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FORECASTING WINTER ROAD CONDITIONS: A DATA-DRIVEN APPROACH

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dc.contributor.authorWilson, Jeswin
dc.contributor.chairZhang, Keen_US
dc.contributor.committeeMemberAult, Tobyen_US
dc.contributor.committeeMemberOrr, Daviden_US
dc.date.accessioned2024-04-05T18:36:31Z
dc.date.available2024-04-05T18:36:31Z
dc.date.issued2023-08
dc.description105 pagesen_US
dc.description.abstractAnnually, 24% of weather-related vehicle crashes happen on snowy, slushy, or icy roads in the United States. Accurate prediction of road surface temperatures and conditions is crucial for ensuring safe and efficient transportation, especially during winter. In this study, we developed machine learning and computer vision models for predicting road surface temperatures and conditions using historical meteorological and road surface sensor data and images. We implemented machine learning algorithms to build models that can predict road surface temperatures and conditions with high accuracy. We also developed computer vision models to detect real-time road surface conditions, including dry, wet, ice, snow, and slush, based on real-time road surface image data. Integration of temperature prediction models, surface condition prediction models, and computer vision models into existing road weather information system (RWIS) networks has the potential to provide accurate predictive information on road surface temperatures and conditions, enhancing the safety and efficiency of transportation systems, especially in rural communities where there are limited RWIS resources.en_US
dc.identifier.doihttps://doi.org/10.7298/mw3f-sb57
dc.identifier.otherWilson_cornell_0058O_11922
dc.identifier.otherhttp://dissertations.umi.com/cornell:11922
dc.identifier.urihttps://hdl.handle.net/1813/114484
dc.language.isoen
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectcomputer visionen_US
dc.subjectmachine learningen_US
dc.subjectroad surface temperaturesen_US
dc.subjectroad weather information systemen_US
dc.subjectwinter road conditionsen_US
dc.titleFORECASTING WINTER ROAD CONDITIONS: A DATA-DRIVEN APPROACHen_US
dc.typedissertation or thesisen_US
dcterms.licensehttps://hdl.handle.net/1813/59810.2
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorCornell University
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Mechanical Engineering

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