Video-Based Surrogate Safety Analysis for Conflict Detection at Intersections Using CCTV Footage and Extreme Value Theory

Video-Based Surrogate Safety Analysis for Conflict Detection at Intersections Using CCTV Footage and 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 Video-Based Surrogate Safety Analysis for Conflict Detection at Intersections Using CCTV Footage and Extreme Value Theory Swaranjit Roy, Ahmed Abdelhadi, Ph.D., Sherif M. Gaweesh, Ph.D., P.E., RSP This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7894809/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Traditional crash-based safety analysis is reactive and limited by underreporting and delayed insights. This study proposes a proactive conflict analysis framework using traffic CCTV footage and advanced video analytics to detect and classify intersection conflicts. A Bird’s Eye View (BEV) transformation and YOLOv8-OBB detection model were employed to extract accurate vehicle trajectories, followed by conflict detection using Post-Encroachment Time (PET) and Time to Collision (TTC). Peak over Threshold (POT) univariate Extreme Value Theory (EVT) and clustering approach was applied separately to PET and TTC to classify conflicts into low, medium, high and very high severity classes. Manual conflict data was collected by identifying event with PET ≤ 4s which was used to validate the accuracy of the automatic conflict detection framework. The automatic conflict detection framework achieved high accuracy of 99% in identifying different conflict types. Conflict severity analysis revealed that PET and TTC values exceeding 1 second were consistently associated with low-severity outcomes, suggesting that even a minimal buffer of 1 second often provides drivers with sufficient time to execute evasive maneuvers and avoid critical interactions. PET proved to be a more reliable conflict indicator for CCTV-based analysis, as it depends only on spatial overlap, while TTC’s reliance on speed and distance makes it more prone to estimation errors. Overall, this framework provides a scalable, plug-and-play solution for proactive safety monitoring using existing camera infrastructure. It addresses key gaps in previous studies by integrating detection, severity modeling, and behavior analysis, supporting data-driven safety improvements aligned with Vision Zero goals. Civil Engineering Artificial Intelligence and Machine Learning Traffic safety analysis intersection safety surrogate safety traffic conflicts video analytics time-to-collision Extreme Value Theory Post-Encroachment Time Full Text Additional Declarations The authors declare potential competing interests as follows: The authors declare no conflict of interest. Cite Share Download PDF Status: Posted Version 1 posted 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-7894809","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":531681826,"identity":"e79b7724-bbb4-4559-83ad-cc6e41cfcba8","order_by":0,"name":"Swaranjit Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYPACNh4IXQHEBxgYJIjSAtFzhngtDAxgLYxtRGiRn5H87MPPHXwy9gy8Dx/zzjucx3eA+eBtHjxaDG6kGc/sPQNyGLuxMe+2w8WSB9iSrfFqkU4wZuBtA2lhY5MGaknccIDHTBqfFvnZ6Z8Z/8K1zAFp4f+GVwvD7RxjZoQtDWBb2PBqMbj/pphZFqTlMBuz4Zxj6YkzD7MZW87B57Ce45sZ37Yds2dvb2N88KbGOrHvePPDG2/wOQwCjjEwMDMwMIHdw0xYOQjUgEnGH8SpHgWjYBSMghEGAJ/7QvGEx9UnAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0003-4003-5451","institution":"University of North Dakota","correspondingAuthor":true,"prefix":"","firstName":"Swaranjit","middleName":"","lastName":"Roy","suffix":""},{"id":531681827,"identity":"e32906b1-a310-4412-a8c3-6744b054bd07","order_by":1,"name":"Ahmed Abdelhadi, Ph.D.","email":"","orcid":"https://orcid.org/0000-0001-6774-2568","institution":"University of North Dakota","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"","lastName":"Abdelhadi","suffix":"Ph.D."},{"id":531681828,"identity":"f45a9c0a-b3c5-4f5c-864f-30f1477b659f","order_by":2,"name":"Sherif M. Gaweesh, Ph.D., P.E., RSP","email":"","orcid":"https://orcid.org/0000-0001-7977-6378","institution":"University of North Dakota","correspondingAuthor":false,"prefix":"","firstName":"P.","middleName":"Ph.D. Sherif M.","lastName":"Gaweesh","suffix":"Ph.D."}],"badges":[],"createdAt":"2025-10-18 16:56:25","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7894809/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7894809/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93987400,"identity":"1ed5998f-0652-449a-b6cd-2aff9ebd28af","added_by":"auto","created_at":"2025-10-21 04:36:07","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":718621,"visible":true,"origin":"","legend":"","description":"","filename":"SMOSPaperFinalisedVersion.docx","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/a52c14e820a3a2c0f24cc60a.docx"},{"id":93987399,"identity":"4eda16cf-de52-4f48-97b6-fc74fb78edab","added_by":"auto","created_at":"2025-10-21 04:36:07","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":342,"visible":true,"origin":"","legend":"","description":"","filename":"rs7894809.json","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/bcfded107d58b729e94fa382.json"},{"id":93987407,"identity":"8d9905dd-d639-48f1-9fa3-96d8fe84b22c","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141339,"visible":true,"origin":"","legend":"","description":"","filename":"rs78948090enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/ec68f9e2690dc838ca0a86d0.xml"},{"id":93987408,"identity":"e3312f07-88b3-46c6-aef5-14bc9638192a","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":56254,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/e906c432045234c5d17effba.png"},{"id":93987398,"identity":"a23658ce-5fb2-4658-b73f-d73f324da7fe","added_by":"auto","created_at":"2025-10-21 04:36:07","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21794,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/561af2910bb462829b23edfd.png"},{"id":93988336,"identity":"64cf598f-983c-4e46-a071-8dff352ea9a5","added_by":"auto","created_at":"2025-10-21 04:52:08","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":107247,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/45b174d75dde0a0cab455dc0.png"},{"id":93987405,"identity":"daa8f92f-6413-451a-a81b-eb3421de975a","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":120359,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/4b2eabd630844fd2b7044e45.png"},{"id":93988335,"identity":"f0a0a8a1-5a88-42a7-b4d9-f54ffb76c1db","added_by":"auto","created_at":"2025-10-21 04:52:07","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97837,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/f98b0785e95b0b5a32c36227.png"},{"id":93988072,"identity":"2b24aa46-a3be-4dde-ab1e-65a258b2902d","added_by":"auto","created_at":"2025-10-21 04:44:07","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":83345,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/924fd13d534690d63c170d9d.png"},{"id":93987406,"identity":"a3f4b6a8-cd4c-426b-ae07-8087e25523ad","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17292,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/4b4f019bce64b87e43b40ba5.png"},{"id":93987410,"identity":"7dcac7ab-c2ed-42c7-9ae8-4c414154e036","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":9493,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/e863dc06b415b2ab1e390d30.png"},{"id":93987401,"identity":"0c2083ba-c500-4196-9ae6-bc0275f9929d","added_by":"auto","created_at":"2025-10-21 04:36:07","extension":"png","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":22800,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/aa6a433105f2d4b2699be419.png"},{"id":93988076,"identity":"1f9fc696-4651-4dd1-b49b-269afba60096","added_by":"auto","created_at":"2025-10-21 04:44:08","extension":"png","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":29596,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/1268cffbddc1826f784146a6.png"},{"id":93988075,"identity":"463949fb-ee22-498a-8231-2b1e509f2aa1","added_by":"auto","created_at":"2025-10-21 04:44:08","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":23701,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/f76f5d18934bf27d60ab2b40.png"},{"id":93987411,"identity":"3d9f260c-6fb4-4198-8f40-2b5b8aa48ffe","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":17779,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/9a68f1cbc09ea330ba0cfe02.png"},{"id":93987414,"identity":"200a5c3a-7037-4319-b0a1-546c0ab33a2f","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"xml","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":140163,"visible":true,"origin":"","legend":"","description":"","filename":"rs78948090structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/16838c6790f280ff7e254495.xml"},{"id":93987413,"identity":"a04d2e67-5f01-4563-bb27-c89fcaa27082","added_by":"auto","created_at":"2025-10-21 04:36:08","extension":"html","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":151698,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1/f32f0ee77ab5110908e99b77.html"},{"id":93988942,"identity":"c5c67497-6bdb-4207-9182-28bdf69c0fc4","added_by":"auto","created_at":"2025-10-21 05:00:09","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":661826,"visible":true,"origin":"","legend":"","description":"","filename":"SMOSPaperFinalisedVersion.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7894809/v1_covered_58bedc62-779d-4f4f-b87e-f189e29e6026.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: The authors declare no conflict of interest.","formattedTitle":"\u003cp\u003eVideo-Based Surrogate Safety Analysis for Conflict Detection at Intersections Using CCTV Footage and Extreme Value Theory\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of North Dakota","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Traffic safety analysis, intersection safety, surrogate safety, traffic conflicts, video analytics, time-to-collision, Extreme Value Theory, Post-Encroachment Time","lastPublishedDoi":"10.21203/rs.3.rs-7894809/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7894809/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTraditional crash-based safety analysis is reactive and limited by underreporting and delayed insights. This study proposes a proactive conflict analysis framework using traffic CCTV footage and advanced video analytics to detect and classify intersection conflicts. A Bird\u0026rsquo;s Eye View (BEV) transformation and YOLOv8-OBB detection model were employed to extract accurate vehicle trajectories, followed by conflict detection using Post-Encroachment Time (PET) and Time to Collision (TTC). Peak over Threshold (POT) univariate Extreme Value Theory (EVT) and clustering approach was applied separately to PET and TTC to classify conflicts into low, medium, high and very high severity classes. Manual conflict data was collected by identifying event with PET\u0026thinsp;\u0026le;\u0026thinsp;4s which was used to validate the accuracy of the automatic conflict detection framework. The automatic conflict detection framework achieved high accuracy of 99% in identifying different conflict types. Conflict severity analysis revealed that PET and TTC values exceeding 1 second were consistently associated with low-severity outcomes, suggesting that even a minimal buffer of 1 second often provides drivers with sufficient time to execute evasive maneuvers and avoid critical interactions. PET proved to be a more reliable conflict indicator for CCTV-based analysis, as it depends only on spatial overlap, while TTC\u0026rsquo;s reliance on speed and distance makes it more prone to estimation errors. Overall, this framework provides a scalable, plug-and-play solution for proactive safety monitoring using existing camera infrastructure. It addresses key gaps in previous studies by integrating detection, severity modeling, and behavior analysis, supporting data-driven safety improvements aligned with Vision Zero goals.\u003c/p\u003e","manuscriptTitle":"Video-Based Surrogate Safety Analysis for Conflict Detection at Intersections Using CCTV Footage and Extreme Value Theory","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-21 04:36:03","doi":"10.21203/rs.3.rs-7894809/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"11fd5e32-e973-46b8-9f93-55bcbcf94b83","owner":[],"postedDate":"October 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56526312,"name":"Civil Engineering"},{"id":56526313,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2025-10-21T04:36:03+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-21 04:36:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7894809","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7894809","identity":"rs-7894809","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}