Surrogate-modeling for Pavement Maintenance Scheduling

Surrogate-modeling for Pavement Maintenance Scheduling | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Surrogate-modeling for Pavement Maintenance Scheduling Abhishek Kumar Prajapati, Ilgin Guler This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558676/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Data Science for Transportation → Version 1 posted 9 You are reading this latest preprint version Abstract Incorporating traffic dynamics into pavement maintenance scheduling improves decision quality by accounting for user delays, but it introduces significant computational challenges. Existing approaches rely on detailed macroscopic traffic flow models to evaluate the impact of maintenance activities on user. These models are typically embedded within population-based optimization algorithms to explore complex decision spaces. However, repeated evaluations during optimization result in high computational costs, making large-scale applications impractical. This paper investigates the use of surrogate modeling techniques—including statistical and machine learning models—to approximate traffic flow responses in place of direct simulation. These surrogate models are trained on data that reflects critical traffic dynamics to capture key traffic relationships and are integrated into the maintenance planning framework to support optimization. In addition to evaluating accuracy, computational efficiency, and the quality of resulting maintenance policies, the study also investigates the generalizability of the surrogate models across different network configurations. Results demonstrate that the proposed method significantly reduces computational burden while maintaining reliable and transferable performance in traffic-aware pavement maintenance planning. The paper concludes with practical guidelines for researchers and practitioners seeking to represent realistic user costs without relying on computationally intensive traffic evaluations. Machine learning Pavement Maintenance Planning Surrogate Modeling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 24 Oct, 2025 Read the published version in Data Science for Transportation → Version 1 posted Editorial decision: Accepted 08 Oct, 2025 Reviews received at journal 07 Oct, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 17 Sep, 2025 Reviewers agreed at journal 16 Sep, 2025 Reviewers invited by journal 16 Sep, 2025 Editor assigned by journal 16 Sep, 2025 Submission checks completed at journal 16 Sep, 2025 First submitted to journal 07 Sep, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7558676","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":516711522,"identity":"50f2f3e8-8788-47c7-84ff-7badb0d79928","order_by":0,"name":"Abhishek Kumar 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Existing approaches rely on detailed macroscopic traffic flow models to evaluate the impact of maintenance activities on user. These models are typically embedded within population-based optimization algorithms to explore complex decision spaces. However, repeated evaluations during optimization result in high computational costs, making large-scale applications impractical. This paper investigates the use of surrogate modeling techniques—including statistical and machine learning models—to approximate traffic flow responses in place of direct simulation. These surrogate models are trained on data that reflects critical traffic dynamics to capture key traffic relationships and are integrated into the maintenance planning framework to support optimization. In addition to evaluating accuracy, computational efficiency, and the quality of resulting maintenance policies, the study also investigates the generalizability of the surrogate models across different network configurations. 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