A domain-informed vision-language model for sustainable freight: zero-shot classification of drayage truck powertrain and cargo types

A domain-informed vision-language model for sustainable freight: zero-shot classification of drayage truck powertrain and cargo types | 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 Article A domain-informed vision-language model for sustainable freight: zero-shot classification of drayage truck powertrain and cargo types Guoliang Feng, Yiqiao Li, Andre Y. C. Tok, Stephen G. Ritchie This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7285770/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Heavy-duty trucks contribute disproportionately to greenhouse gas (GHG) and air pollutant emissions, despite accounting for only a small share of vehicle fleets. Drayage trucks, a category of heavy-duty vehicles that transport containers between ports, railyards, and warehouses, are a major source of emissions in freight-intensive corridors, particularly around ports. Due to their short-haul and predictable operation cycles, drayage trucks are considered strong candidates for zero-emission technologies. As California and other states establish ambitious targets for transitioning drayage fleets into zero-emission vehicles, it is essential to assess fleet composition and the pace of technology adoption. Accurate identification of truck powertrain types, such as diesel, electric, hydrogen, and compressed natural gas (CNG), and cargo configurations is critical for tracking policy implementation, planning charging and fueling infrastructure, and reducing emissions from inefficient operations such as empty hauls. However, conventional classification methods rely on large manually labeled datasets, and existing studies have not focused on identifying truck powertrain types. This study introduces ZeroDray, a novel zero-shot classification framework that enables a domain-informed vision-language model to identify drayage truck attributes without requiring labeled training data. ZeroDray employs expert-informed prompts that integrate domain knowledge, visual evidence, spatial reasoning, and chain-of-thought processing to generate interpretable and accurate predictions. The framework was evaluated on 443 distinct drayage truck images collected along a highway corridor serving the Ports of Los Angeles and Long Beach and achieved F_1 scores above 92 percent across all 11 powertrain-cargo configurations. ZeroDray offers an interpretable framework to track zero-emission vehicle adoption, which supports data-driven decision-making in sustainable freight planning and environmental regulation. Physical sciences/Engineering Physical sciences/Mathematics and computing vision-language model zero-shot learning drayage trucks domain-informed prompting sustainable freight Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 13 Sep, 2025 Reviews received at journal 13 Sep, 2025 Reviews received at journal 02 Sep, 2025 Reviews received at journal 14 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers agreed at journal 07 Aug, 2025 Reviewers invited by journal 07 Aug, 2025 Submission checks completed at journal 07 Aug, 2025 First submitted to journal 07 Aug, 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. 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-7285770","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":501473285,"identity":"60ec6a71-a787-403f-9a79-0c2d7ee6a74f","order_by":0,"name":"Guoliang Feng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwklEQVRIiWNgGAWjYHACxgMJDAz8/EDWAQYGZuL0gLRIzmwgSQsQS244AGYToUU+IvnAgQc1dySMbyQ/PMBQYZ3YQEiL4Y20hAMJx55JmN1IMzjAcCadCC0zcgwOJLAdrjO7kcNwgLHtMLFa/h2WMJ4B0vKPCC3yEkAtiW2HJQwkQFoaiNBiwPMs4UBi32EJiTPPgNYdSzcmbEt78sGHP74dluBvT3784UONtSxhWw4g8xIIKQfbQtDQUTAKRsEoGAUAZmpHbDi+mXgAAAAASUVORK5CYII=","orcid":"","institution":"Institute of Transportation Studies, University of California, Irvine","correspondingAuthor":true,"prefix":"","firstName":"Guoliang","middleName":"","lastName":"Feng","suffix":""},{"id":501473286,"identity":"9d120d11-322c-417f-91bd-c40d097ce970","order_by":1,"name":"Yiqiao Li","email":"","orcid":"","institution":"The City College of New York","correspondingAuthor":false,"prefix":"","firstName":"Yiqiao","middleName":"","lastName":"Li","suffix":""},{"id":501473287,"identity":"4addffc7-cb81-4b52-88bf-ed7f4705e7dc","order_by":2,"name":"Andre Y. C. Tok","email":"","orcid":"","institution":"Institute of Transportation Studies, University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Andre","middleName":"Y. C.","lastName":"Tok","suffix":""},{"id":501473288,"identity":"83401eaa-0b65-49c3-bac7-34e09044b126","order_by":3,"name":"Stephen G. Ritchie","email":"","orcid":"","institution":"Institute of Transportation Studies, University of California, Irvine","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"G.","lastName":"Ritchie","suffix":""}],"badges":[],"createdAt":"2025-08-04 00:38:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7285770/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7285770/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89448371,"identity":"09f672df-909b-44d2-997d-ad900196db8c","added_by":"auto","created_at":"2025-08-20 05:42:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1459668,"visible":true,"origin":"","legend":"","description":"","filename":"RevisedManuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7285770/v1_covered_a746f8c6-74b3-4d17-80fa-131a2e2869b0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A domain-informed vision-language model for sustainable freight: zero-shot classification of drayage truck powertrain and cargo types","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"vision-language model, zero-shot learning, drayage trucks, domain-informed prompting, sustainable freight ","lastPublishedDoi":"10.21203/rs.3.rs-7285770/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7285770/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Heavy-duty trucks contribute disproportionately to greenhouse gas (GHG) and air pollutant emissions, despite accounting for only a small share of vehicle fleets. Drayage trucks, a category of heavy-duty vehicles that transport containers between ports, railyards, and warehouses, are a major source of emissions in freight-intensive corridors, particularly around ports. Due to their short-haul and predictable operation cycles, drayage trucks are considered strong candidates for zero-emission technologies. As California and other states establish ambitious targets for transitioning drayage fleets into zero-emission vehicles, it is essential to assess fleet composition and the pace of technology adoption. Accurate identification of truck powertrain types, such as diesel, electric, hydrogen, and compressed natural gas (CNG), and cargo configurations is critical for tracking policy implementation, planning charging and fueling infrastructure, and reducing emissions from inefficient operations such as empty hauls. However, conventional classification methods rely on large manually labeled datasets, and existing studies have not focused on identifying truck powertrain types. This study introduces ZeroDray, a novel zero-shot classification framework that enables a domain-informed vision-language model to identify drayage truck attributes without requiring labeled training data. ZeroDray employs expert-informed prompts that integrate domain knowledge, visual evidence, spatial reasoning, and chain-of-thought processing to generate interpretable and accurate predictions. The framework was evaluated on 443 distinct drayage truck images collected along a highway corridor serving the Ports of Los Angeles and Long Beach and achieved F_1 scores above 92 percent across all 11 powertrain-cargo configurations. ZeroDray offers an interpretable framework to track zero-emission vehicle adoption, which supports data-driven decision-making in sustainable freight planning and environmental regulation.","manuscriptTitle":"A domain-informed vision-language model for sustainable freight: zero-shot classification of drayage truck powertrain and cargo types","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-20 05:34:03","doi":"10.21203/rs.3.rs-7285770/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-13T16:03:06+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-13T05:04:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-02T17:23:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-14T21:28:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"194261167101605449407292657075572154934","date":"2025-08-12T12:13:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"301072950462540899276271804256386578791","date":"2025-08-12T05:09:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332256343545137642516703353547213808293","date":"2025-08-07T12:24:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-07T12:02:11+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T05:41:54+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Sustainable Mobility and Transport","date":"2025-08-07T05:38:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-sustainable-mobility-and-transport","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [npj Sustainable Mobility and Transport](https://www.nature.com/npjsustainmobil)","snPcode":"44333","submissionUrl":"https://submission.springernature.com/new-submission/44333/3","title":"npj Sustainable Mobility and Transport","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c3e3d6ad-dc5b-4aee-ac44-214cb0fffda0","owner":[],"postedDate":"August 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":53262014,"name":"Physical sciences/Engineering"},{"id":53262015,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2026-01-29T08:54:33+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-20 05:34:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7285770","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7285770","identity":"rs-7285770","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}