Leverage the most reliable and compliant mobile device location/foot traffic dataset on the market. Veraset Movement (Mobile Device GPS / Foot Traffic Data) offers unparalleled insights into footfall traffic patterns across North America.

Covering the United States, Canada and Mexico, Veraset's Mobile Location Data draws on raw GPS data from tier-1 apps, SDKs, and aggregators of mobile devices to provide customers with accurate, up-to-the-minute information on human movement. Ideal for ad tech, planning, retail analysis, and transportation logistics, Veraset's Movement data helps in shaping strategy and making data-driven decisions.

Veraset’s North American Movement Panel: - United States: 768M Devices, 70B+ Pings - Canada: 55M+ Devices, 9B+ Pings - Mexico: 125M+ Devices, 14B+ Pings - MAU/Devices and Monthly Pings

Uses for Veraset's Mobile Location Data: - Advertising - Ad Placement, Attribution, and Segmentation - Audience Creation/Building - Dynamic Ad Targeting - Infrastructure Plans - Route Optimization - Public Transit Optimization - Credit Card Loyalty - Competitive Analysis - Risk assessment, Underwriting, and Policy Personalization - Enrichment of Existing Datasets - Trade Area Analysis - Predictive Analytics and Trend Forecasting

This dataset contains the Department of Transport and Main Roads road location details (both spatial and through distance) as well as associated traffic data.

It allows users to locate themselves with respect to road section number and through distance using the spatial coordinates on the state-controlled road network.

Through distance – the distance in kilometres measured from the gazetted start point of the road section.

Note: "Road location and traffic data" resource has been updated as of June 2025.

GapMaps Foot Traffic Data by Azira provides actionable insights on consumer travel patterns at a global scale empowering Marketing and Operational Leaders to confidently reach, understand, and market to highly targeted audiences and optimize their business results.

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

This foot traffic dataset provides GPS-based mobile movement signals from across South America. It is ideal for retailers, city agencies, advertisers, and real estate professionals seeking insights into how people move through physical locations and urban spaces.

Each record includes:

Device ID (IDFA or GAID) Timestamps (in milliseconds and readable format) GPS coordinates (lat/lon) Country code Horizontal accuracy (85%) Optional IP address, mobile carrier, and device model

Access the data via polygon queries (up to 10,000 tiles), and receive files in CSV, JSON, or Parquet, delivered hourly or daily via API, AWS S3, or Google Cloud. Data freshness is strong (95% delivered within 3 days), with full historical backfill available from September 2024.

This solution supports flexible credit-based pricing and is privacy-compliant under GDPR and CCPA.

Key Attributes:

Custom POI or polygon query capability Backfilled GPS traffic available across LATAM High-resolution movement with daily/hourly cadence GDPR/CCPA-aligned with opt-out handling Delivery via API or major cloud platforms

Use Cases:

Competitive benchmarking across malls or stores Transport and infrastructure planning Advertising attribution for outdoor/DOOH campaigns Footfall modeling for commercial leases City zoning, tourism, and planning investments Telecom & tower planning across developing corridors

GapMaps Mobility Data uses location data on mobile phones sourced by Azira which is collected from smartphone apps when the users have given their permission to track their location. It can shed light on consumer visitation patterns (“where from” and “where to”), frequency of visits, profiles of consumers and much more.

Businesses can utilise Mobility data to answer key questions including: - What is the demographic profile of customers visiting my locations? - What is my primary catchment? And where within that catchment do most of my customers travel from to reach my locations? - What points of interest drive customers to my locations (ie. work, shopping, recreation, hotel or education facilities that are in the area) ? - How far do customers travel to visit my locations? - Where are the potential gaps in my store network for new developments?
- What is the sales impact on an existing store if a new store is opened nearby? - Is my marketing strategy targeted to the right audience? - Where are my competitor's customers coming from?

Mobility Data provides a range of benefits that make it a valuable addition to location intelligence services including: - Real-time - Low-cost at high scale - Accurate - Flexible - Non-proprietary - Empirical

Azira have created robust screening methods to evaluate the quality of Mobility data collected from multiple sources to ensure that their data lake contains only the highest-quality mobile location data.

This includes partnering with trusted location SDK providers that get proper end user consent to track their location when they download an application, can detect device movement/visits and use GPS to determine location co-ordinates.

Data received from partners is put through Azira's data quality algorithm discarding data points that receive a low quality score.

Use cases in Europe will require approval on a case by case basis to ensure compliance with GDPR.

Traffic-related data collected by the Boston Transportation Department, as well as other City departments and State agencies. Various types of counts: Turning Movement Counts, Automated Traffic Recordings, Pedestrian Counts, Delay Studies, and Gap Studies.

~_Turning Movement Counts (TMC)_ present the number of motor vehicles, pedestrians, and cyclists passing through the particular intersection. Specific movements and crossings are recorded for all street approaches involved with the intersection. This data is used in traffic signal retiming programs and for signal requests. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Automated Traffic Recordings (ATR)_ record the volume of motor vehicles traveling along a particular road, measures of travel speeds, and approximations of the class of the vehicles (motorcycle, 2-axle, large box truck, bus, etc). This type of count is conducted only along a street link/corridor, to gather data between two intersections or points of interest. This data is used in travel studies, as well as to review concerns about street use, speeding, and capacity. Counts are typically conducted for 12- & 24-Hr periods.

~_Pedestrian Counts (PED)_ record the volume of individual persons crossing a given street, whether at an existing intersection or a mid-block crossing. This data is used to review concerns about crossing safety, as well as for access analysis for points of interest. Counts are typically conducted for 2-, 4-, 11-, and 12-Hr periods.

~_Delay Studies (DEL)_ measure the delay experienced by motor vehicles due to the effects of congestion. Counts are typically conducted for a 1-Hr period at a given intersection or point of intersecting vehicular traffic.

~_Gap Studies (GAP)_ record the number of gaps which are typically present between groups of vehicles traveling through an intersection or past a point on a street. This data is used to assess opportunities for pedestrians to cross the street and for analyses on vehicular “platooning”. Counts are typically conducted for a specific 1-Hr period at a single point of crossing.

Comprehensive dataset containing 34 verified Traffic Management, Inc. locations in United States with complete contact information, ratings, reviews, and location 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 dataset contains the location, GPS coordinates and textural description, of all traffic counts conducted by VicRoads over the past 20+ years. VicRoads collects thousands of counts each year …Show full descriptionThis dataset contains the location, GPS coordinates and textural description, of all traffic counts conducted by VicRoads over the past 20+ years. VicRoads collects thousands of counts each year across the state. Depending on the type of equipment used, this will determine the accuracy and the type of data collected. eg Data collected at Traffic Lights (SCATS) does not classify the data into cars and truck. Turning Movement data does not have a traffic speed component etc. Data is not collected at every location every year. Some Traffic flow movements (TFMs) have also been used for adhoc counts. A strategic counting program has been developed to ensure an even coverage of data is available both temporally and spatially based on value to the state. About this dataset...

This foot traffic dataset offers detailed GPS-based mobility data from across the Asia region. Built for developers, urban analysts, and retail strategists, this data enables precise analysis of movement around key points of interest (POIs), districts, or regions.

Each GPS event contains:

Latitude and longitude coordinates Device ID (IDFA/GAID) Event timestamps in epoch and date format Country code Horizontal accuracy (85% fill rate) Optional IP address, carrier, and device model

The data is queryable via API by custom polygon areas, up to 10,000 tiles per request. Events can be delivered daily or hourly in Parquet, CSV, or JSON, through API or cloud endpoints (S3 or GCS). 95% of events are delivered within a 3-day lag, and data can be backfilled to September 2022.

This product is particularly well-suited for organizations operating in mobile-first Asia markets, where real-time traffic data can drive competitive advantage.

Qualitative Features:

High-resolution mobile movement around POIs Backfilled and real-time query capability Credit-based usage for scalability Compliant with global data privacy standards (GDPR, CCPA) Custom schema and delivery paths available

Use Cases:

Urban zoning and infrastructure investments Public transit and congestion planning DOOH advertising optimization Retail visitation benchmarking Location-based market entry strategy Audience mobility modeling for campaigns Disaster readiness and emergency planning

Iowa Department of Transportation's Intelligent Transportation System (ITS) Detector Sensors. Sensor Feed: Includes location of sensors, current travel speed, traffic counts, occupancy counts, and more.Work Zone Alert Feed: Includes work zones that have dropped below the normal speed and are determined to have a critical traffic speed abnormality.

The Wisconsin Traffic Counts dataset combines traffic count data with GIS mapping technology to display data in a tabular format, on a map, or both. Traffic_Count_AADT is a source of Wisconsin DOT traffic data information for road sections of the State Highways or select Local Federal-Aid roads. Traffic counts are reported as the number of vehicles expected to pass a given location on an average day of the year. This value is called the "annual average daily traffic" or AADT. The AADT is produced for either continuous count sites or short duration count sites. WisDOT collects continuous count data from about 320 permanent data collection locations primarily located on the State Trunk Highway System. Data at continuous count sites are scheduled to be collected in hourly intervals each day of the year. A short duration traffic count usually collects hourly intervals for a 48-hour period, taken at the specific locations throughout the state. Using continuous count data, short duration counts are then adjusted for the variation in traffic volume that occurs throughout the year. Short duration counts are collected over three, six, or ten-year cycles at more than 26,000 rural and urban locations throughout the state. In addition to Wisconsin DOT use for transportation management purposes, Wisconsin DOT is required to collect and report these statistics to the Federal Highway Administration monthly and annually.Related Dataset: Traffic Counts

Traffic Data as a Service Market Outlook

According to our latest research, the global Traffic Data as a Service market size stood at USD 1.68 billion in 2024, reflecting the increasing integration of real-time traffic intelligence across urban and commercial landscapes. The market is projected to grow at a robust CAGR of 19.2% from 2025 to 2033, reaching an estimated USD 7.84 billion by 2033. This significant expansion is primarily driven by the rising need for smart mobility solutions, government investments in intelligent transportation infrastructure, and the accelerating adoption of cloud-based analytics for dynamic traffic management. As per our latest research, the surge in connected vehicles, IoT adoption, and the imperative to minimize urban congestion are key factors propelling the global market forward.

One of the most influential growth factors in the Traffic Data as a Service market is the rapid urbanization and proliferation of smart city initiatives worldwide. As cities continue to expand and populations grow, urban planners and government agencies are increasingly relying on advanced traffic data solutions to manage congestion, optimize public transport, and enhance road safety. The deployment of intelligent transportation systems (ITS) that leverage real-time and predictive data is becoming standard practice, enabling authorities to make data-driven decisions for efficient traffic flow. Furthermore, the integration of advanced sensors, GPS, and IoT devices into road networks is fueling the demand for comprehensive traffic data platforms, which in turn is driving market growth at an unprecedented rate.

Another major driver is the escalating need for route optimization and logistics efficiency among commercial enterprises and logistics companies. The rise of e-commerce and on-demand delivery services has placed immense pressure on supply chains to minimize delivery times and reduce operational costs. Traffic Data as a Service platforms offer granular insights into real-time and historical traffic patterns, allowing businesses to optimize delivery routes, avoid congested areas, and improve overall fleet utilization. This not only enhances customer satisfaction but also contributes to significant cost savings and reduced carbon emissions, making traffic data solutions an essential tool for the modern logistics sector.

The increasing adoption of cloud-based deployment models is also transforming the Traffic Data as a Service landscape. Cloud-based solutions offer unparalleled scalability, flexibility, and cost-effectiveness, enabling organizations of all sizes to access advanced analytics without the need for substantial capital investment in infrastructure. The ability to integrate traffic data with other enterprise systems, such as ERP and CRM, further amplifies the value proposition for end-users. Additionally, advancements in artificial intelligence and machine learning are enabling predictive analytics capabilities, empowering stakeholders to anticipate and mitigate traffic-related challenges proactively. These technological innovations are expected to further accelerate market growth over the forecast period.

From a regional perspective, North America currently dominates the Traffic Data as a Service market, driven by robust investments in smart infrastructure and the widespread adoption of connected vehicle technologies. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, government-led smart city projects, and the proliferation of mobile devices. Europe, with its focus on sustainable urban mobility and stringent emission regulations, also represents a significant share of the global market. As these regions continue to prioritize intelligent transportation solutions, the demand for advanced traffic data services is expected to witness sustained growth across the globe.

Component Analysis

The Traffic Data as a Service market is segmented by component into Software, Services, and Platforms, each playing

Traffic volumes data across Dublin City from the SCATS traffic management system. The Sydney Coordinated Adaptive Traffic System (SCATS) is an intelligent transportation system used to manage timing of signal phases at traffic signals. SCATS uses sensors at each traffic signal to detect vehicle presence in each lane and pedestrians waiting to cross at the local site. The vehicle sensors are generally inductive loops installed within the road. 3 resources are provided: SCATS Traffic Volumes Data (Monthly) Contained in this report are traffic counts taken from the SCATS traffic detectors located at junctions. The primary function for these traffic detectors is for traffic signal control. Such devices can also count general traffic volumes at defined locations on approach to a junction. These devices are set at specific locations on approaches to the junction but may not be on all approaches to a junction. As there are multiple junctions on any one route, it could be expected that a vehicle would be counted multiple times as it progress along the route. Thus the traffic volume counts here are best used to represent trends in vehicle movement by selecting a specific junction on the route which best represents the overall traffic flows. Information provided: End Time: time that one hour count period finishes. Region: location of the detector site (e.g. North City, West City, etc). Site: this can be matched with the SCATS Sites file to show location Detector: the detectors/ sensors at each site are numbered Sum volume: total traffic volumes in preceding hour Avg volume: average traffic volumes per 5 minute interval in preceding hour All Dates Traffic Volumes Data This file contains daily totals of traffic flow at each site location. SCATS Site Location Data Contained in this report, the location data for the SCATS sites is provided. The meta data provided includes the following; Site id – This is a unique identifier for each junction on SCATS Site description( CAP) – Descriptive location of the junction containing street name(s) intersecting streets Site description (lower) - – Descriptive location of the junction containing street name(s) intersecting streets Region – The area of the city, adjoining local authority, region that the site is located LAT/LONG – Coordinates Disclaimer: the location files are regularly updated to represent the locations of SCATS sites under the control of Dublin City Council. However site accuracy is not absolute. Information for LAT/LONG and region may not be available for all sites contained. It is at the discretion of the user to link the files for analysis and to create further data. Furthermore, detector communication issues or faulty detectors could also result in an inaccurate result for a given period, so values should not be taken as absolute but can be used to indicate trends.

Location of traffic counters in York. *Please note that the data published within this dataset is a live API link to CYC's GIS server. Any changes made to the master copy of the data will be immediately reflected in the resources of this dataset.The date shown in the "Last Updated" field of each GIS resource reflects when the data was first published.

This dataset contains real-time data collected from a network of IoT sensors deployed throughout an urban environment to support public management systems. The data includes vehicle speed, GPS location coordinates, road traffic patterns, and incident reports, which are continuously gathered and processed at the network's edge to minimize latency and enable real-time event detection. The dataset captures key factors such as congestion, accident hotspots, and traffic disruptions, which are essential for improving the efficiency and responsiveness of public services.

The dataset includes the following attributes:

Timestamp: The exact time when the data was recorded. Sensor ID: A unique identifier for each vehicle sensor. Vehicle Speed (km/h): Speed of the vehicle in kilometers per hour. Latitude and Longitude: GPS coordinates representing the vehicle's location. Traffic Pattern: Describes the level of traffic at the time of recording (Light, Moderate, Heavy, Clear). Incident Report: Type of incident occurring at the time (None, Accident, Breakdown, Traffic Jam, Police Blockade). Accident Hotspot: Binary indicator showing if the location is a known accident hotspot. Event Type: Categorizes the event as Normal, Accident, or Congestion based on the collected data.

This table provides location data and summary statistics of each traffic study. The SDOT Traffic Counts group runs studies across the city to collect traffic volumes. Most studies are done with pneumatic tubes, but some come from video systems as well. Use the field study_id to match it with other tables for more detailed information. Data are binned in 15 minute and 60 minute bins in other tables. LANE_DESIGNATION_CODE_ID 1     Standard 2     Right Turn 3     Left Turn 4     Thru Only 5     Thru + Right Turn 6     Thru + Left Turn 7     Aggregate Element 8     Anomaly / Special Event 9     Unknown 10    0 11    1 12    2 13    3 14    4 15    5 16    6 TRAFFIC_FLOW_DIR_ID: 1 N 2 S 3 E 4 W 5 NE 6 SE 7 SW 8 NW 9 REV 10 UNKNOWN 11 TOTAL

Real-Time Traffic Data Market Outlook

According to our latest research, the global real-time traffic data market size in 2024 stands at USD 7.8 billion, reflecting the rapidly increasing adoption of intelligent transportation systems and smart mobility solutions worldwide. The market is experiencing robust growth, with a compound annual growth rate (CAGR) of 14.2% projected from 2025 to 2033. By the end of 2033, the real-time traffic data market is forecasted to reach a substantial USD 23.1 billion. This impressive expansion is driven by the escalating demand for efficient traffic management, growing urbanization, and the proliferation of connected and autonomous vehicles, as per our latest research findings.

One of the primary growth factors for the real-time traffic data market is the rapid urbanization and the consequent increase in vehicular traffic across major metropolitan areas. As cities continue to expand and populations rise, the pressure on existing road infrastructure intensifies, leading to frequent congestion and delays. Real-time traffic data solutions are being increasingly deployed by urban planners and transportation authorities to monitor, analyze, and manage traffic flows dynamically. These systems enable authorities to respond proactively to traffic incidents, optimize signal timings, and implement adaptive traffic control measures, thereby reducing congestion and improving commuter experiences. The integration of real-time data into urban mobility platforms is also facilitating smarter travel choices for citizens, further propelling market growth.

Another significant driver of market expansion is the advancement and widespread adoption of connected vehicle technologies and the Internet of Things (IoT). The proliferation of GPS-enabled devices, in-vehicle sensors, and mobile applications has dramatically increased the volume, accuracy, and granularity of real-time traffic data available for analysis. These data sources provide comprehensive insights into vehicle speed, location, traffic density, and incident detection, enabling transportation authorities and commercial enterprises to make informed decisions in real time. Moreover, the convergence of cloud computing and artificial intelligence (AI) with traffic data analytics is enhancing the predictive capabilities of these systems, allowing for more efficient route optimization, public transportation scheduling, and emergency response coordination. This technological synergy is a key catalyst for the sustained growth of the real-time traffic data market.

Furthermore, the growing emphasis on sustainability and the reduction of carbon emissions is influencing government policies and investments in smart mobility infrastructure. Real-time traffic data solutions play a crucial role in supporting eco-friendly transportation initiatives by minimizing idle times, reducing fuel consumption, and promoting the use of public transit and alternative mobility services. Governments and transportation agencies are increasingly leveraging these solutions to implement congestion pricing, optimize public transportation networks, and encourage multimodal mobility options. The integration of real-time traffic data with smart city platforms and urban planning tools is creating new opportunities for market participants, as cities strive to become more resilient, efficient, and sustainable in the face of mounting environmental challenges.

Navigation And Real Time Traffic Apps are becoming increasingly integral to the daily lives of commuters and city dwellers. These applications leverage real-time traffic data to provide users with up-to-date information on road conditions, traffic congestion, and estimated travel times. By integrating data from various sources such as GPS, sensors, and mobile applications, these apps offer dynamic route suggestions and alerts, helping users avoid traffic jams and reach their destinations more efficiently. The convenience and accuracy of these apps are driving their widespread adoption, as they not only enhance the commuting experience but also contribute to reduced fuel consumption and lower emissions by optimizing travel routes. As urban areas continue to grow, the role of navigation and real-time traffic apps in facilitating smoother and more sustainable urban mobility is expected to expand significantly.

Maryland Annual Average Daily Traffic (AADT) Points data consists of point geometric features which represent traffic count locations along public roadways in the State of Maryland. Traffic counts are performed in order to calculate the annual average daily traffic (AADT), annual average weekday traffic (AAWDT), and AADT based on vehicle class (current year only) for locations along public roadways in the State of Maryland. Overall percent utilization, percent utilization based on vehicle class, and truck-specific percent utilization are showcased as statistical metrics for each location where applicable. Ten years of historic AADT and AAWDT traffic count information is also available for each location where applicable.Annual Average Daily Traffic (AADT) data is collected from over 8700 program count stations and 84 ATRs, located throughout Maryland. The quality control feature of the system allow data edit checks and validation for data from the 84 permanent, continuous automatic traffic recorders (ATRs) and short-term traffic counts. To date, four (4) ATRs have been removed from the ATR Program. Program count data is collected (both directions) at regular locations on either a three (3) year or six (6) year cycle depending on type of roadway. Growth Factors are applied to counts which were not taken during the current year and the counts are factored based on the past yearly growth of an associated ATR. Counters are placed for 48 hours on a Monday or Tuesday and are picked up that Thursday or Friday, respectively. The ATR and toll count data is collected on a continuous basis. Toll station data is provided by the Maryland Transportation Authority (MDTA). A special numeric code was added to the AADT numbers, starting in 2006, to identify the years when the count was actually taken. The last digit represents the number of years prior to the actual count. Where “0” represents the current year when data was collected (in 2014), “1” represents the count taken in 2013, “2” represents the count taken in 2012, “3” represents the count taken in 2011 and so forth.Annual Average Daily Traffic (AADT) data is a strategic resource for the Federal Highway Administration (FHWA), the Maryland Department of Transportation (MDOT), the Maryland Department of Transportation State Highway Administration (MDOT SHA), as well as many other State and local government agencies. The data is essential in the planning, design and operation of the statewide road system and the development and implementation of State highway improvement and safety programs. The MDOT SHA Traffic Monitoring System (TMS) is a product of the ISTEA Act of 1991, which required a traffic data program to effectively and efficiently meet MDOT SHA’s long-term traffic data monitoring and reporting requirements.Annual Average Daily Traffic (AADT) data is updated and published on an annual (yearly) basis for the prior year. This data is for the year 2019. View the most current AADT data in the Maryland Annual Average Daily Traffic (AADT) LocatorFor AADT data information, contact the MDOT SHA Traffic Monitoring System (TMS) TeamEmail: TMS@mdot.maryland.govFor additional information, contact the MDOT SHA Geospatial Technologies TeamEmail: GIS@mdot.maryland.govFor additional information related to the Maryland Department of Transportation (MDOT):https://www.mdot.maryland.gov/For additional information related to the Maryland Department of Transportation State Highway Administration (MDOT SHA):https://roads.maryland.gov/Home.aspxMDOT SHA Geospatial Data Legal Disclaimer:The Maryland Department of Transportation State Highway Administration (MDOT SHA) makes no warranty, expressed or implied, as to the use or appropriateness of geospatial data, and there are no warranties of merchantability or fitness for a particular purpose or use. The information contained in geospatial data is from publicly available sources, but no representation is made as to the accuracy or completeness of geospatial data. MDOT SHA shall not be subject to liability for human error, error due to software conversion, defect, or failure of machines, or any material used in the connection with the machines, including tapes, disks, CD-ROMs or DVD-ROMs and energy. MDOT SHA shall not be liable for any lost profits, consequential damages, or claims against MDOT SHA by third parties.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Transportation/MD_AnnualAverageDailyTraffic/FeatureServer/0