[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118676-en":3,"doc-seo-118676-105":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},118676,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",8,"Research & Report","Evaluating Machine Learning Predictions for Budget Overruns in U.S. Mass Transit Projects - A Correlational Study - Dissertation Manuscript","This study evaluates supervised and unsupervised machine learning approaches to determine whether statistical correlations can validate U.S. mass transit project budget forecasts. Capital budget estimates for new projects have often been underestimated, creating budget overruns that complicate Federal Transit Administration funding decisions and force local transit authorities to obtain additional financing. Data from 108 projects drawn from publicly available U.S. government documents were modeled using SPSS with a neural network and k-means clustering, producing high variance explanations (R2=0.946 and R2=0.882). Results support an AI-driven, reproducible budget validation process that incorporates project-specific and environmental variables while improving transparency versus custom code.","Evaluating Machine Learning Predictions for Budget Overruns in U.S. Mass Transit  \nProjects: A Correlational Study  \nDissertation Manuscript  \nSubmitted to National University  \nSchool of Business and Economics  \nin Partial Fulfillment of the  \nRequirements for the Degree of  \nDOCTOR OF BUSINESS ADMINISTRATION  \nby  \nPAUL ARTHUR BOUDREAU  \nSan Diego, California  \nAbstract  \nThis study examined supervised and unsupervised machine learning methods to determine if statistical correlations could be established to validate mass transit project budgets. Capital budget forecasts for new mass transit projects in the United States have consistently been underestimated. These budget overruns resulted in funding allocation challenges for the Federal Transit Administration and obligated local transit authorities to secure additional financing. Developing accurate budget estimates is a significant challenge for project managers, particularly given the influence of cognitive biases such as optimism bias. Data from 108 projects were collected from publicly available U.S. government documents to serve as input for the machine learning models. Using a neural network and k-means clustering in SPSS software, the study created models that explained 94 .6%(R2 = 0 .946) and 88 .2%(R2 = 0 . 882) of the variance in project budget outcomes. The findings highlighted the importance for organizations to apply AIbased machine learning technology to validate budget forecasts. This research built on prior research by applying machine learning methods that had been explored in other project contextsand problem domains. By employing SPSS software with configurable settings, rather than relying on customized Python code, the study enhanced the transparency and accessibility of machine learning regression analysis. The results demonstrated that a budget validation process could be developed using reproducible, data-driven predictive models that incorporate both project-specific and environmental variables.  \nTable of Contents  \nChapter 1: Introduction ................................................................................................................... 1  \nStatement of the Problem .......................................................................................................... 3  \nPurpose of the Study ................................................................................................................. 4  \nIntroduction to the Conceptual Framework .............................................................................. 5  \nIntroduction to the Research Methodology and Design ........................................................... 7  \nResearch Questions ................................................................................................................... 9  \nSignificance of the Study ........................................................................................................ 10  \nDefinition of Key Terms ......................................................................................................... 11  \nSummary ................................................................................................................................. 12  \nChapter 2: Literature Review ........................................................................................................ 13  \nLiterature Review Strategy ..................................................................................................... 14  \nConceptual Framework ........................................................................................................... 16  \nProject Budget Methodology .................................................................................................. 20  \nU.S. Mass Transit Systems ..................................................................................................... 33  \nMachine Learning .......................................................................","cbCaic89z48KdKVe","https://ap.wps.com/l/cbCaic89z48KdKVe","pdf",1200599,1,159,"English","en",105,"# Abstract\n# Chapter 1: Introduction\n## Statement of the Problem\n## Purpose of the Study\n## Introduction to the Conceptual Framework\n## Introduction to the Research Methodology and Design\n## Research Questions\n## Significance of the Study\n## Definition of Key Terms\n## Summary\n# Chapter 2: Literature Review\n## Literature Review Strategy\n## Conceptual Framework\n## Project Budget Methodology\n## U.S. Mass Transit Systems\n## Machine Learning\n## Data Management\n## Applying Machine Learning to Project Management\n## Additional Algorithm Types\n## Using SPSS for Machine Learning Research\n## Addressing Bias in Machine Learning\n## Summary\n# Chapter 3: Research Method\n## Research Methodology and Design\n## Population\n## Instrumentation\n## Study Procedures\n## Data Analysis\n## Machine Learning Using Supervised Learning\n## Machine Learning Using Unsupervised Learning\n## Assumptions","[{\"question\":\"What problem does the dissertation address in U.S. mass transit projects?\",\"answer\":\"It addresses persistent underestimation of capital budget forecasts, which leads to budget overruns and funding allocation challenges for federal and local transit stakeholders.\"},{\"question\":\"Which machine learning techniques were used, and how were they implemented?\",\"answer\":\"The study used a neural network and k-means clustering implemented in SPSS software, using data from 108 projects sourced from publicly available U.S. government documents.\"},{\"question\":\"How do the results contribute to improving budget estimation and validation?\",\"answer\":\"The findings show that reproducible, data-driven predictive models can validate budget forecasts by incorporating both project-specific and environmental variables, supporting an AI-based validation process.\"}]","Evaluating Machine Learning Predictions for Budget Overruns in U.S. Mass Transit Projects - A Correlational Study - Dissertation Manuscript | PDF",1785684844,401,{"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},"evaluating-machine-learning-predictions-for-budget-overruns-in-us-mass-transit-projects-a-correlational-study-dissertation-manuscript","",{"@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/evaluating-machine-learning-predictions-for-budget-overruns-in-us-mass-transit-projects-a-correlational-study-dissertation-manuscript/118676/",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","2026-08-02",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 the dissertation address in U.S. mass transit projects?","Question",{"text":75,"@type":76},"It addresses persistent underestimation of capital budget forecasts, which leads to budget overruns and funding allocation challenges for federal and local transit stakeholders.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning techniques were used, and how were they implemented?",{"text":80,"@type":76},"The study used a neural network and k-means clustering implemented in SPSS software, using data from 108 projects sourced from publicly available U.S. government documents.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the results contribute to improving budget estimation and validation?",{"text":84,"@type":76},"The findings show that reproducible, data-driven predictive models can validate budget forecasts by incorporating both project-specific and environmental variables, supporting an AI-based validation process.","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"]