[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127253-en":3,"doc-seo-127253-105":30,"detail-sidebar-cat-0-en-105":95},{"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},127253,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Estimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling - Course MEE 498 Honors Capstone Project","Research compares pedestrian crossing times at scramble crosswalks versus conventional two-phase crosswalks, focusing on pedestrians’ diagonal crossing ability and resultant walking behaviors. An agent-based model identifies behavior patterns that produce the fastest crossing times across two configurations. Machine learning regression then fits polynomials relating crossing time to prominent walking behaviors, enabling quantitative comparisons and graphical analysis. Results indicate that relaxed walking style improves efficiency in each configuration, while scramble crossings generally yield lower crossing times when pedestrian traffic is sufficient; otherwise, diagonal additions can show no benefit or even increases in time.","Northern Illinois University  \nSpring 2025  \nEstimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling  \nCourse: MEE 498 (Special Topic Course: Machine Learning); Honors Capstone Project  \nInstructor/Mentor: Dr. Kyu Taek Cho  \nDate: 04/21/2025  \nAuthor: Sho Takami  \n1  \nAbstract  \nScramble crosswalks differ from conventional crosswalks in their ability for pedestrians to cross diagonally. This research compares the average crossing times and investigates the walking behaviors that pedestrians adopt to produce the speediest times in the two crosswalk configurations. Identification of the most efficient set of walking behaviors is done through an agent-based model, whereas producing polynomials relating crossing times to the most prominent walking behaviors is done through regression algorithms in machine learning. With the combination of these two approaches, it is revealed that pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient. Additionally, between conventional and scramble crosswalks, the scramble configuration generally leads to lower crossing times, provided that there is sufficient pedestrian traffic. In all other cases, transitioning from a conventional to scramble design by the addition of diagonal routes leads to no significant changes – or even an increase – in crossing times.  \n2  \nAbstract...........................................................................................................................................1  \nList of Figures.................................................................................................................................3  \nList of Tables...................................................................................................................................4  \nIntroduction....................................................................................................................................5  \nMethodology................................................................................................................................... 6  \nData Collection from the Scramble Crossing Agent-Based Model........................................... 6  \nUsage of Regression Methods in Machine Learning.................................................................9  \nResults and Discussion.................................................................................................................10  \nObservations from the Agent-Based Model............................................................................ 10  \nObservations from the Machine Learning Regression Process............................................... 12  \nFinding The “Break-Even Region” from the Regression Formulas........................................ 14  \nConclusions................................................................................................................................... 16  \nReferences..................................................................................................................................... 17  \nAppendix....................................................................................................................................... 18  \n3  \nList of Figures  \nFigure 1: Shibuya Crossing, Page 5  \nFigure 2: NetLogo Braess’ Paradox Model vs. Scramble Crossing Model, Page 6  \nFigure 3: Visualization of RX-, H-, and P-PZ’s, Page 7  \nFigure 4: Standard and Scramble Simulations: t = 100 ticks, P0 = 3000, Page 11  \nFigure 5: Number of Pedestrians and Walking Types in Standard (left) and Scramble (right) Simulations: P0 = 3000, Page 11  \nFigure 6: Crossing Times vs. Population, Diagonals Off and On, Page 13  \nFigure 7: Crossing Times vs. Crossing Type, Passive and Relaxed Behaviors, Page 13  \nFigure 8: Desmos Visualization of Optimal Crosswalk Choice and the “Break-Even Region”, Page 15  \n4  \nList of Tables  \nTable 1: Intercepts and Coeffi","cbCaitta8Mgg29Cc","https://ap.wps.com/l/cbCaitta8Mgg29Cc","pdf",8768033,1,20,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data Collection from the Scramble Crossing Agent-Based Model\n## Usage of Regression Methods in Machine Learning\n# Results and Discussion\n## Observations from the Agent-Based Model\n## Observations from the Machine Learning Regression Process\n## Finding The “Break-Even Region” from the Regression Formulas\n# Conclusions\n# References\n# Appendix","[{\"question\":\"What problem does this project address about scramble crossings?\",\"answer\":\"It addresses the lack of professional research that quantitatively assesses time efficiency for pedestrians at scramble crossings worldwide, and evaluates their feasibility for wider implementation.\"},{\"question\":\"How are crossing times analyzed in the study?\",\"answer\":\"An agent-based model simulates walking behaviors and captures their impact on crossing completion times, then machine learning regression fits polynomial relationships between behaviors and crossing times.\"},{\"question\":\"What walking behavior is found to improve efficiency?\",\"answer\":\"Pedestrians generally need to adopt a relaxed walking style to make each crosswalk configuration efficient.\"},{\"question\":\"When do scramble crossings outperform conventional crossings?\",\"answer\":\"Scramble crossings generally lead to lower crossing times when there is sufficient pedestrian traffic; otherwise, switching from conventional to scramble design may produce no significant change or even increase crossing times.\"}]","Estimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling - Course MEE 498 Honors Capstone Project | PDF",1785937766,50,{"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":90,"head_meta":92,"extra_data":94,"updated_unix":28},"estimating-pedestrian-crossing-times-at-scramble-crossings-via-machine-learning-and-agent-based-modeling-course-mee-498-honors-capstone-project","",{"@graph":36,"@context":89},[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/estimating-pedestrian-crossing-times-at-scramble-crossings-via-machine-learning-and-agent-based-modeling-course-mee-498-honors-capstone-project/127253/",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",[71,77,81,85],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does this project address about scramble crossings?","Question",{"text":75,"@type":76},"It addresses the lack of professional research that quantitatively assesses time efficiency for pedestrians at scramble crossings worldwide, and evaluates their feasibility for wider implementation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are crossing times analyzed in the study?",{"text":80,"@type":76},"An agent-based model simulates walking behaviors and captures their impact on crossing completion times, then machine learning regression fits polynomial relationships between behaviors and crossing times.",{"name":82,"@type":73,"acceptedAnswer":83},"What walking behavior is found to improve efficiency?",{"text":84,"@type":76},"Pedestrians generally need to adopt a relaxed walking style to make each crosswalk configuration efficient.",{"name":86,"@type":73,"acceptedAnswer":87},"When do scramble crossings outperform conventional crossings?",{"text":88,"@type":76},"Scramble crossings generally lead to lower crossing times when there is sufficient pedestrian traffic; 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