[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121283-en":3,"doc-seo-121283-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121283,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Machine Learning Approaches for Modeling Transportation System Behavior - Dissertation","Machine learning models enable deeper transportation analysis by efficiently extracting insights from large, multidimensional public datasets, including annual pavement condition records and geolocation mobility metrics. This capability supports agencies as they broaden system performance beyond physical condition to include sustainability, resilience, and equity, accounting for both infrastructure assets and interactions among people, communities, and connected systems. The work addresses limited knowledge about how power system failures affect transportation behavior.","Machine Learning Approaches for Modeling Transportation System Behavior  \nby  \nElijah Evers  \nB. S., Colorado School of Mines, 2020  \nM. S., Colorado School of Mines, 2020  \nM.S., University of Colorado Boulder, 2023  \nA thesis submitted to the Faculty of the Graduate School of the University of Colorado Boulder in partial fulfillment of the requirement for the degree of Doctor of Philosophy  \nDepartment of Civil, Environmental, and Architectural Engineering  \n2024  \nCommittee Co-Chairs:  \nCristina Torres-Machi  \nUniversity of Colorado Boulder  \nKyri Baker  \nUniversity of Colorado Boulder  \nCommittee Members:  \nAmelia Celoza  \nUniversity of Colorado Boulder  \nAmir Behzadan  \nUniversity of Colorado Boulder  \nAdeeba Raheem  \nUniversity of Texas, El Paso  \nEvers, Elijah Richard (Ph.D. in Civil Engineering, Department of Civil, Environmental, and Architectural Engineering)  \nMachine Learning Approaches for Modeling Transportation System Behavior  \nDissertation directed by Assistant Professor Cristina Torres-Machi and Associate Professor Kyri Baker  \nAbstract: Recent increases in volume and variety of publicly available datasets related to infrastructure, particularly with annual pavement condition data, and geolocation-based mobility metrics, presents an opportunity for more detailed transportation analyses. Machine learning algorithms offer an efficient and effective means for interpreting this large-sized and multidimensional data to extract information about the transportation system. This opportunity comes at a time where transportation agencies are looking at broadening the definition of system performance (traditionally focused on physical condition) to incorporate sustainability, resilience, and equity in their decision-making processes – a process that requires a more comprehensive understanding of the multiple components in the transportation infrastructure system. This includes the physical assets, how individuals and communities interact with the infrastructure, and how transportation systems interact with other infrastructure systems. These considerations are particularly relevant given the poor state of road infrastructure in the United States, rising EV adoption, increasing strain on power systems, and that the impact of power system failures on transportation systems is mostly unknown.  \nThe dissertation seeks to leverage publicly available data and machine learning techniques to better understand transportation infrastructure by (1) quantifying the impacts of improved deterioration modeling of recycled pavements on the management of pavement assets; (2)  \nestablishing a methodology to determine how power disruptions influence travel behavior compared to weather events; and (3) understanding how electric vehicle adoption and sociodemographic factors relate to travel impacts from power disruptions.  \nFindings from Chapter 2 contribute knowledge of the performance of recycled pavements, as well as demonstrating the effectiveness of the machine-learning-based pavement deterioration modeling. Chapter 3 establishes a time-series-based methodology to isolate baseline travel behavior and implements decision trees to determine how power outage events result in changes from expected travel. Chapter 4 isolates power outage-related events to better understand the connection between power failure and travel in the context of electric vehicle adoption and demographic factors. The results of this dissertation demonstrate how large-scale, publicly available data can be used to promote sustainability, resilience, and equity in asset management and shows the connection between these considerations in power and transportation systems.  \nDEDICATION  \nThis dissertation is dedicated to my parents, Mark and April, my fiancée Katie, and Jen. Without their inspiration, encouragement, and love, this work would not have been possible.  \nACKNOWLEDGEMENTS  \nFirstly, I would like to thank my co-advisors Professor Cristina Torres-Machi and Profe","cbCaikJnTMnF1Tgi","https://ap.wps.com/l/cbCaikJnTMnF1Tgi","pdf",3632956,1,179,"English","en",105,"# Abstract\n# Dedication\n# Acknowledgements","[{\"question\":\"What kinds of public datasets motivate the dissertation’s machine learning approach?\",\"answer\":\"The dissertation focuses on publicly available infrastructure datasets, including annual pavement condition data and geolocation-based mobility metrics.\"},{\"question\":\"How does the dissertation study deterioration of recycled pavements?\",\"answer\":\"It quantifies impacts of improved deterioration modeling of recycled pavements for managing pavement assets and demonstrates effectiveness of machine-learning-based deterioration modeling.\"},{\"question\":\"How do power disruptions influence travel behavior according to the dissertation?\",\"answer\":\"It establishes a time-series methodology to isolate baseline travel behavior and uses decision trees to detect changes in travel outcomes associated with power outage events, considering weather and factors tied to electric vehicle adoption and demographics.\"}]","Machine Learning Approaches for Modeling Transportation System Behavior - 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