Cubit's Blog

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Updated: 11/2021. The only time of year that I’m actually happy about traffic is when we get updated traffic data for our reports. Below are 52 of my favorite sources of traffic data that you can use to visualize the AADT or annual average daily traffic that drives past your business (yay!) or past your home (boo!). Let’s start with a table of traffic count data sources from state departments of transportation — DOTs for short.

State DOT Traffic Counts Resources

State DOT Name & Link	Most Current AADT/ADT Year in Interactive Map	Notes
Alabama DOT Traffic	2020 AADT
Alaska DOT Traffic	2020 AADT
Arizona DOT Traffic	2020 (some) AADT
Arkansas DOT Traffic	2020 ADT	Static maps by county here
California DOT Traffic	2019 AADT	Tabular data here
Colorado DOT Traffic	2020 AADT	Other source here
Connecticut DOT Traffic	2019 (some) AADT
Washington DC DOT Traffic	2019 AADT
Delaware DOT Traffic	2020 AADT
Florida DOT Traffic	2020 AADT	Can download shapefile here
Georgia DOT Traffic	2020 (some) AADT	Other source here
Hawaii DOT Traffic	2020 AADT
Idaho DOT Traffic	2019 AADT
Illinois DOT Traffic	2019 (some) ADT	Static traffic map here
Indiana DOT Traffic	2020 AADT	Can download shapefiles here
Iowa DOT Traffic	2019 (some) AADT	Static maps here
Kansas DOT Traffic	2020 AADT	Other map here
Kentucky DOT Traffic	2020 (some) AADT
Louisana DOT Traffic	2020 (some) AADT
Maine	2019 (some) AADT
Maryland DOT Traffic	2020 AADT	Static map here
Massachusetts DOT Traffic	2020 AADT
Michigan DOT Traffic	2020 AADT
Minnesota DOT Traffic	2020 (some) AADT	Static maps & shapefile here
Mississippi DOT Traffic	2019 ADT
Missouri DOT Traffic	2020 AADT
Montana DOT Traffic	2020 AADT
Nebraska DOT Traffic	2020 AADT	Other resource here
Nevada DOT Traffic	2020 AADT	Static maps here
New Hampshire DOT Traffic	2020 AADT
New Jersey DOT Traffic	2019 (some) AADT	Can download shapefile here
New Mexico DOT Traffic	2021 (some) AADT	Static maps here
New York DOT Traffic	2019 AADT	Can download shapefile here
North Carolina DOT Traffic	2019 AADT	Can download shapefile; Static map available
North Dakota DOT Traffic	2020 (some) AADT	Static map here
Ohio DOT Traffic	2020 AADT
Oklahoma DOT Traffic	2020 AADT	Can download shapefile here
Oregon DOT Traffic	2019 AADT
Pennsylvania DOT Traffic	2020 AADT	Static maps here
Rhode Island DOT Traffic	2016 AADT
South Carolina DOT Traffic	2020 AADT
South Dakota DOT Traffic	2020 ADT
Tennessee DOT Traffic	2021 (some) AADT
Texas DOT Traffic	2020 AADT
Utah DOT Traffic	2019 AADT	Data in KMZ file
Vermont DOT Traffic	2020 (some) AADT
Virginia DOT Traffic	2020 ADT
Washington DOT Traffic	2020 AADT
West Virginia DOT Traffic	2017 (some) AADT	Other map here
Wisconsin DOT Traffic	2020(some) AADT
Wyoming DOT Traffic	2019 AADT

The DOT datasets above hit the data sleuthing jackpot in that they are free traffic count data sources as well as trustworthy and fairly current. If you have to have 2021 data, skip to the What traffic count data do we use in Cubit’s reports & maps? section below. It can be a bit of pain to incorporate DOT traffic data into your business documents, but it’s nothing that you can’t solve with a good GIS (mapping software) or worst-case scenario, a screen capture tool. 

Need traffic counts for the entire US?

DOT traffic count data are by definition just for 1 state, but some projects require multi-state traffic counts. The Federal Highway Administration provides spatial data for traffic counts for the entire US. The last time I emailed them (November 2021), the shapefiles contained 2019 AADT. I really like the FHWA data, but what I hear from my clients is that there’s a huge learning curve to working with shapefiles. So if you aren’t already familiar with shapefiles, consider sticking with the state DOT resources in the table above or working with us at Cubit to build you a custom map with traffic data.

Don’t have time to learn about AADT, GIS, shapefiles, etc.?

I hear you! Sure, you could pull traffic data yourself. Or for $124, you can get a radius report with traffic data. In fact, our clients order more traffic data reports than any other radius report customization – it’s that popular! Since 2009, these same clients outsource these data pulling tasks and research to us at Cubit so they can focus on building their businesses. 

What traffic count data do we use in Cubit’s reports & maps?

Unlike most of our demographic data that comes directly from government data sources, we use current (2021) traffic estimates from Kalibrate, a private data vendor, first because:

1. Kalibrate sources their data from public & private data sources. This means that for certain areas, they have more traffic counts than public data sources alone.
2. Kalibrate’s data are consistent across states. If you need a radius report in New York City where you want to see traffic counts for both New York and New Jersey, your traffic data will be consistent rather than using 2020 New York data versus 2019 New Jersey data.

Second, if the Kalibrate traffic data doesn’t have enough current traffic counts for a particularly rural area, then we double-check with either the federal dataset or the state DOT datasets (above). So you get the best of both worlds – current data from a private data source supplemented by government data.

Got questions about traffic counts? Email me, because I’m here to help.

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Check out this map of the 100 fastest growing cities with populations of 50,000 or greater in the US from 2013 to 2014.

The data are from the US Census Bureau’s 2014 Population Estimates that were released in May 2015.

The top 20 fastest growing US cities from 2013 to 2014 are listed in the Chart below.

#	Geography	Population Estimate (as of July 1)		Change, 2013 to 2014
		2013	2014	Number	%
1	San Marcos city, Texas	54,567	58,892	4,325	7.9
2	Georgetown city, Texas	54,934	59,102	4,168	7.6
3	Doral city, Florida	50,594	54,116	3,522	7
4	Frisco city, Texas	137,062	145,035	7,973	5.8
5	South Jordan city, Utah	59,379	62,781	3,402	5.7
6	Conroe city, Texas	62,591	65,871	3,280	5.2
7	McKinney city, Texas	149,168	156,767	7,599	5.1
8	Milpitas city, California	70,110	73,672	3,562	5.1
9	Meridian city, Idaho	83,515	87,743	4,228	5.1
10	Castle Rock town, Colorado	53,152	55,747	2,595	4.9
11	Dublin city, California	52,162	54,695	2,533	4.9
12	Irvine city, California	237,111	248,531	11,420	4.8
13	New Braunfels city, Texas	63,365	66,394	3,029	4.8
14	Pleasanton city, California	74,248	77,682	3,434	4.6
15	Buckeyetown, Arizona	56,899	59,470	2,571	4.5
16	Chino city, California	81,083	84,723	3,640	4.5
17	Ankeny city, Iowa	51,551	53,801	2,250	4.4
18	Lake Elsinore city, California	57,542	60,029	2,487	4.3
19	Fort Myers city, Florida	68,096	70,918	2,822	4.1
20	Mount Pleasant town, South Carolina	74,735	77,796	3,061	4.1

Download the top 100 city list as an Excel file.

Or if you need to look up the 2014 population for your city, you can find that data for free by clicking on your state and then navigation to your city: https://www.cubitplanning.com/data/quick-reports

Texas and California respectively contain 6 of the top 20 fastest growing Cities.

Since I live in central Texas (Austin to be precise), I’m not surprised to see San Marcos, Georgetown or New Braunfels on the above list. I’m seeing tremendous changes in the smaller cities surrounding Austin, especially as the housing prices & rent rates in Austin are increasing.

Got a question? Contact us.

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Once in a blue moon, I get a custom data request to use the most current US Census data to produce estimates for Nielsen’s designated market areas or DMAs.

If you can buy data from Nielsen, you should, because they are the only definitive source for DMAs. That said, I’ve tried to purchase data from Nielsen multiple times and they’ve never called or emailed me back. So if you can’t get Nielsen to call you back, you can use the following process.

Nielsen defines “a DMA region is a group of counties that form an exclusive geographic area in which the home market television stations hold a dominance of total hours viewed.”

Given that DMAs are groups of counties and the US Census Bureau publishes estimates for counties, then all you have to do is:

1. Identify the counties that make up the DMA;
2. Pull the most current Census demographics for those counties; &
3. Sum up the estimates.

Easy peasey, right? There are 12 DMAs that split counties. See the map below for 2 examples of DMAs that split counties.

List of DMAs that Split Counties

Oneida County	Utica
	Syracuse
Lea County	Odessa-Midland
	Albuquerque-Santa Fe
Apache County	Phoenix
	Albuquerque-Santa Fe
Kern County	Bakersfield
	Los Angeles
Riverside County	Los Angeles
	Palm Springs
Solano County	San Francisco- Oakland – San Jose
	Sacramento – Stockton – Modesto
El Dorado County	Reno
	Sacramento – Stockton – Modesto

So if you don’t need data for these problem DMAs, you can google search for a mapping of DMAs to counties (like this one). Then you can pull the most current Census data from census.gov (or we sell the most popular demographic data points for all US counties for $199 as an instant download here) and then use a handy SUMIF function in Excel to produce your estimates.

But if you need demographics for all DMAs or at least 1 DMA that splits counties, here’s how you can handle these problem geographies.

1. Produce a shapefile with DMA Boundaries

For the 199 DMAs that play nice with county boundaries

 A. Start with these boundaries: https://github.com/simzou/nielsen-dma

 B. Fix Rochester, MN and Rochester, NY (which are swapped) and created boundaries for Anchorage, AK, Juneau, AK, Fairbanks, AK and Honolulu, HI (which were missing).

 C. Doublecheck your work using Nielsen documentation and make any needed corrections.

The 11 DMAs that split counties

1. In the file that you’ve created above, swap the boundaries for these 11 DMAs listed in this blog post above with the boundaries in the 2008 shapefile provided by Harvard here.
2. Doublecheck your work using Nielsen documentation and make any needed corrections.

2. Intersections with Census geographies.

Now that you have a DMA spatial file that you are happy with, run intersections with Census geographies.

1. For the 199 DMAs that play nice with counties, produce a county to DMA list.
2. For the 11 DMAs that split counties, run intersections between the DMA and Census tracts and calculate the area percent overlap. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people.

 3. Estimating Demographics

1. For the 199 DMAs that play nice with counties, sum the county estimates.
2. For the 11 DMAs that split counties, multiply the Census tract estimates by the percent overlap. If 10% of the area of a Census tract is in the Reno DMA, then assign 10% of the population in that Census tract to the Reno DMA. Thoughts: there are more accurate ways to solve this, but this one works pretty well for the level of effort involved.
3. You can use this formula for median calculations like median income or median age.

4. QA

1. Compared your results to Nielsen’s data. For example, New York, Los Angeles, & Chicago are the DMAs with the largest populations. Are your 3 largest DMAs also New York, Los Angeles & Chicago?
2. Consider calculating a percent difference between Nielsen’s Metro age 12+ population for 2019 and the Census Bureau’s 2018 age 10+ population. This percent difference would show you if any of your estimates are off.

Want to geek out more about Census demographics or DMAs or anything else data related?

Either fill out the Custom Data Request form or call me, Kristen, at 1-800-939-2130.

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Zoom around on this map to explore the 100 fastest growing counties in the US from 2013 to 2014.

The data are from the US Census Bureau’s 2014 Population Estimates that were released on March 26, 2015.

The top 20 fastest growing US counties from 2013 to 2014 are listed in the Chart below.

Rank	Geography	Percent Change, 2013 to 2014
1	Williams County, North Dakota	8.7
2	Stark County, North Dakota	7
3	Sumter County, Florida	5.4
4	Pickens County, Alabama	5.1
5	Hays County, Texas	4.8
6	Fort Bend County, Texas	4.7
7	Forsyth County, Georgia	4.6
8	Wasatch County, Utah	4.3
9	Comal County, Texas	4
10	Morgan County, Utah	4
11	St. Johns County, Florida	4
12	Andrews County, Texas	4
13	Montgomery County, Texas	3.8
14	Williamson County, Texas	3.8
15	Kendall County, Texas	3.8
16	Walton County, Florida	3.6
17	Dallas County, Iowa	3.6
18	Osceola County, Florida	3.6
19	Richland County, Montana	3.5
20	Loudoun County, Virginia	3.4
Source: US Census Bureau, 2014 Population Estimates, Released March 26, 2015.

Download the top 100 county list as an Excel file.

Or if you need to look up the 2014 population for your county, you can find that data for free by 1. clicking on your state and then 2. navigating to your county’s page starting here: https://www.cubitplanning.com/data/quick-reports

Texas contains 7 of the top 20 fastest growing counties while Florida has 4 counties.

Source: US Census Bureau, 2014 Population Estimates, Released March 26, 2015.

Unsurprising: Texas and Florida
As a Central Texan who lives near to Hays, Comal & Williamson Counties, I’m not surprised to see these 3 counties in the top 20 fastest growing list. With solid job opportunities and a reasonable cost of living, continued population growth in Texas is likely.

As for Florida, I’d be interested to pull age data for the new residents. Is this Florida population growth due to Baby Boomer retirees, or are families and young couples moving there in search of job opportunities?

Surprising: North Dakota and Where’s California?
With 2 North Dakota counties at the top of the fastest 100 growing counties list, it appears that the North Dakota oil boom continued to attract workers through 2014. And surprisingly, no California counties appear in the top 20 list this year.

Got questions? Contact us.

I was working on a custom data request for a new business owner who was considering a particular location for a new venture. This owner wanted California traffic count data for the highway near the potential location. Here’s how I got traffic count data for this California state highway.

What’s a Postmile(& Why Should You Care)?
Wikipedia says “California uses a postmile highway location marker system on all of its state highways, including U.S. Routes and Interstate Highways. The postmile markers indicate the distance a route travels through individual counties, as opposed to mile markers that indicate the distance traveled through a state.” You should care about Postmiles, because you need to know the postmile to look up the traffic count data.

A. How to Lookup the Postmile

1. Go to Caltrans Earth – which is a GIS provided by the California Department of Transportation.
   1. You will probably have to download the Earth for Web plugin. Clicking on the download plugin link provided by Caltrans didn’t work for me. So I searched for “GoogleEarth-Mac-Plugin-Latest.dmg” and found this site with the Mac version of the Google Earth Plugin. If you are running a PC, you’ll need to find the PC plug-in.
2. Once you get the Caltrans Earth working, enter your area of interest to zoom to location.
   1. Note: since the data are state highways, your area of interest will likely be a state highway AND an intersecting street. The intersecting street can be a smaller road or it can also be another state highway. I’m going to use Santa Monica (Route 2) and North Crescent Heights in Los Angeles County as an example.
3. Click on Postmile look up.
4. WithClick selected in the Postmile look up tool, click on the map where your area of interest is. So in my example, I’m clicking on the intersection of Santa Monica (2) & North Crescent Heights. (You can turn on the Roads in the left navigation to see the street names).
5. The map will then show you the closest County Postmile.

B. How to Use the Postmile to look up Traffic Counts

1. Download the most current traffic volume data from the Caltrans Traffic Data Branch. The most current data that I could find was 2012. The department will release 2013 and 2014 traffic data as soon as it’s available, but for my purposes, 2012 is the best data available & will work.
2. Here’s the 2012 traffic counts that we can use as an example. Looking at the data look first at the Route, then the County, and then the nearest post mile.

For our example, I’d want to double check that Highland Avenue is close to North Crescent Heights. And according to Google maps, Highland Avenue is 4 minutes or 1.6 miles away from North Crescent Heights.

C. How to Read the Traffic Count Data

1. This handy Caltrans webpage provides us with some important definitions.
   1. Back AADT, Peak Month, and Peak Hour usually represents traffic South or West of the count location.
   2. Ahead AADT, Peak Month, and Peak Hour usually represents traffic North or East of the count location.
   3. Annual Average Daily Traffic(Annual ADT) is the total volume for the year divided by 365 days.
   4. Peak Hour is an estimate of the “peak hour” traffic at all points on the state highway system.
   5. Peak Month ADT is the average daily traffic for the month of heaviest traffic flow.

   So with the above resources, you are now able to get traffic counts for California state highways. While Caltrans does not collect traffic data on locally maintained roads, oftentimes the city or county has a traffic engineer who has this data. If you need traffic counts for local roads, you should call the City and ask to speak with either the traffic engineer, Public works department or the Planning department.