[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126731-en":3,"doc-seo-126731-105":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},126731,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Machine learning application powering automation of efficient traffic operation - 博士论文","Machine learning techniques enable automated, data-driven traffic operations aimed at improving efficiency and congestion management. The dissertation presents a congestion identification and classification workflow using speed-based traffic state estimation and traffic-state clustering, followed by recurrent versus nonrecurrent congestion labeling. It further develops automatic detector mapping metadata generation from high-resolution control inputs, including detector type and phase identification with reported accuracy measures. A computer vision vehicle-tracking study addresses tracking opportunities and challenges, supporting smarter intelligent infrastructure.","Machine learning application powering automation of efficient traffic operation  \nby  \nAtousa Zarindast  \nA dissertation submitted to the graduate faculty  \nin partial fulfillment of the requirements for the degree of  \nDOCTOR OF PHILOSOPHY  \nMajor: Civil Engineering (Intelligent Infrastructure Engineering)  \nProgram of Study Committee:  \nAnuj Sharma, Co-major Professor  \nChristopher Day, Co-major Professor  \nJohnathan Wood  \nJing Dong  \nSoumik Sarkar  \nThe student author, whose presentation of the scholarship herein was approved by the program of study committee, is solely responsible for the content of this dissertation. The Graduate College will ensure this dissertation is globally accessible and will not permit alterations after a  \ndegree is conferred  \nIowa State University  \nAmes, Iowa  \n2022  \nCopyright © Atousa Zarindast, 2022. All rights reserved.  \nTABLE OF CONTENTS  \nPage  \nLIST OF FIGURES ....................................................................................................................... iv  \n[LIST OF TABLES ......................................................................................................................... vi](LIST OF TABLES ......................................................................................................................... vi)  \n[ABSTRACT...............................](ABSTRACT...............................)................................................................................................... vii  \nCHAPTER 1. GENERAL INTRODUCTION ................................................................................1  \nReferences ................................................................................................................................. 6  \nCHAPTER 2. A DATA DRIVEN METHOD FOR CONGESTION IDENTIFICATION AND CLASSIFICATION .........................................................................................................................8  \n2.1. Abstract ........................................................................................................................... 8  \n2.2. Introduction ..................................................................................................................... 8  \n2.3. Methodology ................................................................................................................. 12  \n2.3.1. Study location and description of the data....................................................... 12  \n2.3.2. Mathematical abstraction of the data ............................................................... 13  \n2.3.3. Smoothing........................................................................................................ 14  \n2.3.4. Identifying traffic states based upon speed values .......................................... 15  \n2.3.5. Congestion detection based on traffic state clustering .................................... 18  \n2.3.6. Recurrent vs. nonrecurrent congestion classification ...................................... 20  \n2.4. Results ........................................................................................................................... 21  \n2.5. Discussion ..................................................................................................................... 23  \n2.6. Conclusion .................................................................................................................... 25  \n2.7. References ..................................................................................................................... 26  \nCHAPTER 3. AUTOMATIC GENERATION OF DETECTOR MAPPING META DATA USING HIGH RESOLUTION CONTROL INPUT......................................................................30  \n3.1. Abstract ......................................................................................................................... 30  \n3.2. Introduction ..................................................","cbCaimJJWlcF53wX","https://ap.wps.com/l/cbCaimJJWlcF53wX","pdf",2163839,1,91,"English","en",105,"# Chapter 1. General Introduction\n# Chapter 2. A Data Driven Method for Congestion Identification and Classification\n## 2.3 Methodology\n## 2.4 Results\n# Chapter 3. Automatic Generation of Detector Mapping Meta Data Using High Resolution Control Input\n## 3.3 Problem statement\n## 3.6 Results\n# Chapter 4. Opportunities and Challenges in Vehicle Tracking - A Computer Vision Based Vehicle Tracking System\n## 4.2 (continued)","[{\"question\":\"What is the dissertation’s main goal for traffic operations automation?\",\"answer\":\"To use machine learning to automate efficient traffic operation by identifying and classifying congestion and generating supporting detector/phase metadata.\"},{\"question\":\"How does the congestion identification method work?\",\"answer\":\"It estimates traffic states from speed values, applies traffic-state clustering for congestion detection, and then distinguishes recurrent from nonrecurrent congestion.\"},{\"question\":\"What does the dissertation contribute regarding detector mapping metadata?\",\"answer\":\"It proposes an approach to automatically generate detector mapping metadata from high-resolution control inputs, including identification of detector types and phase states with accuracy evaluation.\"}]","Machine learning application powering automation of efficient traffic operation - 博士论文 | PDF",1785934481,229,{"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},"machine-learning-application-powering-automation-of-efficient-traffic-operation-phd-dissertation","",{"@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/machine-learning-application-powering-automation-of-efficient-traffic-operation-phd-dissertation/126731/",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 is the dissertation’s main goal for traffic operations automation?","Question",{"text":75,"@type":76},"To use machine learning to automate efficient traffic operation by identifying and classifying congestion and generating supporting detector/phase metadata.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the congestion identification method work?",{"text":80,"@type":76},"It estimates traffic states from speed values, applies traffic-state clustering for congestion detection, and then distinguishes recurrent from nonrecurrent congestion.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the dissertation contribute regarding detector mapping metadata?",{"text":84,"@type":76},"It proposes an approach to automatically generate detector mapping metadata from high-resolution control inputs, including identification of detector types and phase states with accuracy evaluation.","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"]