The FDOT Telemetered Traffic Monitoring Site (TTMS) feature class provides information on Florida Telemetered Traffic Monitoring Site locations, as well affiliated information like KFCTR and TFCTR from the FDOT Traffic Characteristics Inventory database. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 02/08/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/DOTShapesFGDB.zip

Information included:counts, provided as average daily flowsan estimate of heavy vehiclesthe number of days sampledtype of sensor equipment.Traffic monitoring for state highways: user manual [PDF 465 KB] Data reuse caveats: as per license.Data quality statement: please read the accompanying user manual, explaining:how this data is collectedidentification of count stationstraffic monitoring technologymonitoring hierarchy and conventionstypical survey specificationdata calculationTMS operation.Traffic monitoring for state highways: user manual [PDF 465 KB] Data quality caveats: it isn’t possible to accurately capture all vehicles using dual loops. An error margin of 2% - 5% is normal. Sites with congestion or lane changing can have higher error margins.AADT (average annual daily traffic) accuracy depends on sampling frequency.Classification isn’t possible at single loop sites, and not all counts at dual loop sites are classified counts. The daily counts at non-continuous sites are adjusted using values from continuous sites. For API explorer users, there is a known issue with number-based attribute filters where the “AND” operator is used instead of the “BETWEEN” operator. Substituting “BETWEEN” for “AND” manually in the query URL will resolve this.

You can also access an API version of this dataset.

TMS

(traffic monitoring system) daily-updated traffic counts API

Important note: due to the size of this dataset, you won't be able to open it fully in Excel. Use notepad / R / any software package which can open more than a million rows.

Data reuse caveats: as per license.

Data quality

statement: please read the accompanying user manual, explaining:

how

 this data is collected identification 

 of count stations traffic 

 monitoring technology monitoring 

 hierarchy and conventions typical 

 survey specification data 

 calculation TMS 

 operation. 

Traffic

monitoring for state highways: user manual

[PDF 465 KB]

The data is at daily granularity. However, the actual update

frequency of the data depends on the contract the site falls within. For telemetry

sites it's once a week on a Wednesday. Some regional sites are fortnightly, and

some monthly or quarterly. Some are only 4 weeks a year, with timing depending

on contractors’ programme of work.

Data quality caveats: you must use this data in

conjunction with the user manual and the following caveats.

The

 road sensors used in data collection are subject to both technical errors and 

 environmental interference.Data 

 is compiled from a variety of sources. Accuracy may vary and the data 

 should only be used as a guide.As 

 not all road sections are monitored, a direct calculation of Vehicle 

 Kilometres Travelled (VKT) for a region is not possible.Data 

 is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For 

 sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are 

 classed as light vehicles. Vehicles over 11m long are classed as heavy 

 vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and 

 heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. 

The NZTA Vehicle

Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts),

and how these map to the Monetised benefits and costs manual, table A37,

page 254.

Monetised benefits and costs manual [PDF 9 MB]

For the full TMS

classification schema see Appendix A of the traffic counting manual vehicle

classification scheme (NZTA 2011), below.

Traffic monitoring for state highways: user manual [PDF 465 KB]

State highway traffic monitoring (map)

State highway traffic monitoring sites

A public map showing traffic volumes (Annual Average Daily Traffic) for State Highways. The data is represented in 2 ways: counts sites and estimated traffic between sites. The map also includes local and regional council boundaries, reference stations and heavy vehicle estimates.

You can also access a zipped csv file version of this dataset.TMS (traffic monitoring system) daily-updated traffic counts CSVData reuse caveats: as per license. Data quality statement: please read the accompanying user manual, explaining: how this data is collected identification of count stations traffic monitoring technology monitoring hierarchy and conventions typical survey specification data calculation TMS operation. Traffic monitoring for state highways: user manual [PDF 465 KB] The data is at daily granularity. However, the actual update frequency of the data depends on the contract the site falls within. For telemetry sites it's once a week on a Wednesday. Some regional sites are fortnightly, and some monthly or quarterly. Some are only 4 weeks a year, with timing depending on contractors’ programme of work. Data quality caveats: you must use this data in conjunction with the user manual and the following caveats. The road sensors used in data collection are subject to both technical errors and environmental interference.Data is compiled from a variety of sources. Accuracy may vary and the data should only be used as a guide.As not all road sections are monitored, a direct calculation of Vehicle Kilometres Travelled (VKT) for a region is not possible.Data is sourced from Waka Kotahi New Zealand Transport Agency TMS data.For sites that use dual loops classification is by length. Vehicles with a length of less than 5.5m are classed as light vehicles. Vehicles over 11m long are classed as heavy vehicles. Vehicles between 5.5 and 11m are split 50:50 into light and heavy.In September 2022, the National Telemetry contract was handed to a new contractor. During the handover process, due to some missing documents and aged technology, 40 of the 96 national telemetry traffic count sites went offline. Current contractor has continued to upload data from all active sites and have gradually worked to bring most offline sites back online. Please note and account for possible gaps in data from National Telemetry Sites. The NZTA Vehicle Classification Relationships diagram below shows the length classification (typically dual loops) and axle classification (typically pneumatic tube counts), and how these map to the Monetised benefits and costs manual, table A37, page 254. Monetised benefits and costs manual [PDF 9 MB] For the full TMS classification schema see Appendix A of the traffic counting manual vehicle classification scheme (NZTA 2011), below. Traffic monitoring for state highways: user manual [PDF 465 KB] State highway traffic monitoring (map) State highway traffic monitoring sites TMS (traffic monitoring system) traffic – historic quarter hourly

Information included:counts, provided as average daily flowsan estimate of heavy vehiclesthe number of days sampledtype of sensor equipment.Traffic monitoring for state highways: user manual [PDF 465 KB]

Data reuse caveats: as per license.Data quality statement: please read the accompanying user manual, explaining:how this data is collectedidentification of count stationstraffic monitoring technologymonitoring hierarchy and conventionstypical survey specificationdata calculationTMS operation.Traffic monitoring for state highways: user manual [PDF 465 KB]

Data quality caveats: it isn’t possible to accurately capture all vehicles using dual loops. An error margin of 2% - 5% is normal. Sites with congestion or lane changing can have higher error margins.AADT (average annual daily traffic) accuracy depends on sampling frequency.Classification isn’t possible at single loop sites, and not all counts at dual loop sites are classified counts. The daily counts at non-continuous sites are adjusted using values from continuous sites. For API explorer users, there is a known issue with number-based attribute filters where the “AND” operator is used instead of the “BETWEEN” operator. Substituting “BETWEEN” for “AND” manually in the query URL will resolve this.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.

The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.

According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.

For more information, please visit MDOT Traffic Monitoring Program.

The Travel Monitoring Analysis System (TMAS) - Volume dataset was compiled on December 31, 2023 and was published on July 16, 2024 from the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TMAS data included in this table have been collected by the FHWA from State DOTs through (temporal data representing each time period) permanent count data. DOTs determine what volume data is reported for any given month or day within the month. Each record in the volume data for the reported site, direction or lane is for the given day of record (it contains all 24 hours of data). The attributes are used by FHWA for its Travel Monitoring Analysis System and external agencies and have been intentionally limited to location referencing attributes since the core station description attribute data are contained within TMAS. The attributes in the Volume data correspond with the Volume file format found in Chapter 6 of the 2001 Traffic Monitoring Guide (https://doi.org/10.21949/1519109).

The Travel Monitoring Analysis System (TMAS) - Stations dataset was compiled on December 31, 2023 and was published on July 22, 2024 from the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Geospatial station data from the FHWA TMAS database contains latitude and longitude data from over 7,000 permanent (temporal data representing each time period) traffic monitoring sites in all 50 states plus DC. Data from these stations are submitted to FHWA every month and is a result of a long standing partnership between FHWA and the state DOTs.

Traffic count data downloaded from GDOT public map here: https://gdottrafficdata.drakewell.com/publicmultinodemap.aspRetrieved Annual Statistics Reports: "All Station AADT and Truck Percent Statistics." Mapped by Lat/Long field.Retrieved and rehosted for staff use and overlay on city maps on 12/14/2018."The Georgia Department of Transportation’s Traffic Analysis and Data Application (TADA!) website presents data collected from the Georgia Traffic Monitoring Program located on the public roads in Georgia. The Website uses a dynamic mapping interface to allow the User to access data from the map as well as in a variety of report, graph, and data export formats."

In 2023, most of the global website traffic was still generated by humans but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 32 percent of global web traffic in the most recently measured period, representing an increase of 1.8 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past two years, indicating a surge in the sophistication of cyber threats. Simultaneously, simple bad bots saw a 6 percent increase compared to the previous year, suggesting a shift in the landscape of automated threats. Meanwhile, areas like entertainment, and law & government face the highest amount of advanced bad bots, with more than 78 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 57.2 percent of the gaming segment's web traffic. Meanwhile, almost half of the online traffic for telecom and ISPs was moved by malicious applications. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and financial services experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2023. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2020. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.

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.

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

SDCC Traffic Congestion Saturation Flow Data for January to June 2023. Traffic volumes, traffic saturation, and congestion data for sites across South Dublin County. Used by traffic management to control stage timings on junctions. It is recommended that this dataset is read in conjunction with the ‘Traffic Data Site Names SDCC’ dataset.A detailed description of each column heading can be referenced below;scn: Site Serial numberregion: A group of Nodes that are operated under SCOOT control at the same common cycle time. Normally these will be nodes between which co-ordination is desirable. Some of the nodes may be double cycling at half of the region cycle time.system: SCOOT STC UTC (UTC-MX)locn: Locationssite: Site numbersday: Days of the week Monday to Sunday. Abbreviations; MO,TU,WE,TH,FR,SA,SU.date: Reflects correct actual Date of when data was collected.start_time: NOTE - Please ignore the date displayed in this column. The actual data collection date is correctly displayed in the 'date' column. The date displayed here is the date of when report was run and extracted from the system, but correctly reflects start time of 15 minute intervals. end_time: End time of 15 minute intervals.flow: A representation of demand (flow) for each link built up over several minutes by the SCOOT model. SCOOT has two profiles:(1) Short – Raw data representing the actual values over the previous few minutes(2) Long – A smoothed average of values over a longer periodSCOOT will choose to use the appropriate profile depending on a number of factors.flow_pc: Same as above ref PC SCOOTcong: Congestion is directly measured from the detector. If the detector is placed beyond the normal end of queue in the street it is rarely covered by stationary traffic, except of course when congestion occurs. If any detector shows standing traffic for the whole of an interval this is recorded. The number of intervals of congestion in any cycle is also recorded.The percentage congestion is calculated from:No of congested intervals x 4 x 100 cycle time in seconds.This percentage of congestion is available to view and more importantly for the optimisers to take into account.cong_pc: Same as above ref PC SCOOTdsat: The ratio of the demand flow to the maximum possible discharge flow, i.e. it is the ratio of the demand to the discharge rate (Saturation Occupancy) multiplied by the duration of the effective green time. The Split optimiser will try to minimise the maximum degree of saturation on links approaching the node.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.

The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.

According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.

For more information, please visit MDOT Traffic Monitoring Program.

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2022. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.

Please note: this data differs in some of its columns from the Oct 2020 - Jan 2022 dataset.

TMS traffic quarter-hourly: Oct 2020 to Jan 2022

Data reuse caveats: as per license.

Data quality statement: please read the accompanying user manual, explaining:

how this data is collected identification of count stations traffic monitoring technology monitoring hierarchy and conventions typical survey specification data calculation TMS operation.

Traffic monitoring for state highways: user manual [PDF 465 KB]

Data quality caveats: you must use this data in conjunction with the user manual, and the following caveats.

Much of this data is sourced from road sensors and may be subject to technical or environmental factors.Data is compiled from a variety of sources. Accuracy may vary and the data should only be used as a guide.Data is representative of the change of traffic volume not of total traffic volume for each region.Data is sourced from Waka Kotahi New Zealand Transport Agency TMS data.Light and heavy traffic volumes have been split using TMS data where vehicles with a length of less than 5.5m are classed as light vehicles. Heavy vehicles are over 11m long. Those between 5.5 and 11m are split 50:50 into light and heavy vehicles.

Please be aware that when unzipped these are very large files and some systems may have difficulty opening it. Each file contains more than 10 million rows, and therefore won't work in Excel. However, each file is in CSV format, and will work in:programming languages such as Python or Rdatabases with SQL.

State highway traffic monitoring (map)

State highway traffic monitoring sites

State highway daily-updated traffic count (API and CSV)

This dataset contains estimates of the average number of vehicles that used roads throughout the City of Detroit in 2019. Each record indicates the Annual Average Daily Traffic (AADT) and Commercial Annual Average Daily Traffic (CAADT) for a road segment, where the road segment is located, and other characteristics. This data is derived from Michigan Department of Transportation's (MDOT) Open Data Portal. SEMCOG was the source for speed limits and number of lanes.The primary measure, Annual Average Daily Traffic (AADT), is the estimated mean daily traffic volume for all types of vehicles. Commercial Annual Average Daily Traffic (CAADT) is the estimated mean daily traffic volume for commercial vehicles, a subset of vehicles included in the AADT. The Route ID is an identifier for each road in Detroit (e.g., Woodward Ave). Routes are divided into segments by features such as cross streets, and Location ID's are used to uniquely identify those segments. Along with traffic volume, each record also states the number of lanes, the posted speed limit, and the type of road (e.g., Trunkline or Ramp) based on the Federal Highway Administration (FHWA) functional classification system.According to MDOT's Traffic Monitoring Program a commercial vehicle would be anything Class 4 and up in the FHWA vehicle classification system. This includes vehicles such as buses, semi-trucks, and personal recreational vehicles (i.e., RVs or campers). Methods used to determine traffic volume vary by site, and may rely on continuous monitoring or estimates based on short-term studies. Approaches to vehicle classification similarly vary, depending on the equipment used at a site, and may consider factors such as vehicle weight and length between axles.For more information, please visit MDOT Traffic Monitoring Program.