[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126236-en":3,"doc-seo-126236-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126236,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Pothole Prediction Based on Machine Learning and Pavement Condition Indicators - Road Network Implementation Recommendations","Potholes are hazardous road defects that contribute to vehicle, motorcycle, and bicycle crashes while creating a substantial financial load for highway authorities. Current approaches lack effective tools to predict both the number and spatial location of potholes across road networks. This study develops pothole prediction tools using Random Forest and K Nearest Neighbour, relying on nine SCANNER-derived pavement condition indicators and training on a Transport for London dataset, achieving high section-level detection rates and actionable guidance to improve accuracy.","1 Pothole Prediction Based on Machine Learning and Pavement  \n2 Condition Indicators  \n3  \n4 Ahmed Abed, PhD, FIHE, FHEA, CEng  \n5 Department of Civil Engineering  \n6 Aston University, Birmingham, B4 7ET, UK  \n7 Corresponding author: [abeda@aston.ac.uk](abeda@aston.ac.uk)  \n[8](8 Correspondence address:)[ Correspondence address:](8 Correspondence address:)  \n9 MB222D  \n10 Main Building  \n11 Aston University  \n12 Birmingham  \n13 UK  \n14 B4 7ET  \n15 ORCID ID: 0000-0002-6822-5519 16  \n17 Mujib Rahman,  \n18 Department of Civil Engineering  \n19 Aston University, Birmingham, B4 7ET, UK.  \n20 [rahmam19@aston.ac.uk](rahmam19@aston.ac.uk)  \n21  \n22 Nick Thom, MA, PhD, MICE, MCIHT, MPWI, CEng  \n23 Nottingham Transportation Engineering Centre  \n24 University of Nottingham, Nottingham, NG7 2RD, UK  \n25 [nicholas.thom@nottingham.ac.uk](nicholas.thom@nottingham.ac.uk)  \n26  \n27 David Hargreaves,  \n28 Faculty of Engineering  \n29 University of Nottingham, Nottingham, NG7 2RD, UK  \n30 [david.hargreaves@nottingham.ac.uk](david.hargreaves@nottingham.ac.uk)  \n31  \n32 Linglin Li, PhD, FIHE,  \n33 Nottingham Transportation Engineering Centre  \n34 University of Nottingham, Nottingham, NG7 2RD, UK  \n35 [linglin.li@nottingham.ac.uk](linglin.li@nottingham.ac.uk)  \n36  \n37  \n38 Gordon Airey, PhD, MCIHT  \n39 Nottingham Transportation Engineering Centre  \n40 University of Nottingham, Nottingham, NG7 2RD, UK  \n41 [gordon.airey@nottingham.ac.uk](gordon.airey@nottingham.ac.uk)  \n42  \n43  \n44 Number of words: 5414 45  \n46  \n47  \n1 Abstract 2  \n3 Potholes are dangerous defects on road surfaces, contributing to numerous  \n4 crashes involving vehicles, motorcycles, and bicycles. They also impose a significant  \n5 economic burden on highway authorities. Currently, no effective tool predicts the  \n6 number and location of potholes in a road network. In this study, an attempt to  \n7 address this gap has been proposed by developing novel pothole prediction tools  \n8 built using two machine learning methods, Random Forest, and K Nearest  \n9 Neighbour. The final prediction model requires nine pavement condition indicators, 10 quantifiable through Surface Condition Assessment for the National Network of  \n11 Roads (SCANNER) surveys, commonly conducted in the UK. This unique approach  \n12 allows for direct implementation by highway authorities. The model has been trained  \n13 on a large dataset of pavement condition and pothole data covering the ‘Transport  \n14 for London’ network. Validation results suggest that the model successfully predicted 15 55.5% of sections with potholes and 99.6% of sections without potholes. Although  \n16 the model demonstrates limited accuracy in predicting potholes, recommendations  \n17 are provided to enhance its performance. This work holds the potential to  \n18 significantly aid strategic financial planning for pavement management in the UK  \n19 and beyond.  \n20  \n21 Keywords: Pavements and Roads, Modelling, Potholes; Pavement Condition;  \n22 SCANNER, Machine Learning, UN SDG 9.  \n23  \n24  \n25  \n26  \n27  \n28  \n29  \n30  \n31  \n32  \n33  \n34  \n35  \n36  \n37  \n38  \n39  \n40  \n41  \n42  \n43  \n44  \n45  \n46  \n47  \n48  \n49  \n1 Introduction  \n2 Potholes are localised defects that form in the surface of roads usually having a  \n3 bowl shape with varying widths, lengths and depths (Miller and Bellinger, 2003) . In  \n4 recent years, this distress has attracted considerable attention by the public in the  \n5 UK due to the ever-rising number of potholes forming in UK roads, and the critical  \n6 negative economic, social, and environmental impacts this distress causes. The latest  \n7 Annual Local Authority Road Maintenance (ALARM) survey report shows that  \n8 1,985,480 potholes were filled in 2023 by the local authorities in England and Wales  \n9 at an average cost of £72.27 per pothole (AiA, 2024) . This represents a 40% increase  \n10 in the number of filled potholes and a 14.4% increase in the average pothole filling  \n11 cost compared to 2022 . Bearing i","cbCaivX9sVphbEM4","https://ap.wps.com/l/cbCaivX9sVphbEM4","pdf",764590,4,1,20,"English","en",105,"# Abstract\n# Introduction\n## Background and impacts of potholes\n## Existing survey and detection approaches\n# Proposed machine learning prediction approach\n## Required pavement condition indicators\n## Dataset and validation overview\n# Results and performance assessment\n## Section-level prediction outcomes\n# Recommendations and applications\n## Road authorities and pavement management planning","[{\"question\":\"What problem does the study address in pothole management?\",\"answer\":\"The study addresses the lack of effective tools that can predict the number and location of potholes across a road network. It aims to support more reliable planning for pavement management.\"},{\"question\":\"Which machine learning methods are used for pothole prediction?\",\"answer\":\"The approach uses two machine learning methods: Random Forest and K Nearest Neighbour. Both are combined with nine pavement condition indicators derived from SCANNER surveys.\"},{\"question\":\"How were prediction performance results evaluated and what do they indicate?\",\"answer\":\"Validation indicates the model predicted 55.5% of sections with potholes and 99.6% of sections without potholes. The work also notes limited accuracy for pothole prediction and provides recommendations to improve performance.\"}]","Pothole Prediction Based on Machine Learning and Pavement Condition Indicators - Road Network Implementation Recommendations | PDF",1785903976,50,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"pothole-prediction-based-on-machine-learning-and-pavement-condition-indicators-road-network-implementation-recommendations","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":20},"https://docshare.wps.com/document/pothole-prediction-based-on-machine-learning-and-pavement-condition-indicators-road-network-implementation-recommendations/126236/",{"url":53,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in pothole management?","Question",{"text":76,"@type":77},"The study addresses the lack of effective tools that can predict the number and location of potholes across a road network. It aims to support more reliable planning for pavement management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods are used for pothole prediction?",{"text":81,"@type":77},"The approach uses two machine learning methods: Random Forest and K Nearest Neighbour. Both are combined with nine pavement condition indicators derived from SCANNER surveys.",{"name":83,"@type":74,"acceptedAnswer":84},"How were prediction performance results evaluated and what do they indicate?",{"text":85,"@type":77},"Validation indicates the model predicted 55.5% of sections with potholes and 99.6% of sections without potholes. The work also notes limited accuracy for pothole prediction and provides recommendations to improve performance.","https://schema.org",{"og:url":53,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":30,"slug":114},6,"Technology","technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":22,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":128,"show_sort_weight":22,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":47,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":47,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]