[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125487-en":3,"doc-seo-125487-105":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},125487,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Behaviorally Informed Machine Learning for Human Mobility - dissertation proposal","Behaviorally Informed Machine Learning for Human Mobility proposes methods to overcome limits of passively collected mobile (PCM) data in metropolitan transportation planning. Large-scale trace datasets enable high-resolution observation of mobility, but suffer from missing segments, trajectory discontinuities, privacy restrictions, and representativeness biases across populations. The dissertation offers four contributions: multi-task Gaussian-process imputation, physics-regularized trace generation, sociodemographic inference with enrichment strategies, and qualitative insights on planners’ barriers and recommendations for adoption.","Behaviorally Informed Machine Learning for Human Mobility  \nEkin U˘gurel  \nA dissertation proposal  \nsubmitted in partial fulfillment of the  \nrequirements for the degree of  \nDoctor of Philosophy  \nUniversity of Washington  \n2025  \nReading Committee:  \nCynthia Chen, Chair  \nShuai Huang, Co-Chair  \nQi ”Ryan”Wang, Member  \nProgram Authorized to Offer Degree:  \nCivil & Environmental Engineering  \n©Copyright 2025 Ekin U˘gurel  \nUniversity of Washington  \nAbstract  \nBehaviorally Informed Machine Learning for Human Mobility  \nEkin U˘gurel  \nCo-Chairs of the Supervisory Committee:  \nProfessor Cynthia Chen  \nCivil & Environmental Engineering  \nProfessor Shuai Huang  \nIndustrial & Systems Engineering  \nLarge-scale digital trace datasets hold considerable promise for long-range transportation planning, offering the potential to observe mobility at metropolitan scales with far greater temporal and spatial resolution than traditional household travel surveys while capturing the regularities in daily travel behavior that underlie trip-making. Passively-collected mobile (PCM) data (e.g., location signals from smartphones and in-vehicle GPS) are central to this promise. Their usefulness, however, is limited by discontinuities in individual trajectories, privacy constraints that restrict data sharing and integration, and representativeness biases that distort inferred patterns of regional travel demand and travel behavior across population groups. This dissertation addresses these limitations through four contributions. First, it develops a multi-task Gaussian Process-based imputation method (grounded in recurring daily, weekly, and seasonal travel behavior patterns) capable of handling both short-and long-duration gaps in GPS traces, significantly improving the completeness and usability of mobility data. Second, it introduces an individualized, physics-regularized learning framework that produces high-fidelity mobility traces reflective of observed movement patterns. These generated trajectories can be scaled to build richer, more diverse mobility datasets for  \ndeveloping and validating activity-based models. Third, it investigates the predictive signal linking mobility patterns as expressions of travel behavior to sociodemographic attributes that shape those behaviors, and develops imputation strategies for enriching PCM datasets with these inferred labels. This enrichment supports both more detailed planning analyses and a clearer diagnosis of representativeness biases in passively collected data. Finally, through a qualitative study of long-range transportation planners, this dissertation investigates barriers to the adoption of big data products and provides recommendations for their effective integration into planning processes. Together, these contributions bridge methodological advances in machine learning with insights from travel behavior research and the practical needs of public agencies, offering a more transparent and behaviorally coherent foundation for data-driven planning and travel behavior analysis.  \nTABLE OF CONTENTS  \nPage  \nList of Figures ....................................... 1  \nList of Tables ........................................ 5  \nChapter 1: Introduction ................................ 8  \n1.1 Motivation ..................................... 8  \n1.2 State of the Art .................................. 11  \n1.3 Research Objectives ................................ 15  \n1.4 Organization of the Dissertation ......................... 15  \nChapter 2: Correcting Missingness in Passively-generated Mobile Data with MultiTask Gaussian Processes .......................... 17  \n2.1 Introduction .................................... 17  \n2.2 Related Work ................................... 22  \n2.3 Methodological Framework ............................ 25  \n2.4 Implementation .................................. 33  \n2.5 Dataset ...................................... 34  \n2.6 Experiments ...................................","cbCaivOmO4lp7jI6","https://ap.wps.com/l/cbCaivOmO4lp7jI6","pdf",10626052,1,192,"English","en",105,"# List of Figures\n# List of Tables\n# Chapter 1: Introduction\n## 1.1 Motivation\n## 1.2 State of the Art\n## 1.3 Research Objectives\n## 1.4 Organization of the Dissertation\n# Chapter 2: Correcting Missingness in Passively-generated Mobile Data with MultiTask Gaussian Processes\n## 2.1 Introduction\n## 2.2 Related Work\n## 2.3 Methodological Framework\n## 2.4 Implementation\n## 2.5 Dataset\n## 2.6 Experiments\n## 2.7 Discussion\n# Chapter 3: Learning to Generate Synthetic Human Mobility Data: a Physicsregularized Gaussian Process approach based on Multiple Kernel Learning\n## 3.1 Introduction\n## 3.2 Related Work\n## 3.3 Methodology\n## 3.4 Numerical Experiments\n## 3.5 Discussion\n# Chapter 4: On Predicting Sociodemographics from Mobility Signals\n## 4.1 Introduction\n## 4.2 Literature Review\n## 4.3 Datasets\n## 4.4 Methodology\n## 4.5 Experiments\n## 4.6 Conclusion\n# Chapter 5: MPO’s uses of and needs for big data\n## 5.1 Introduction\n## 5.2 Literature Review\n## 5.3 Methods\n## 5.4 Findings\n# Chapter 6: Conclusion, Discussion, and Future Work\n## 6.1 Discussion\n## 6.2 Future Work\n# Appendices","[{\"question\":\"What problem does this dissertation address in passively collected mobile (PCM) mobility data?\",\"answer\":\"It addresses trajectory discontinuities and missingness, privacy constraints that limit sharing and integration, and representativeness biases that distort inferred travel demand and behavior across population groups.\"},{\"question\":\"How does the dissertation improve mobility data completeness?\",\"answer\":\"It develops a multi-task Gaussian Process-based imputation method that leverages recurring daily, weekly, and seasonal travel behavior patterns to handle gaps of both short and long durations.\"},{\"question\":\"What are the goals of the planner-focused qualitative study?\",\"answer\":\"It investigates barriers to adopting big data products by long-range transportation planners and provides recommendations for integrating these products effectively into planning processes.\"}]","Behaviorally Informed Machine Learning for Human Mobility - dissertation proposal | PDF",1785899292,484,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"behaviorally-informed-machine-learning-for-human-mobility-dissertation-proposal","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/behaviorally-informed-machine-learning-for-human-mobility-dissertation-proposal/125487/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this dissertation address in passively collected mobile (PCM) mobility data?","Question",{"text":75,"@type":76},"It addresses trajectory discontinuities and missingness, privacy constraints that limit sharing and integration, and representativeness biases that distort inferred travel demand and behavior across population groups.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the dissertation improve mobility data completeness?",{"text":80,"@type":76},"It develops a multi-task Gaussian Process-based imputation method that leverages recurring daily, weekly, and seasonal travel behavior patterns to handle gaps of both short and long durations.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the goals of the planner-focused qualitative study?",{"text":84,"@type":76},"It investigates barriers to adopting big data products by long-range transportation planners and provides recommendations for integrating these products effectively into planning processes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]