Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks

Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks | 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 Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks Yuanyuan Xiao, Zongtao Duan, Peiying Lei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4250529/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 Accurately predicting the severity of traffic accidents is crucial for preventing them and safeguarding traffic safety. Practitioners need to understand the underlying predictive mechanisms to identify associated risk factors and develop appropriate interventions effectively. Unfortunately, existing research often falls short in predicting diverse outcomes, with some studies neglecting the latter entirely. Moreover, designing explainable deep neural networks (DNNs) is challenging, unlike traditional models, which makes it difficult to achieve explainability with DNNs that incorporate neural networks. We propose a multi-task deep neural network framework designed to predict different types of injury severity, including injury, fatality, and property damage. Our proposed approach offers a thorough and precise method for analyzing crash injury severity. Unlike black-box models, our framework can pinpoint the critical factors contributing to injury severity by employing improved layer-wise relevance propagation. Experiments on Chinese traffic accidents demonstrate that our model accurately predicts the factors associated with injury severity and surpasses existing methods. Moreover, our experiments reveal that the critical factors identified by our approach are more logical and informative compared to those provided by baseline models. Additionally, our findings can assist policymakers make more enlightened decisions when devising and implementing improvements in traffic safety. Crash injury severity prediction Relevance propagation Multi-task learning Explainability Deep neural network Full Text Additional Declarations No competing interests reported. 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. 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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-4250529","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":290332093,"identity":"44af14ec-1127-473e-80db-0658d1f9fb32","order_by":0,"name":"Yuanyuan Xiao","email":"data:image/png;base64,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","orcid":"","institution":"Chang’an University","correspondingAuthor":true,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Xiao","suffix":""},{"id":290332094,"identity":"35efae85-8378-4eba-a551-adf66c4b2232","order_by":1,"name":"Zongtao Duan","email":"","orcid":"","institution":"Chang’an University","correspondingAuthor":false,"prefix":"","firstName":"Zongtao","middleName":"","lastName":"Duan","suffix":""},{"id":290332095,"identity":"0edc27aa-ac51-4521-95d7-cbd3ccec8d44","order_by":2,"name":"Peiying Lei","email":"","orcid":"","institution":"Xi’an University of Architecture and Technology Architecture College","correspondingAuthor":false,"prefix":"","firstName":"Peiying","middleName":"","lastName":"Lei","suffix":""}],"badges":[],"createdAt":"2024-04-11 06:21:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4250529/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4250529/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54902009,"identity":"f41ecad3-ce85-4948-b3b5-5f0156327068","added_by":"auto","created_at":"2024-04-18 10:35:28","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":612560,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4250529/v1_covered_bac33514-4805-43eb-a2a4-91101efd5983.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Explaining Multiple Types of Crash Injury Severity Predictions with Layer-wise Relevance Propagation in Multi-task Deep Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"Crash injury severity prediction, Relevance propagation, Multi-task learning, Explainability, Deep neural network","lastPublishedDoi":"10.21203/rs.3.rs-4250529/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4250529/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccurately predicting the severity of traffic accidents is crucial for preventing them and safeguarding traffic safety. 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