Preliminary Scope

In the last few years, the 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. DOTs are tasked with the identification, prioritization, and timely repair of these road defects. However, practices for managing this task vary among DOTs due to differences in climate, traffic volume, and available resources.

Although they share a goal of a rapid response 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 practice 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):

• 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 number of duplicate requests from unique requestors);
• 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 maintenance management systems;
• Case examples of state DOTs with pothole repair strategies;
• Challenges faced by DOTs in the early detection of potholes and identification of pothole-prone areas; and
• Challenges and limitations faced by DOTs in 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. 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
Dr. Zhiye Li
Email: Zli@nas.edu

Meeting Dates
First Panel Meeting: TBD (Virtual via Microsoft Teams)
Teleconference with Consultant: TBD
Second Panel Meeting: TBD (Virtual via Microsoft Teams)