{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101724"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101724","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Implementing deep learning techniques for network-scale traffic forecasting","abstract":"In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and create prediction models that can outperform a lot of the existing state-of-the-art methods. This is primarily due to two main reasons; the rise of big data, where huge amounts of information has become readily available to the public, as well as the recent technological advancements in computer processing powers which has enabled researchers to take advantage of these large volumes of data. One of the major fields which requires dealing with and understanding extensive amounts of data is transportation. In the United States alone, 220 billion vehicle trips have taken place in 2017 [1]. This creates the need for researchers who can work with such huge data to build models and infer beneficial knowledge which can contribute to improving transportation networks and the overall travel experience. In this thesis, we study the use of several machine learning and deep learning techniques to predict travel times on a road network. The two main methods proposed to tackle the problem are Convolutional Neural Networks and Long-Short Term Memory Networks. The location of interest of this thesis is the city of New York. The New York City Taxi and Limousine Commission provides the origin and destination pairs, along with the travel times and other formation, for each taxi trip between the years of 2010 and 2013. A more refined representation of the data was obtained from B. Donovan and D. Work [2], where travel time estimates for each hour of the day is provided along every road in the city.","abstract_html":"In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and create prediction models that can outperform a lot of the existing state-of-the-art methods. This is primarily due to two main reasons; the rise of big data, where huge amounts of information has become readily available to the public, as well as the recent technological advancements in computer processing powers which has enabled researchers to take advantage of these large volumes of data. One of the major fields which requires dealing with and understanding extensive amounts of data is transportation. In the United States alone, 220 billion vehicle trips have taken place in 2017 [1]. This creates the need for researchers who can work with such huge data to build models and infer beneficial knowledge which can contribute to improving transportation networks and the overall travel experience. In this thesis, we study the use of several machine learning and deep learning techniques to predict travel times on a road network. The two main methods proposed to tackle the problem are Convolutional Neural Networks and Long-Short Term Memory Networks. The location of interest of this thesis is the city of New York. The New York City Taxi and Limousine Commission provides the origin and destination pairs, along with the travel times and other formation, for each taxi trip between the years of 2010 and 2013. A more refined representation of the data was obtained from B. Donovan and D. Work [2], where travel time estimates for each hour of the day is provided along every road in the city.","abstract_has_math":false,"creators":["Ammourah, Rami Ahmad Mohammad"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Civil Engineering","degree_department":null,"school":null,"contributors":["Ouyang, Yanfeng","Sowers, Richard"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-27T16:34:22Z","date_published":"2018-09-27T16:34:22Z","updated_at":"2026-07-22T22:24:40Z","subjects":["Deep Learning","LSTM","CNN","Traffic Forecasting","Traffic Prediction","Long Short Term Memory Networks","Convolutional Neural Networks","Network-Scale Traffic Prediction"],"languages":["en"],"rights":["Copyright 2018 Rami Ammourah"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101724","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ouyang, Yanfeng","Sowers, Richard"]},{"key":"dc:creator","label":"Author","values":["Ammourah, Rami Ahmad Mohammad"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-27T16:34:22Z","2020-09-28T09:15:24Z","2018-07-19","2018-08"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Civil Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Deep Learning","LSTM","CNN","Traffic Forecasting","Traffic Prediction","Long Short Term Memory Networks","Convolutional Neural Networks","Network-Scale Traffic Prediction"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Rami Ammourah"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101724"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["In the past few years, Deep learning has re-emerged as a powerful tool to solve complex problems and create prediction models that can outperform a lot of the existing state-of-the-art methods. This is primarily due to two main reasons; the rise of big data, where huge amounts of information has become readily available to the public, as well as the recent technological advancements in computer processing powers which has enabled researchers to take advantage of these large volumes of data. One of the major fields which requires dealing with and understanding extensive amounts of data is transportation. In the United States alone, 220 billion vehicle trips have taken place in 2017 [1]. This creates the need for researchers who can work with such huge data to build models and infer beneficial knowledge which can contribute to improving transportation networks and the overall travel experience. In this thesis, we study the use of several machine learning and deep learning techniques to predict travel times on a road network. The two main methods proposed to tackle the problem are Convolutional Neural Networks and Long-Short Term Memory Networks. The location of interest of this thesis is the city of New York. The New York City Taxi and Limousine Commission provides the origin and destination pairs, along with the travel times and other formation, for each taxi trip between the years of 2010 and 2013. A more refined representation of the data was obtained from B. Donovan and D. Work [2], where travel time estimates for each hour of the day is provided along every road in the city.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Rami Ammourah, accepted the attached license on 2018-07-18 at 10:54.","The student, Rami Ammourah, submitted this Thesis for approval on 2018-07-18 at 11:06.","This Thesis was approved for publication on 2018-07-19 at 09:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12920 on 2018-09-27 at 11:19:25","Made available in DSpace on 2018-09-27T16:34:22Z (GMT). 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The location of interest of this thesis is the city of New York. The New York City Taxi and Limousine Commission provides the origin and destination pairs, along with the travel times and other formation, for each taxi trip between the years of 2010 and 2013. A more refined representation of the data was obtained from B. Donovan and D. Work [2], where travel time estimates for each hour of the day is provided along every road in the city.","Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2020-08-01","The student, Rami Ammourah, accepted the attached license on 2018-07-18 at 10:54.","The student, Rami Ammourah, submitted this Thesis for approval on 2018-07-18 at 11:06.","This Thesis was approved for publication on 2018-07-19 at 09:01.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12920 on 2018-09-27 at 11:19:25","Made available in DSpace on 2018-09-27T16:34:22Z (GMT). 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