{"id":{"repo_id":"cornell","oai_identifier":"oai:ecommons.cornell.edu:1813/114484"},"canonical_url":"https://search.dev.ndltd.org/etd/cornell/oai:ecommons.cornell.edu:1813/114484","repository":{"repo_id":"cornell","name":"Cornell University","base_url":"https://ecommons.cornell.edu/server/oai/request"},"display":{"title":"FORECASTING WINTER ROAD CONDITIONS: A DATA-DRIVEN APPROACH","abstract":"Annually, 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.","abstract_html":"Annually, 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.","abstract_has_math":false,"creators":["Wilson, Jeswin"],"institution":"Cornell University","degree_name":"M.S., Mechanical Engineering","degree_level":"Master of Science","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":["Ault, Toby","Orr, David"],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-24T01:49:04Z","subjects":["computer vision","machine learning","road surface temperatures","road weather information system","winter road conditions"],"languages":["en"],"rights":["Attribution 4.0 International"],"rights_urls":["https://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/mw3f-sb57"],"render_values":[{"text":"https://doi.org/10.7298/mw3f-sb57","href":"https://doi.org/10.7298/mw3f-sb57","code":true}]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11922","ProQuest Publication ID: 30630973"],"render_values":[{"text":"ProQuest Submission ID: 11922","href":null,"code":true},{"text":"ProQuest Publication ID: 30630973","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/1813/114484","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Ault, Toby","Orr, David"]},{"key":"dc:creator","label":"Author","values":["Wilson, Jeswin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2024-04-05T18:36:31Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2024-04-05T18:36:31Z"]},{"key":"dc:date.issued","label":"Date","values":["2023-08"]},{"key":"dc:type","label":"Dc Type","values":["dissertation or thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Master of Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S., Mechanical Engineering"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Cornell University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer vision","machine learning","road surface temperatures","road weather information system","winter road conditions"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["https://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.7298/mw3f-sb57"]},{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["ProQuest Submission ID: 11922","ProQuest Publication ID: 30630973"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1813/114484"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["105 pages"]},{"key":"dc:description.abstract","label":"Abstract","values":["Annually, 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."]},{"key":"dc:format.mimetype","label":"Dc Format Mimetype","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["FORECASTING WINTER ROAD CONDITIONS: A DATA-DRIVEN APPROACH"]}]}],"canonical_facts":{"dc:contributor.committeemember":["Ault, Toby","Orr, David"],"dc:creator":["Wilson, Jeswin"],"dc:date.accessioned":["2024-04-05T18:36:31Z"],"dc:date.available":["2024-04-05T18:36:31Z"],"dc:date.issued":["2023-08"],"dc:description":["105 pages"],"dc:description.abstract":["Annually, 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."],"dc:format.mimetype":["application/pdf"],"dc:identifier.doi":["https://doi.org/10.7298/mw3f-sb57"],"dc:identifier.other":["ProQuest Submission ID: 11922","ProQuest Publication ID: 30630973"],"dc:identifier.uri":["https://hdl.handle.net/1813/114484"],"dc:language.iso":["en"],"dc:rights":["Attribution 4.0 International"],"dc:rights.uri":["https://creativecommons.org/licenses/by/4.0/"],"dc:subject":["computer vision","machine learning","road surface temperatures","road weather information system","winter road conditions"],"dc:title":["FORECASTING WINTER ROAD CONDITIONS: A DATA-DRIVEN APPROACH"],"dc:type":["dissertation or thesis"],"thesis:degree_discipline":["Mechanical Engineering"],"thesis:degree_level":["Master of Science"],"thesis:degree_name":["M.S., Mechanical Engineering"],"thesis:institution_name":["Cornell University"]},"updated_at":"2026-07-24T01:49:04Z"}