when available).linqmap:level0-5Traffic congestion level (0 = free flow 5 = blocked).linqmap:uuidStringUnique jam identifier.linqmap:turnLineCoordinatesA set of coordinates of a turn only when the jam is in a turn (supplied when available).linqmap:turnTypeStringWhat kind of turn it is: left, right, exit R or L, continue straight, or NONE (no info) (supplied when available).linqmap:blockingAlertUuidStringIf the jam is connected to a block (see alerts).ElementValueDescriptionpubDateTimePublication date.georss:pointCoordinatesLocation per report (Lat long).linqmap:uuidStringUnique system ID.linqmap:magvarInteger (0359)Event direction (Driver heading at report time. 0 degrees at North, according to the driver's device).linqmap:typeSee alert type tableEvent type.linqmap:subtypeSee alert subtypes tableEvent subtype depends on parameter.linqmap:reportDescriptionStringReport description (supplied when available).linqmap:streetStringStreet name (as is written in database, no canonical form, may be null).linqmap:cityStringCity and state name [City, State] in case both are available, [State] if not associated with a city (supplied when available).linqmap:countryStringSee two letters codes in .linqmap:roadTypeIntegerRoad type (see road types table in the appendix).linqmap:reportRatingIntegerUser rank between 16 (6 = high ranked user).linqmap:jamUuidStringIf the alert is connected to a jam jam ID.linqmap:Reliability (new)0-10How reliable is the report, 10 being most reliable. Based on reporter level and user respon-reference from (https://ops.fhwa.dot.gov/publications/fhwahop18084/ch2.htm)

App Reviews

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2Fd4a6033b6bd31af45d5175d02e697934%2FAPPLEAPPS2.png?generation=1700357122842963&alt=media" alt="">

1. uber-request-a-ride-us- 73787 rows
2. waze-navigation-live-traffic-us- 26260 rows
3. facebook-us- 24200 rows
4. spotify-music-and-podcasts-us- 15580 rows
5. netflix-us- 11760 rows
6. pinterest-us- 10860 rows
7. X-us- 8160 rows
8. tiktok-us- 2542 rows
9. tinder-dating-chat-friends-us- 1060 rows
10. instagram-us- 300 rows

These reviews are from Apple App Store

Usage

This dataset should paint a good picture on what is the public's perception of the apps over the years. Using this dataset, we can do the following

1. Extract sentiments and trends
2. Identify which version of an app had the most positive feedback, the worst.
3. Use topic modelling to identify the pain points of the application.

(AND MANY MORE!)

Note

Images generated using Bing Image Generator

Waze and Crowdsourced Incident Integration Platforms Market Outlook

As per our latest research, the Waze and Crowdsourced Incident Integration Platforms market size reached USD 2.3 billion in 2024 globally, with a recorded CAGR of 14.8% from the previous year. This robust growth is primarily attributed to the increasing adoption of real-time traffic management solutions and the integration of advanced crowdsourced data analytics. By leveraging the current CAGR, the market is projected to attain a value of USD 7.3 billion by 2033. The expansion of connected vehicle ecosystems, rising urban congestion, and the growing reliance on data-driven navigation are central to the market’s acceleration, as per our comprehensive 2025 research findings.

The surging demand for intelligent transportation systems is a significant growth factor for the Waze and Crowdsourced Incident Integration Platforms market. Urbanization has led to escalating traffic congestion, forcing city planners and transportation authorities to seek innovative solutions for real-time traffic management. Crowdsourced platforms, such as Waze, empower users to share live traffic incidents, road hazards, and delays, which are then aggregated and analyzed to provide actionable insights. This democratization of data not only improves navigation accuracy but also supports proactive traffic management strategies. Governments and municipalities are increasingly integrating these platforms into their smart city initiatives, recognizing the value of real-time, user-generated data for optimizing traffic flow and enhancing commuter safety.

Another pivotal growth driver is the evolution of emergency response systems powered by crowdsourced incident reporting. Traditional emergency dispatch systems often face delays in incident detection and response, leading to inefficiencies and, at times, critical outcomes. The integration of crowdsourced platforms enables authorities to receive immediate alerts from users on the ground, facilitating faster dispatch of emergency services and more effective resource allocation. This capability is particularly vital in densely populated urban areas, where timely intervention can save lives and reduce the impact of accidents or hazards. The proliferation of smartphones and the ubiquity of mobile internet have further accelerated the adoption of these platforms, making real-time incident reporting accessible to millions of users worldwide.

Technological advancements in artificial intelligence, machine learning, and big data analytics are also fueling the growth of the Waze and Crowdsourced Incident Integration Platforms market. These technologies enable the aggregation and processing of vast amounts of user-generated data, transforming raw reports into valuable insights for navigation, urban planning, and traffic forecasting. The integration of predictive analytics allows platforms to anticipate congestion patterns and recommend optimal routes, further enhancing user experience. Additionally, partnerships between platform providers, automotive OEMs, and public agencies are fostering the development of comprehensive mobility solutions that seamlessly integrate crowdsourced data with vehicle telematics and infrastructure sensors.

From a regional perspective, North America remains the dominant market for Waze and Crowdsourced Incident Integration Platforms, driven by high smartphone penetration, robust digital infrastructure, and strong government support for smart transportation initiatives. Europe follows closely, with significant investments in connected mobility and urban sustainability. The Asia Pacific region is witnessing the fastest growth rate, propelled by rapid urbanization, increasing vehicle ownership, and government-led smart city projects. Each region exhibits unique adoption patterns, but the overarching trend is a global shift towards data-driven, user-centric mobility solutions that prioritize efficiency, safety, and sustainability.

Component Analysis

The Waze and Crowdsourced Incident In

Approved and currently issued right-of-way construction permits. Limited to projects that will affect roadway traffic.Permitted Construction points data (Road Closures) comes from approved right-of-way construction permits in Accela. An applicant submits a site for approval and after review and approval, a permit is issued. Permits are often approved for a window of time occasionally spanning several weeks. It is likely that this data could show an approved permit construction site where construction has either already been completed or has yet to begin. These points are added and removed based upon the time window expressed on the permit and do not reflect the actual condition of work on the ground.

Field name

Field description

PERMIT_NO

unique identifier for the ROW Permit issued

APPLICANT_NAME

name of the person or organization that applied for the permit

FROM_DATE

the date range for the encroachment to occur

TO_DATE

WORK_DESCRIPTION

detailed descripton of the work to be done

WORK_TYPE

general type of work to be performed, to include ()

STREET_ADDRESS

nearest street address to the location of work

CITY

location of work

STATE

location of work

ZIP

location of work

LONGITUDE

location of work

LATITUDE

location of work

Contact Information:Solita SolimanGIS AnalystPublic Works and AssetsSolita.Soliman@louisvilleky.gov 502-574-5931

Waze Jams, or congestion, are areas of slow-moving or stopped traffic. These are generated by Waze by processing the following data sources: GPS location-points sent from user devices (when using the Waze mobile application)Calculations of actual speed vs average speed (on specific time-slots)Free flow speed (maximum speed measured along road link)User-generated reports. Much like the Alerts feed, reports are shared from Waze road users. These will appear as alerts and affect the way Waze identify traffic jams.This table is the current jams received from Waze in the West Midlands Combined Authority defined area of interest. To request access contact the Data Insight Team.

The global speed enforcement camera app market is experiencing robust growth, driven by increasing traffic violations, heightened road safety concerns, and advancements in mobile technology. The market, estimated at $250 million in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, the rising adoption of smartphones and increased internet penetration globally provides a larger addressable market for these apps. Secondly, government initiatives promoting road safety and stricter enforcement of speed limits are creating significant demand. Furthermore, continuous improvements in app features, such as real-time speed camera alerts, offline database access, and integration with other navigation tools, enhance user experience and drive adoption. The segment encompassing real-time online monitoring applications holds a significant market share, benefiting from the immediate feedback and proactive nature of its alerts. Major players like Waze and Coyote already benefit from established user bases, creating a competitive landscape. However, the market also faces restraints, including concerns about data privacy, potential inaccuracies in data feeds, and varying legal frameworks across different jurisdictions regarding the use of such applications. The North American market currently dominates, driven by early adoption and a strong focus on road safety initiatives. Geographical expansion, particularly in developing economies with rapidly growing vehicle populations, presents a significant growth opportunity. The market segmentation, divided by application (government departments, law enforcement, and individual users) and type (online real-time and offline database), allows for targeted marketing strategies. The emergence of innovative features like augmented reality overlays on speed camera locations and integration with connected car technologies will continue to shape the market's trajectory. Future growth will depend on overcoming data accuracy issues, navigating privacy concerns through transparent data handling policies, and ensuring consistent legal compliance across diverse global markets. The long-term forecast points to sustained growth, driven by improving app functionalities and the enduring need for effective speed enforcement strategies.

The global road safety app market, currently valued at $239 million in 2025, is projected to experience robust growth, fueled by a compound annual growth rate (CAGR) of 8.4% from 2025 to 2033. This expansion is driven by several key factors. Increasing smartphone penetration, coupled with rising public awareness of road safety and the benefits of technology-driven solutions, is significantly boosting adoption rates. Furthermore, the integration of advanced features like real-time accident reporting, emergency assistance functionalities, and driver behavior monitoring within these apps is enhancing their appeal to both individual users and enterprise clients (fleet management companies, insurance providers). Governments are also increasingly promoting the use of such apps through public awareness campaigns and integration with existing road safety infrastructure, further bolstering market growth. The market is segmented by application (enterprise and personal) and operating system (iOS and Android), with the Android segment likely holding a larger market share due to its global dominance in smartphone operating systems. Competitive intensity is high, with numerous players ranging from established tech giants like Google to specialized road safety app developers vying for market share. The competitive landscape is characterized by ongoing innovation in features and functionalities, strategic partnerships, and mergers and acquisitions. While the market displays significant growth potential, challenges remain. Data privacy concerns and the potential for misuse of location data are significant hurdles to overcome. Ensuring user trust and adherence to strict data protection regulations is critical for sustained market growth. Additionally, effective user engagement and app usability are important factors for long-term market success. Differences in regulatory frameworks across various regions can also pose challenges for app developers seeking global market penetration. However, continuous technological advancements and the increasing focus on road safety globally are expected to outweigh these challenges, ensuring the sustained expansion of this dynamic market. The North American and European markets are expected to continue dominating the market, driven by high smartphone penetration and advanced technological infrastructure. However, rapid growth is anticipated in the Asia-Pacific region, particularly in countries like India and China, as increasing smartphone ownership and rising concerns about road safety drive adoption.

English Description below. Liniová vrstva plynulosti dopravy na cestách v Brně. Data pocházejí od společnosti Waze Mobile Ltd. (dále jen Waze), která je sbírá od uživatelů své navigace. Plynulost je zaznamenávána v reálném čase, je aktualizována každých několik sekund a pochází jenom z aut které se ocitli v zácpě nebo v zdržení oproti normálnímu stavu. Datum prvního záznamu je 13.7.2020. Společnost WAZE je poskytuje městu Brnu skrze program WAZE for cities. Data jsou magistrátem města Brna spravovány a ukládány prostřednictvím Geoevent Servru. Souřadnicový systém dat je GCS WGS84. Při použití je nutné citovat společnost WAZE a Magistrát města Brna jako zdroj. Jinak lze datovou sadu užít bez omezení. Podrobnou dokumentaci najdete zde. Stahování a dotazování je omezeno na 10 000 záznamů. K přístupu k datům proto primárně využijte prosím url feed.A line layer of traffic delay on roads in Brno. The data comes from Waze Mobile Ltd., which collects them from users of its navigation. Traffic delays are recorded in real time, are updated every few seconds and originates only from cars that are in congestion or delay. The data allows for an overview of the current traffic delays situation on the roads in Brno. Date of the first entry is 13.7.2020. WAZE provides the data to the city of Brno through the WAZE for cities program. The data is managed and stored by the City of Brno via the Geoevent Server. The coordinate data system is GCS WGS84. When using the data, it is necessary to cite WAZE and the City of Brno as a source. Otherwise, the dataset can be used without any restriction. Detailed documentation can be found here. Downloads are limited to 10,000 records per request. To access the data, please use the url feed primarily.

This dataset is a live feed of current road disruptions/closures on York Region’s regional road network. Data is read from the Advance Traffic Management System maintained by the operators at York Region’s Roads and Traffic Operation Centre. Data is updated from the Roads and Traffic Operation Centre 24/7. Data is for traffic related events on Regional roads only, data for local municipalities is not included. Data will include event type (i.e., watermain break, disabled vehicle, roadwork, etc.), event location, event priority, traffic direction being affected, lane(s) being affected, event start time, anticipated event end time, etc. [Note: Event priority is determined by the Roads and Traffic Operation Centre operator at the time the event occurs to indicate the severity of the road disruptions.]

In December 2016, Waze and the Metropolitan Transportation Commission entered into a data sharing agreement that provides the Freeway Service Patrol (FSP) program with real-time information that will help FSP tow drivers quickly detect incidents. Waze, in turn, will receive the FSP’s highway incident information – including crashes and stalls – to share with its users. Together, both will have more data and be better able to provide timely assistance to drivers in the region.The map represents a snapshot of the incidents reported by FSP and Waze users during peak congestion hours in the nine-county San Francisco Bay Region. Traffic incidents and assists from the FSP program are shown in blue, while reported incidents from Waze are shown in yellow. Incidents have been aggregated along major corridors throughout the region in an attempt to compare the number of incidents reported by Waze users with the total number of assists handled by the FSP program. Alameda County (32%) has the largest share of the total number of incidents during the peak congestion hours, followed by Santa Clara (26%), Contra Costa (15%), and San Mateo (12%).

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

This layer contains traffic jams detected by Waze that have been aggregated to street data for Johns Creek, GA. By aggregating the tiny and/or inconsistently sized jams to larger portions of roadway, traffic analysis can be done more accurately. Data is updated every 5 minutes continuously.