AI Applications for Automatic Pavement Condition Evaluation (2024)
Chapter: Appendix B: Agency Survey Responses
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Previous Chapter:
Appendix A: Agency Survey Questionnaire
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 |
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 |
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 |
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 |
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 |
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 |
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 |
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 |
|---|---|---|
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 | |||
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 |
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 |
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 |
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.
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, |
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.
| 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 |
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.
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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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