Microscopic Conflict Prediction in Work Zones: A Simulation-Driven Approach Using Extreme Value Theory

Microscopic Conflict Prediction in Work Zones: A Simulation-Driven Approach Using Extreme Value Theory | 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 Microscopic Conflict Prediction in Work Zones: A Simulation-Driven Approach Using Extreme Value Theory Alican Sevim, Eren Erman Ozguven, Qianwen Guo, Mizanur Rahman, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9314977/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract This study develops a microscopic conflict prediction framework for highway work zones by integrating Post-Encroachment Time (PET)-based surrogate safety measures, Extreme Value Theory (EVT), and predictive modeling. Unlike conventional approaches that focus on isolated work zone segments, this study introduces a dual-snapshot method that models conflict dynamics by systematically analyzing both leader and follower vehicle states at critical conflict detection points while incorporating upstream and downstream traffic influences. Accordingly, a calibrated microscopic simulation replicates real-world work zone geometry and traffic flow. The framework integrates high-resolution vehicle detection using induction loop detectors with EVT-based risk quantification. By calibrating the simulation environment, the study enables systematic data collection of vehicle interactions at a fine temporal resolution. One key contribution of this study is the use of a 6-second PET threshold to flag interactions where one vehicle closely follows another during a merge or lane change. These short-time-gap cases lie in the lower tail of the PET distribution and enable EVT-based estimation of severe conflict likelihood. For conflict prediction, the framework employs both logistic regression for interpretability and Extreme Gradient Boosting (XGBoost) to capture complex, nonlinear relationships among traffic variables. Importantly, a feature importance analysis validates that the EVT-derived risk probability emerges as the most influential predictor within the XGBoost model. The proposed framework was evaluated using both parametric and non-parametric classification models, each achieving over 85% accuracy and sensitivity in distinguishing between conflict and non-conflict cases. Work Zone Safety Conflict Prediction Extreme Value Theory SUMO XGBoost Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 06 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers invited by journal 20 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 09 Apr, 2026 First submitted to journal 03 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9314977","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629638509,"identity":"8d2a8ff4-0f50-4f3b-82df-2ab07311dd1d","order_by":0,"name":"Alican Sevim","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIie3PsUoDMRjA8XwctEtK1ixeXyFHB5GW9lV6COmSwamrdxScFNcbfYS6ZM4RcJLLGnCJDyAUXOxQMFHolnpugvkP37fkRxKEUqm/2hLNvvYujGEV5uBHwsOCJkys+hCE9C8IuddP9FWY/Jw8QLUXGmHyzNBuraOEWs5pKV8mF42D+k56QgWDposTZPGEeVJurRq6kScLilk2uomTsTHvnnTXnkB9kN8Pyw4nCFMCXCnVkpkKNuEWjATL4AQpLC88uSy2FurNmVxhTPlVe9utoiQ32rV7OR8zo9v6TU5zTPSj+1hP498/RssqLByG6nHeR3qeS6VSqf/XJwyZW3Hl2m7VAAAAAElFTkSuQmCC","orcid":"","institution":"Florida State University","correspondingAuthor":true,"prefix":"","firstName":"Alican","middleName":"","lastName":"Sevim","suffix":""},{"id":629638512,"identity":"1c27189b-5110-4402-918a-c989fab96e8d","order_by":1,"name":"Eren Erman Ozguven","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Eren","middleName":"Erman","lastName":"Ozguven","suffix":""},{"id":629638515,"identity":"8241e078-4085-46c1-b944-72b76b8cd770","order_by":2,"name":"Qianwen Guo","email":"","orcid":"","institution":"Florida State University","correspondingAuthor":false,"prefix":"","firstName":"Qianwen","middleName":"","lastName":"Guo","suffix":""},{"id":629638517,"identity":"2ded5c56-8101-41b8-b930-fb45a6d90cb5","order_by":3,"name":"Mizanur Rahman","email":"","orcid":"","institution":"University of Alabama","correspondingAuthor":false,"prefix":"","firstName":"Mizanur","middleName":"","lastName":"Rahman","suffix":""},{"id":629638519,"identity":"36d09128-36f8-4758-9f0c-79dc7507d557","order_by":4,"name":"Sakib Khan","email":"","orcid":"","institution":"Amtrak (United States)","correspondingAuthor":false,"prefix":"","firstName":"Sakib","middleName":"","lastName":"Khan","suffix":""},{"id":629638522,"identity":"9154f37e-5b18-4855-b05b-62f95ebaacd5","order_by":5,"name":"Li Zhao","email":"","orcid":"","institution":"University of Nebraska–Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Zhao","suffix":""},{"id":629638523,"identity":"9c4232a8-b19b-40b0-80f4-194ca8d637ce","order_by":6,"name":"Nathan Huynh","email":"","orcid":"","institution":"University of Nebraska–Lincoln","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Huynh","suffix":""}],"badges":[],"createdAt":"2026-04-03 16:40:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9314977/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9314977/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108182900,"identity":"ef6c3eb9-e15d-4668-a105-07562ad750ac","added_by":"auto","created_at":"2026-04-30 08:59:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6688369,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9314977/v1_covered_7f913116-2348-4634-be23-82a49948ce7e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Microscopic Conflict Prediction in Work Zones: A Simulation-Driven Approach Using Extreme Value Theory","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Work Zone Safety, Conflict Prediction, Extreme Value Theory, SUMO, XGBoost","lastPublishedDoi":"10.21203/rs.3.rs-9314977/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9314977/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study develops a microscopic conflict prediction framework for highway work zones by integrating Post-Encroachment Time (PET)-based surrogate safety measures, Extreme Value Theory (EVT), and predictive modeling. Unlike conventional approaches that focus on isolated work zone segments, this study introduces a dual-snapshot method that models conflict dynamics by systematically analyzing both leader and follower vehicle states at critical conflict detection points while incorporating upstream and downstream traffic influences. Accordingly, a calibrated microscopic simulation replicates real-world work zone geometry and traffic flow. The framework integrates high-resolution vehicle detection using induction loop detectors with EVT-based risk quantification. By calibrating the simulation environment, the study enables systematic data collection of vehicle interactions at a fine temporal resolution. One key contribution of this study is the use of a 6-second PET threshold to flag interactions where one vehicle closely follows another during a merge or lane change. These short-time-gap cases lie in the lower tail of the PET distribution and enable EVT-based estimation of severe conflict likelihood. For conflict prediction, the framework employs both logistic regression for interpretability and Extreme Gradient Boosting (XGBoost) to capture complex, nonlinear relationships among traffic variables. Importantly, a feature importance analysis validates that the EVT-derived risk probability emerges as the most influential predictor within the XGBoost model. The proposed framework was evaluated using both parametric and non-parametric classification models, each achieving over 85% accuracy and sensitivity in distinguishing between conflict and non-conflict cases.\u003c/p\u003e","manuscriptTitle":"Microscopic Conflict Prediction in Work Zones: A Simulation-Driven Approach Using Extreme Value Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-29 09:50:33","doi":"10.21203/rs.3.rs-9314977/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:52:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111472486972808746662510196666112080535","date":"2026-05-06T18:19:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"31127572317561243176529107576268267704","date":"2026-05-06T04:46:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317109267682409985025732257321024451646","date":"2026-04-20T19:34:17+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-20T19:29:00+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-09T16:56:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-09T16:55:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Infrastructure Preservation and Resilience","date":"2026-04-03T16:22:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-infrastructure-preservation-and-resilience","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jipr","sideBox":"Learn more about [Journal of Infrastructure Preservation and Resilience](https://jipr.springeropen.com)","snPcode":"43065","submissionUrl":"https://submission.nature.com/new-submission/43065/3","title":"Journal of Infrastructure Preservation and Resilience","twitterHandle":"@SpringerEng","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e70d5bcf-857b-4d44-8d0a-e0ac7b2d3e6c","owner":[],"postedDate":"April 29th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-06T18:52:13+00:00","index":23,"fulltext":""},{"type":"reviewerAgreed","content":"111472486972808746662510196666112080535","date":"2026-05-06T18:19:15+00:00","index":22,"fulltext":""},{"type":"reviewerAgreed","content":"31127572317561243176529107576268267704","date":"2026-05-06T04:46:40+00:00","index":21,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-29T09:50:34+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-29 09:50:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9314977","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9314977","identity":"rs-9314977","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}