[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121486-en":3,"doc-seo-121486-105":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},121486,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Data-Driven Machine Learning for Simulating and Predicting Urban Intersection Traffic - Master of Science Thesis","Urban traffic systems are difficult to operate due to scarce and unreliable data, limited ability to build realistic scenarios, and uncertainty in short-term traffic-volume forecasts. This thesis examines how machine learning enhances simulation and management by addressing data quality issues, improving realistic scenario construction, and enabling more reliable volume prediction. It reviews existing traffic simulators and models, surveys ML-based approaches, implements a DQN signal control system in Newcastle, Ontario, and develops CrossFlow to generate SUMO scenarios from real-world Turning Movement Count data, validated across 106 Toronto intersections.","Data-Driven Machine Learning for Simulating and Predicting Urban Intersection Traffic  \nby  \nHarshit Maheshwari  \nA thesis submitted to the School of Graduate and Postdoctoral Studies in partial fulfillment of the requirements for the degree of  \nMaster of Science in Computer Science  \nFaculty of Business & IT  \nUniversity of Ontario Institute of Technology (Ontario Tech University)  \nOshawa, Ontario, Canada  \nAugust 2025  \n© Harshit Maheshwari, 2025  \nThesis Examination Information  \nSubmitted by: Harshit Maheshwari  \nMaster of Science in Computer Science  \nThesis title: Data-Driven Machine Learning for Simulating and Predicting Urban  \nIntersection Traffic  \nAn oral defense of this thesis took place on August 5, 2025, in front of the following examining committee:  \nExamining Committee:  \nChair of Examining Committee Research Supervisor  \nResearch Co-Supervisor  \nExamining Committee Member  \nThesis Examiner  \nDr. Ali Neshati  \nDr. Richard W. Pazzi  \nDr. Li Yang  \nDr. Pooria Madani  \nDr. Mehram Ebrahimi  \nThe above committee determined that the thesis is acceptable in form and content and that a satisfactory knowledge of the field covered by the thesis was demonstrated by the candidate during an oral examination. A signed copy of the Certificate of Approval is available from the School of Graduate and Postdoctoral Studies.  \nAbstract  \nUrban traffic systems are hard to manage because quality data are scarce, realistic scenarios are difficult to build, and short-term volume forecasts are uncertain. This thesis investigates how machine learning improves urban traffic simulation and management, tackling poor data quality, realistic scenario creation, and reliable volume forecasting to bridge the gap between simulation and reality. It reviews existing simulators and models, surveys the use of Machine Learning in the context of urban traffic simulation, implements a DQN-based signal control system in Newcastle, Ontario that cuts average wait times by approximately 22%, introduces an open-source tool called CrossFlow to convert real world Turning Movement Count data into realistic SUMO scenarios, and analyzes several deep-learning architectures for traffic-volume forecasting on Toronto vehicular data. Validation across 106 Toronto intersections with varied data availability shows generalizable gains, indicating that adaptive, data-driven methods can improve urban traffic simulation and management.  \nkeywords: Machine Learning, Urban Traffic Simulation, Intersection modeling, SUMO, Traffic prediction  \nAuthor’s Declaration  \nI hereby declare that this thesis consists of original work of which I have authored. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I authorize the University of Ontario Institute of Technology (Ontario Tech University) to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize University of Ontario Institute of Technology (Ontario Tech University) to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. I understand that my thesis will be made electronically available to the public.  \nHarshit Maheshwari  \nStatement of Contribution  \nThis thesis represents the culmination of my research conducted under the supervision and guidance of Dr. Richard Pazzi and Dr. Li Yang at Ontario Tech University.  \nIn Chapter 3, an extensive literature survey is conducted to cover emerging machine learning techniques in urban traffic simulation, systematically analyzing state-of-the-art approaches across traffic signal control, vehicle lane changing, car following behavior, vehicle trajectory prediction, highway control, and vehicle routing. This comprehensive review, which covered a wide range of research papers, established the theoretical foundation and identified research opportunities for applying Ma","cbCaipU9mRUCK6l0","https://ap.wps.com/l/cbCaipU9mRUCK6l0","pdf",28953081,1,264,"English","en",105,"# Abstract\n# Thesis Examination Information\n# Author’s Declaration\n# Statement of Contribution\n## Chapter 3: Literature Survey\n## Chapter 4: Reinforcement Learning Signal Control\n## Chapter 5: CrossFlow and Turning Movement Count to SUMO","[{\"question\":\"What core problem does the thesis address in urban traffic systems?\",\"answer\":\"It addresses limited data quality, difficulty building realistic simulation scenarios, and uncertainty in short-term traffic-volume forecasting.\"},{\"question\":\"What method is implemented for traffic signal control in Newcastle, Ontario?\",\"answer\":\"A DQN-based reinforcement learning signal control system is implemented to control multiple intersection signals in a realistic scenario.\"},{\"question\":\"What is CrossFlow and how does it contribute to simulation realism?\",\"answer\":\"CrossFlow converts real-world Turning Movement Count data from Toronto into realistic SUMO scenarios by automating map retrieval, intersection mapping, and defining vehicular flows; a GUI is also integrated.\"}]","Data-Driven Machine Learning for Simulating and Predicting Urban Intersection Traffic - Master of Science Thesis | PDF",1785735873,665,{"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},"data-driven-machine-learning-for-simulating-and-predicting-urban-intersection-traffic-master-of-science-thesis","",{"@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/data-driven-machine-learning-for-simulating-and-predicting-urban-intersection-traffic-master-of-science-thesis/121486/",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","2026-08-03",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 core problem does the thesis address in urban traffic systems?","Question",{"text":75,"@type":76},"It addresses limited data quality, difficulty building realistic simulation scenarios, and uncertainty in short-term traffic-volume forecasting.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What method is implemented for traffic signal control in Newcastle, Ontario?",{"text":80,"@type":76},"A DQN-based reinforcement learning signal control system is implemented to control multiple intersection signals in a realistic scenario.",{"name":82,"@type":73,"acceptedAnswer":83},"What is CrossFlow and how does it contribute to simulation realism?",{"text":84,"@type":76},"CrossFlow converts real-world Turning Movement Count data from Toronto into realistic SUMO scenarios by automating map retrieval, intersection mapping, and defining vehicular flows; 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