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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

APPENDIX B

Agency Survey Responses

Responding DOTs (43):

Alabama DOT
Alaska DOT and Public Facilities
Arizona DOT
Colorado DOT
Connecticut DOT
Delaware DOT
Florida DOT
Georgia DOT
Idaho Transportation Department
Illinois DOT
Indiana DOT
Iowa DOT
Kentucky Transportation Cabinet
Maine DOT
Maryland DOT
Michigan DOT
Minnesota DOT
Mississippi DOT
Missouri DOT
Montana DOT
Nebraska DOT
Nevada DOT
New Hampshire DOT
New Jersey DOT
New Mexico DOT
New York State DOT
North Carolina DOT
North Dakota DOT
Ohio DOT
Oklahoma DOT
Oregon DOT
Rhode Island DOT
South Carolina DOT
South Dakota DOT
Tennessee DOT
Texas DOT
Utah DOT
Vermont DOT
Virginia DOT
Washington State DOT
West Virginia Division of Highways
Wisconsin DOT
Wyoming DOT

Does your agency conduct an automated pavement condition survey to quantify pavement surface distress (excludes inertial profile measurements)?

Response	Agencies	Count
Yes	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	38
No	Maine, Nevada, New Jersey, South Dakota, and Wisconsin	5
	No. DOTs	43

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Does your agency use other technology to complement the automated pavement condition survey (e.g., smartphones to track potholes, more frequent assessment of safety-related distress)?

Response	Agencies	Count
Yes	Iowa: Falling weight deflectometer and friction testing. Kentucky: LCMS, light detection and ranging (LiDAR), Sideway-force Coefficient Routine Investigation Machine (SCRIM), and visual survey. Montana: Right-of-way imagery and mobile LiDAR. Ohio: PCR. Texas: Visual rating for the audit sections (about 6% of network).	5
No	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Idaho, Illinois, Indiana, Maryland, Minnesota, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	31
Not sure	Georgia and Michigan	2
	No. DOTs	38

Do you think the new technology (e.g., autonomous vehicle, crowdsource) may be used for future AI-based automated pavement condition data collection?

Response	Agencies	Count
Not sure	Arizona, Connecticut, Delaware, Iowa, Maryland, Michigan, Mississippi, New Hampshire, New York, North Carolina, North Dakota, Ohio, Oklahoma, Rhode Island, South Carolina, Vermont, West Virginia, and Wyoming	18
Yes	Alaska, Florida, Georgia, Idaho, Illinois, Indiana, Kentucky, Minnesota, Montana, Nebraska, New Mexico, Tennessee, Texas, Virginia, and Washington	15
No	Alabama, Colorado, Missouri, Oregon, and Utah	5
	No. DOTs	38

Does your agency (or vendor) use AI technology to process the automated pavement condition survey?

Response	Agencies	Count
No	Alaska, Connecticut, Florida, Indiana, Kentucky, Maryland, Minnesota, Mississippi, Montana, Nebraska, New Mexico, North Carolina, Ohio, Rhode Island, Virginia, Washington, West Virginia, and Wyoming	18
Not sure	Arizona, Colorado, Delaware, Idaho, Illinois, Michigan, Missouri, Oregon, South Carolina, Texas, Utah, and Vermont	12
Yes	Alabama, Georgia, Iowa, New Hampshire, New York, North Dakota, Oklahoma, and Tennessee	8
	No. DOTs	38

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Would your agency consider using AI in the future to collect and process the automated pavement condition data?

Response	Agencies	Count
Yes	Alaska, Connecticut, Delaware, Florida, Idaho, Illinois, Indiana, Kentucky, Maryland, Michigan, Minnesota, Montana, North Carolina, Oregon, Texas, Utah, Vermont, and Virginia	18
Not sure	Arizona, Colorado, Mississippi, Missouri, Nebraska, New Mexico, Ohio, Rhode Island, South Carolina, Washington, West Virginia, and Wyoming	12
	No. DOTs	30

Is the automated pavement condition survey conducted using agency equipment and staff or contracted through a vendor?

Response	Agencies	Count
Vendor	Alabama, Alaska, Arizona, Colorado, Delaware, Indiana, Michigan, Mississippi, New Hampshire, New Mexico, New York, North Carolina, Oklahoma, Oregon, Rhode Island, Tennessee, Texas, Utah, Vermont, Virginia, West Virginia, and Wyoming	22
Agency	Connecticut, Florida, Idaho, Kentucky, Maryland, Minnesota, Missouri, Montana, Nebraska, North Dakota, Ohio, and Washington	12
Agency and vendor	Georgia, Illinois, Iowa, and South Carolina	4
	No. DOTs	38

Please indicate the vendor or equipment provider your agency uses for the automated pavement condition survey.

Response	Agencies	Count
Vendor A	Alabama, Georgia, Idaho, Indiana, Iowa, Michigan, Minnesota, Montana, Nebraska, New Hampshire, North Dakota, Ohio, Oregon, Rhode Island, Texas, Utah, and Washington	17
Vendor B	Alaska, Colorado, Connecticut, Maryland, Missouri, New Mexico, New York, North Carolina, South Carolina, Vermont, Virginia, and West Virginia	12
Vendor C	Arizona, Delaware, Florida, and Wyoming	4
Vendor D	Illinois, Kentucky, Oklahoma, and Tennessee	4
Vendor E	Mississippi	1
	No. DOTs	38

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Requirements for automated pavement distress identification include (select all that apply):

Response	Agencies	Count
Distress type	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	38
Distress severity	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	38
Linear referencing system	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	37
Distress extent	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Mississippi, Missouri, Montana, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	35
Reporting interval	Alabama, Alaska, Arizona, Connecticut, Delaware, Florida, Georgia, Idaho, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, New Hampshire, New Mexico, New York, North Carolina, Ohio, Oregon, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	31
Data repeatability	Alabama, Alaska, Arizona, Colorado, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, South Carolina, Utah, Vermont, Virginia, Washington, and Wyoming	30
Precision and accuracy	Alabama, Alaska, Arizona, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Mississippi, Missouri, Montana, Nebraska, New Mexico, New York, North Carolina, North Dakota, Ohio, Rhode Island, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	29
Compatibility with existing PMS	Alabama, Alaska, Arizona, Connecticut, Delaware, Georgia, Idaho, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Mexico, New York, North Carolina, Oregon, Rhode Island, South Carolina, Texas, Vermont, Virginia, Washington, and Wyoming	26

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Response	Agencies	Count
Spatial resolution	Alabama, Alaska, Delaware, Florida, Georgia, Idaho, Indiana, Iowa, Kentucky, Maryland, Michigan, Mississippi, Missouri, Montana, New Mexico, North Carolina, Ohio, Washington, and Wyoming	19
Sampling rate Productivity (miles/day)	Alabama, Alaska, Delaware, Idaho, Indiana, Iowa, Kentucky, Maryland, Missouri, Montana, New Mexico, New York, North Carolina, and Ohio Idaho, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Nebraska, New Mexico, North Carolina, and Wyoming	14 11
Additional Comments:	Delaware: Based on agency data dictionary. Oregon: Based on agency protocols.	2
	No. DOTs	38

What data format requirements are needed for AI training/post-processing?

Response	Agencies	Count
Not sure	Alaska, Arizona, Colorado, Connecticut, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Missouri, Nebraska, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, South Carolina, Texas, Utah, Vermont, Washington, West Virginia, and Wyoming	29
No additional requirements	Alabama, Montana, New Hampshire, North Dakota, Tennessee, and Virginia	6
Other	Florida: Pavemetrics libraries for most, range jpgs for raveling	1
	No. DOTs	36

What image quality requirements are needed to conduct AI training/post-processing?

Response	Agencies	Count
Not sure	Alabama, Arizona, Connecticut, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Michigan, Minnesota, Missouri, Nebraska, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Texas, Utah, Vermont, Washington, West Virginia, and Wyoming	28
No additional requirements	Montana, New Hampshire, North Dakota, Tennessee, and Virginia	5
Other	Alaska: likely at least 2mm resolution based on Federal Aviation Association research. Colorado: Need similar resolution images. Older images are not as high resolution as new images. Florida: 4mm x 4mm pixel resolution for range image Maryland: Image size and resolution. Free of artifacts and Shadows	4
	No. DOTs	37

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Pavement surface condition data are used for (select all that apply):

Response	Agencies	Count
HPMS reporting	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	38
Pavement performance modeling	Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	36
Pavement condition rating or index	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	36
MAP-21 reporting	Alabama, Alaska, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and West Virginia	33
Detecting individual distress types	Alabama, Alaska, Colorado, Connecticut, Delaware, Florida, Idaho, Illinois, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, New Mexico, New York, North Dakota, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Virginia, Washington, and Wyoming	29
Detecting prevalent distress type	Alabama, Colorado, Delaware, Florida, Idaho, Illinois, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Mexico, New York, North Dakota, Tennessee, Texas, Utah, Virginia, and Wyoming	22
Road safety assessment	Georgia, Illinois, Kentucky, Maryland, Michigan, Minnesota, Missouri, Texas, Utah, Washington, and Wyoming	11
Other	Alabama: Screening potential preservation treatments (e.g., microsurfacing might require a second pass in the wheel paths if rutting is > 0.25-in.). Oregon: State KPM pavement condition performance measure. Texas: Support the development of pavement management plans.	3
	No. DOTs	38

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Pavement condition data is used to support the following agency decision-making activities (select all that apply):

Response	Agencies	Count
Verify performance models	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	38
Multi-year budget planning (network)	Alabama, Arizona, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New York, North Carolina, North Dakota, Oregon, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	31
Treatment selection	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Montana, New Mexico, New York, North Carolina, North Dakota, Oregon, Rhode Island, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	31
Targeted performance goals	Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, Oklahoma, Oregon, South Carolina, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	31
Establish performance targets	Alabama, Alaska, Connecticut, Delaware, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, Montana, Nebraska, New Mexico, New York, North Carolina, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	31
Budgeting	Alaska, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Minnesota, Missouri, Montana, New York, North Carolina, North Dakota, Oregon, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, and Wyoming	27
Contract performance specifications and measures	Idaho, Kentucky, Maryland, Michigan, Minnesota, Mississippi, Missouri, New Mexico, New York, North Carolina, Texas, Utah, Vermont, and Washington	14
Trigger safety-related repairs	Illinois, Iowa, Kentucky, Maryland, Minnesota, Nebraska, New Mexico, Texas, Vermont, and Washington	10
Approximate bid quantities (project)	Kentucky, Maryland, Montana, New Mexico, New York, North Carolina, Utah, Washington, and Wyoming	9
Other	Alabama: Used in transportation asset management plan scenarios.	4

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Response	Agencies	Count
	Maryland: Asset management and system preservation. Ohio: Data used for federal reporting purposes. South Carolina: Contract performance.
	No. DOTs	38

Response

Agencies

Count

Maryland: Asset management and system preservation.

Ohio: Data used for federal reporting purposes.

South Carolina: Contract performance.

No. DOTs

38

What asphalt-surfaced pavement condition types does your agency evaluate using AI technology (select all that apply)?

Response	Agencies Who Use AI	Count	Agencies Who Are Unsure if AI Is Used	Count
Longitudinal cracking	Alabama, Iowa, New Hampshire, New York, North Dakota, Oklahoma, and Tennessee	7	Arizona, Delaware, Idaho, South Carolina, and Vermont	5
Transverse cracking	Alabama, Iowa, New Hampshire, New York, North Dakota, Oklahoma, and Tennessee	7	Arizona, Delaware, Idaho, South Carolina, and Vermont	5
Alligator cracking	Iowa, New York, North Dakota, Oklahoma, and Tennessee	5	Arizona, Delaware, Idaho, and Vermont	4
Block cracking	New York, North Dakota, Oklahoma, and Tennessee	4	Arizona, Delaware, Idaho, and Vermont	4
Delamination / potholes	New York, North Dakota, Oklahoma, and Tennessee	4	Idaho	1
Patching	Alabama, Oklahoma, and Tennessee	3	Delaware, Idaho, and South Carolina	3
Edge cracking	Iowa and New Hampshire	2	Arizona, Delaware, and Idaho	3
Raveling	Oklahoma	1	Arizona and South Carolina	2
Bleeding	None	0	Delaware	1
Weathering	None	0	None	0
Other	Alabama: Wheel path and non-wheel path cracking rather than alligator cracking. South Carolina: Wheel path cracking distress varieties all grouped as fatigue cracking. Utah: AI usage (excluding vendor efforts) has been research related and focused on cracking, potholing, and patching. Washington: Use the automated crack rating system from vendor to perform a quality acceptance check against agency visual rating.			4
			No. DOTs	14

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

What concrete-surfaced pavement condition types does your agency evaluate using AI technology (select all that apply)?

Response	Agencies Who Use AI	Count	Agencies Who Are Unsure if AI Is Used	Count
Transverse cracking	Alabama, Iowa, New York, North Dakota, Oklahoma, and Tennessee	6	Connecticut, Delaware, Idaho, and Utah	4
Longitudinal cracking	Iowa, New York, North Dakota, Oklahoma, and Tennessee	5	Connecticut, Delaware, Idaho, and Utah	4
Spalling	New York, North Dakota, Oklahoma, and Tennessee	4	Utah	1
Corner cracking	Alabama, North Dakota, and Oklahoma	3	0	0
Multi-cracked slabs	Iowa, North Dakota, and Oklahoma	3	0	0
Patching	Oklahoma and Tennessee	2	Delaware and Utah	2
Punchout	Alabama and Oklahoma	2	None	0
Map cracking	New York	1	None	0
Joint seal damage	None	0	Delaware and Idaho	2
Blowups	None	0	None	0
Polished aggregate	None	0	None	0
Pumping	None	0	None	0
Scaling	None	0	None	0
Other	Delaware: alkali-silica reactivity. Washington: Use the automated crack rating system from vendor to perform a quality acceptance check against agency visual rating.			2
			No. DOTs	11

What other roadway features does your agency assess using AI technology (select all that apply)?

Response	Agencies	Count
Excess vegetation growth	Arizona, Idaho, Iowa, New York, Vermont, and Wyoming	6
Right-of-way (e.g., slope, embankment)	None	0
Roadside assets (e.g., markings, signs)	None	0
Other	Georgia and Utah: evaluating AI technology for roadside assets and/or excess vegetative growth.	2
	No. DOTs	8

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

What AI technologies, tools, and models does your agency currently use for pavement condition evaluation (select all that apply)?

Response	Agencies	Count
Not sure	Arizona, Georgia, Idaho, Iowa, Michigan, Missouri, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Oklahoma, Oregon, South Carolina, and West Virginia	16
Random forest	Florida and Tennessee	2
Machine learning	Alabama and Vermont	2
Neural network	Montana	1
Pattern recognition (e.g., data mining)	Delaware	1
Deep learning		0
Other	Texas: Conducting research to examine the various AI models. Traditional statistical analysis is currently used. Utah: Research project has mostly evaluated machine learning and deep learning.	2
	No. DOTs	24

How does your agency conduct AI-technique development, training, and evaluation (e.g., ground truth testing) (select all that apply)?

Response	Agencies	Count
Not sure	Georgia, Idaho, Illinois, Iowa, Michigan, Missouri, Nebraska, New Mexico, North Carolina, Oregon, South Carolina, Texas, Vermont, West Virginia, and Wyoming	15
Accuracy, precision, and repeatability	Arizona, Delaware, Montana, New York, Oklahoma, and Utah	6
Compare to manual surveys	Alabama, Montana, New Hampshire, North Dakota, and Washington	5
Pre-defined reference sections	Alabama, Montana, and New York	3
Random reference sections	Alabama, Montana, and Oklahoma	3
Compare to traditional automated pavement condition survey	Montana and Utah	2
Other	Florida: manual image classification. Tennessee: Distress library was used to train the models. We do not have a ground truth test section.	2
	No. DOTs	27

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Is your agency’s AI process in accordance with AASHTO R 85-18?

Response	Agencies	Count
Yes	New Hampshire and Oregon	2
No	Texas and Wyoming	2
Not sure	Arizona, Delaware, Florida, Georgia, Idaho, Illinois, Iowa, Missouri, Montana, Nebraska, New Mexico, New York, North Carolina, North Dakota, Oklahoma, South Carolina, Tennessee, Utah, Vermont, and West Virginia	20
Other	Alabama: very close to R85; however, wheel paths are 3 ft wide. Michigan: Vendor required to follow the basis of R85 with modifications.	2
	No. DOTs	26

Can your agency’s AI process be used on historical records (i.e., archived videos or images)?

Response	Agencies	Count
Yes	Montana, North Dakota, and Utah	3
No	Idaho, New York, Oregon, and Washington	4
Not sure	Alabama, Arizona, Delaware, Georgia, Illinois, Iowa, Michigan, Missouri, Nebraska, New Hampshire, New Mexico, North Carolina, Oklahoma, South Carolina, Tennessee, Vermont, and West Virginia	17
Other	Florida: only if previous surveys used an LCMS to collect the data.	1
	No. DOTs	25

What challenges does your agency have with the current AI process for automated pavement condition surveys (select all that apply)?

Response	Agencies	Count
Limited agency knowledge	Alaska, Florida, Georgia, Idaho, Iowa, Maryland, Michigan, Montana, New Mexico, North Carolina, South Carolina, Tennessee, Utah, and Vermont	14
Ground truth testing	Alabama, Arizona, Florida, Idaho, Maryland, Michigan, New Hampshire, Tennessee, Texas, and Vermont	10
Trusting results	Alabama, Alaska, Florida, Maryland, Montana, New Hampshire, Tennessee, Utah, and Vermont	9
AI training	Florida, Idaho, Maryland, Oklahoma, Tennessee, and Washington	6
Computer computation capabilities	Florida, Idaho, Missouri, Montana, and Washington	5
Not sure	Oregon and Wyoming	2
Other	Alaska: AI is not currently used and has a limited understanding of how it could be implemented. Delaware: rely on vendor expertise.	5

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Georgia: 2023 was the first year AI was used.

Michigan: AI used by vendor; uncertain of utilization and challenges.

Washington: current AI algorithm is having difficulties distinguishing between multiple crack types in each area as well as asphalt longitudinal cracking at lane edge, and shoulder paint or rumble strips (quantifies as cracks).

No. DOTs

24

What are the agency benefits of using AI for processing the automated pavement condition survey (select all that apply)?

Response	Agencies	Count
Objectivity (consistency)	Alabama, Delaware, Georgia, Idaho, Illinois, Iowa, Michigan, Montana, New Hampshire, New York, North Dakota, Tennessee, Vermont, and Washington	14
Accuracy (once trained)	Alabama, Arizona, Idaho, Illinois, Maryland, Montana, New York, North Dakota, Oklahoma, South Carolina, Tennessee, Texas, and Vermont	13
Increased productivity	Arizona, Delaware, Georgia, Idaho, Maryland, Michigan, Montana, New Hampshire, New York, North Dakota, Oklahoma, Tennessee, and Washington	13
Cost	Idaho, New Hampshire, New Mexico, New York, North Dakota, Tennessee, and Utah	7
Not sure	North Carolina, Oregon, and Wyoming	3
Other	Florida: increased resolution	1
	No. DOTs	25

Do you have any other suggestions or comments related to using AI with automated pavement condition surveys?

Response	Agencies	Count
Yes	Alaska: Prior to implementation, need to see results of extensive ground truthing of the processed data. Indiana: More education surrounding advancements made and functionality of current AI systems. Missouri: To survey roadways, you need measurements, measurements provide a definite answer, and AI is not needed for interpretations. Montana: Leverage AI technologies for QA/QC processes. This could be change-detection technology while conducting the surveys and QC processes to review the data. Tennessee: There is a need to establish a standard distress library which can be used for training AI models for distress classification. Texas: Industry standard images (i.e., reporting format) would make it easier for AI learning and results comparison. Utah: AI has shown promise for cracking and asset detection but unable to collect all the required data. Unless paired with other data collection efforts, it would be insufficient to replace current automated data collection methods.	7
No	Alabama, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Iowa, Kentucky, Maryland, Michigan, Minnesota,

Response

Agencies

Count

Yes

Alaska: Prior to implementation, need to see results of extensive ground truthing of the processed data.

Indiana: More education surrounding advancements made and functionality of current AI systems.

Missouri: To survey roadways, you need measurements, measurements provide a definite answer, and AI is not needed for interpretations.

Montana: Leverage AI technologies for QA/QC processes. This could be change-detection technology while conducting the surveys and QC processes to review the data.

Tennessee: There is a need to establish a standard distress library which can be used for training AI models for distress classification.

Texas: Industry standard images (i.e., reporting format) would make it easier for AI learning and results comparison.

Utah: AI has shown promise for cracking and asset detection but unable to collect all the required data. Unless paired with other data collection efforts, it would be insufficient to replace current automated data collection methods.

7

No

Alabama, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Iowa, Kentucky, Maryland, Michigan, Minnesota,

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	Mississippi, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Vermont, Virginia, Washington, West Virginia, and Wyoming	31
	No. DOTs	38

Are you willing to participate in a follow-up interview (via email) in the event additional information or clarification of your responses are needed?

Response	Agencies	Count
Yes	Delaware, Idaho, Iowa, Kentucky, Montana, North Dakota, Tennessee, and Washington	8
No	Alabama, Alaska, Arizona, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Maryland, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, Rhode Island, Texas, Utah, Vermont, Virginia, West Virginia, and Wyoming	28
	No. DOTs	36

The synthesis will also include case examples highlighting agency practices related to AI technology and automated pavement condition surveys. Agencies will be provided the opportunity to review the case example write-up for accuracy. Would your agency be interested in participating in a case example?

Response	Agencies	Count
Yes	Delaware, Idaho, Iowa, Kentucky, Montana, North Dakota, Tennessee, and Washington	8
No	Alabama, Alaska, Arizona, Colorado, Connecticut, Florida, Georgia, Illinois, Indiana, Maryland, Michigan, Mississippi, Missouri, Nebraska, New Hampshire, New Mexico, New York, North Carolina, Ohio, Oklahoma, Oregon, Rhode Island, Texas, Utah, Vermont, Virginia, West Virginia, and Wyoming	28
	No. DOTs	36

If available, please include additional documentation related to AI and automated pavement condition surveys.

Response	Agencies	Count
Provided a file	Not applicable	0
Provided a link	Not applicable	0
No additional information available	Alabama, Alaska, Arizona, Colorado, Connecticut, Delaware, Florida, Georgia, Idaho, Illinois, Indiana, Iowa, Kentucky, Maryland, Michigan, Mississippi, Montana, Nebraska, New Hampshire, New Mexico, New York, North Carolina, North Dakota, Ohio, Oklahoma, Oregon, Rhode Island, South Carolina, Tennessee, Texas, Utah, Vermont, Virginia, Washington, West Virginia, and Wyoming	36
	No. DOTs	36

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.

Abbreviations and acronyms used without definitions in TRB publications:

A4A	Airlines for America
AAAE	American Association of Airport Executives
AASHO	American Association of State Highway Officials
AASHTO	American Association of State Highway and Transportation Officials
ACI–NA	Airports Council International–North America
ACRP	Airport Cooperative Research Program
ADA	Americans with Disabilities Act
APTA	American Public Transportation Association
ASCE	American Society of Civil Engineers
ASME	American Society of Mechanical Engineers
ASTM	American Society for Testing and Materials
ATA	American Trucking Associations
CTAA	Community Transportation Association of America
CTBSSP	Commercial Truck and Bus Safety Synthesis Program
DHS	Department of Homeland Security
DOE	Department of Energy
EPA	Environmental Protection Agency
FAA	Federal Aviation Administration
FAST	Fixing America’s Surface Transportation Act (2015)
FHWA	Federal Highway Administration
FMCSA	Federal Motor Carrier Safety Administration
FRA	Federal Railroad Administration
FTA	Federal Transit Administration
GHSA	Governors Highway Safety Association
HMCRP	Hazardous Materials Cooperative Research Program
IEEE	Institute of Electrical and Electronics Engineers
ISTEA	Intermodal Surface Transportation Efficiency Act of 1991
ITE	Institute of Transportation Engineers
MAP-21	Moving Ahead for Progress in the 21st Century Act (2012)
NASA	National Aeronautics and Space Administration
NASAO	National Association of State Aviation Officials
NCFRP	National Cooperative Freight Research Program
NCHRP	National Cooperative Highway Research Program
NHTSA	National Highway Traffic Safety Administration
NTSB	National Transportation Safety Board
PHMSA	Pipeline and Hazardous Materials Safety Administration
RITA	Research and Innovative Technology Administration
SAE	Society of Automotive Engineers
SAFETEA-LU	Safe, Accountable, Flexible, Efficient Transportation Equity Act: A Legacy for Users (2005)
TCRP	Transit Cooperative Research Program
TEA-21	Transportation Equity Act for the 21st Century (1998)
TRB	Transportation Research Board
TSA	Transportation Security Administration
U.S. DOT	United States Department of Transportation

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Suggested Citation: "Appendix B: Agency Survey Responses." National Academies of Sciences, Engineering, and Medicine. 2024. AI Applications for Automatic Pavement Condition Evaluation. Washington, DC: The National Academies Press. doi: 10.17226/27993.