Final Scope

In the last few years, pothole detection and information transfer techniques have come a long way. State DOTs have begun using some of these new techniques, and there is considerable benefit to making this information available to other DOTs looking to upgrade their pothole detection and repair processes. Potholes present a significant challenge for roadway maintenance by affecting road safety, causing traffic congestion and vehicle damage, and affecting driver comfort. State DOTs are tasked with the identification, prioritization, and timely repair of these road defects. However, practices for managing this task vary among state DOTs due to differences in climate, traffic volume, and available resources.

Although they share a goal of rapidly responding to pothole repairs, state DOTs exhibit a variety of approaches in the collection and management of data relevant to identifying, prioritizing, and addressing these road defects. These differences in methods range from the use of advanced technologies, such as vision-based mapping and mobile sensor data, to more traditional methods like public reporting and manual inspections. In addition, DOTs frequently encounter high volumes of pothole repair requests, particularly in seasons prone to significant freeze/thaw cycles. Such seasonal challenges underscore the need for effective maintenance strategies, but these challenges also raise questions regarding the practices adopted by different DOTs for the early detection of potholes and the monitoring of areas susceptible to their formation. This divergence in data collection and management practices highlights the need for a synthesis to document the range of practices employed by state DOTs, with the goal of identifying those that promote efficient, effective, and rapid pothole repairs. This synthesis seeks to explore these practices.

The objective of this synthesis is to document current state DOT practices and requirements for the collection, management, and utilization of data in the process of pothole repair, focusing on the technological and methodological approaches to data collection, prioritization algorithms, and management systems that facilitate pothole repairs.

Information to be gathered includes (but is not limited to):

• Definitions of potholes (e.g., diameter, depth, area, severity, concrete, and asphalt) and their differences across state DOTs, especially for potholes that will trigger action;
• Data collection technologies used for identifying potholes (e.g., crowdsourcing, mobile sensors, drones, public reporting systems, and internal reporting systems);
• Criteria and algorithms for prioritizing pothole repairs (e.g., size, location, traffic volume, and duplicated requests/tickets for the same location);
• Detection frequency, its determination and effect, and how seasonal variations are reflected in performance.
• How and when detection information is conveyed to responsible maintenance resources for action;
• Data management systems used to track and coordinate maintenance requests and repair activities;
• Integration of data collection and management practices with management systems;
• Identification of how state DOTs are collecting data and conducting repairs, either by in-house worker or contract?
• Identification of how state DOTs track performance of pothole patching;
• Identification of key performance indicators of the current technologies and methodologies used by state DOTs;
• Identification of incentives/penalties and risk mitigation strategies;
• Data collection/management challenges faced by DOTs for early pothole detection, identification of pothole-prone areas, and pothole repair efforts.

Information will be gathered through a literature review, a survey of state DOTs, and follow-up interviews with selected DOTs for the development of case examples of state DOTs implementing strategies for data collection and management in pothole repair. Information gaps and suggestions for research to address those gaps will be identified. 

Information Sources (Partial)

• Chougule, S., and Barhatte, A. (2023). “Smart Pothole Detection System using Deep Learning Algorithms.” International Journal of Intelligent Transportation Systems Research, Volume 21, Issue 3, pp 483-492, https://trid.trb.org/view/2292501
• Ranyal, E., Sadhu, A., and Jain, K. (2023).  “Automated pothole condition assessment in pavement using photogrammetry-assisted convolutional neural network.” International Journal of Pavement Engineering, Volume 24, Issue 1, 2183401, https://trid.trb.org/view/2310698
• FHWA. Start date: 21 Dec. 2023. “Exploring the Use of Ground-Based Robotic Assistance in Uncrewed Operations of State DOTs.” https://trid.trb.org/view/2307269
• Office of the Assistant Secretary for Research and Technology. Start date: 18 Feb. 2020. “Visible and Thermal Imaging in a Deep-Learning Approach to Robust Automated Pothole Detection and Highway Maintenance Prioritization.” https://trid.trb.org/view/1691153
• Sharma, N., and Garg, R. D. (2023). “Real-Time IoT-Based Connected Vehicle Infrastructure for Intelligent Transportation Safety.” IEEE Transactions on Intelligent Transportation Systems, Volume 24, Issue 8, pp 8339-8347, https://trid.trb.org/view/2224193
• Talha, S. A., Karasneh, M. A., Manasreh, D., Al Oide, A., and Nazzal, M. D. (2023). “A LiDAR-camera fusion approach for automated detection and assessment of potholes using an autonomous vehicle platform.” Innovative Infrastructure Solutions, 8(10), 274.
• Anastasopoulos, P. Ch., McCullouch, Bob G., Gkritza, K., Mannering, F., and Sinha, K. C. (2010). “Cost Savings Analysis of Performance-Based Contracts for Highway Maintenance Operations.” Journal of Infrastructure Systems, Volume 16, Issue 4, pp 251-263, https://trid.trb.org/view/1084328
• Romero-Chambi E., Villarroel-Quezada S., Atencio E., Muñoz-La Rivera F. (2020). “Analysis of Optimal Flight Parameters of Unmanned Aerial Vehicles (UAVs) for Detecting Potholes in Pavements.” Applied Sciences. 10(12):4157. https://doi.org/10.3390/app10124157
• Aarabi, F., and Batta, R. (2020). “Scheduling spatially distributed jobs with degradation: Application to pothole repair.” Socio-Economic Planning Sciences, 72, 100904.

TRB Staff
Zhiye Li
Email: Zli@nas.edu

Meeting Dates
First Panel Meeting: 02/25/2025 (Virtual, via Zoom)
Teleconference with Consultant: 04/15/25 (Virtual, via Zoom)
Second Panel Meeting: 12/09/25 (In-person, in Washington, DC)

Panel Members
David Bratton, Wisconsin Department of Transportation
Wilson Espinoza, Texas State University, San Marcos
Christopher C. Harris, Tennessee Department of Transportation
Nooralhuda F. Saleh, Continuum Infrastructure Solutions
Kerstin IL Tomlinson, California Department of Transportation
Zhaoxing Xie, Maryland Department of Transportation 
Antonio Nieves Torres, Federal Highway Administration (FHWA)