Abed, Ahmed, Rahman, Mujib, Thom, Nick, Hargreaves, David, Li, Linglin and Airey, Gordon (2025). Pothole prediction based on machine learning and pavement condition indicators. Proceedings of the ICE - Transport ,
Abstract
Potholes are dangerous defects on road surfaces, contributing to numerous crashes involving vehicles, motorcycles and bicycles. They also impose a significant economic burden on highway authorities. Currently, no effective tool predicts th enumber and location of potholes in a road network. In this work, an attempt to address this gap was conducted by developing novel pothole prediction tools built using two machine learning methods – random forest and K-nearest neighbour. The final prediction model requires nine pavement condition indicators, quantifiable through Surface Condition Assessment for the National Network of Roads surveys, commonly conducted in the UK. This unique approach allows for direct implementation by highway authorities. The model was trained on a large dataset of pavement condition and pothole data covering the Transport for London network. Validation results suggest that the model successfully predicted 55.5%of sections with potholes and 99.6% of sections without potholes. Although the model demonstrates limited accuracy in predicting potholes, recommendations are provided to enhance its performance. This work holds the potential to significantly aid strategic financial planning for pavement management in the UK and elsewhere.
Publication DOI: | https://doi.org/10.1680/jtran.24.00148 |
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Divisions: | College of Engineering & Physical Sciences > School of Infrastructure and Sustainable Engineering > Civil Engineering College of Engineering & Physical Sciences Aston University (General) |
Additional Information: | Copyright © ICE Publishing. This AAM is deposited under the CC BY-NC 4.0 licence. Any reuse is allowed in accordance with the terms outlined by the licence. To reuse the AAM for commercial purposes, permission should be sought by contacting permissions@emeraldinsight.com. |
Uncontrolled Keywords: | machine learning,modelling,pavement condition,pavements & roads,potholes/,urface Condition Assessment for the National Network of Roads (Scanner),Civil and Structural Engineering |
Publication ISSN: | 0965-092X |
Last Modified: | 03 Jul 2025 14:59 |
Date Deposited: | 03 Jul 2025 14:55 |
Full Text Link: | |
Related URLs: |
https://www.ice ... /jtran.24.00148
(Publisher URL) |
PURE Output Type: | Article |
Published Date: | 2025-06-28 |
Published Online Date: | 2025-06-28 |
Accepted Date: | 2025-04-07 |
Authors: |
Abed, Ahmed
Rahman, Mujib ( ![]() Thom, Nick Hargreaves, David Li, Linglin Airey, Gordon |