[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127256-en":3,"doc-seo-127256-105":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},127256,2336475104362,"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 - Honors Capstone","Scramble crosswalks enable diagonal pedestrian crossings, unlike conventional designs. This research compares average crossing times and analyzes pedestrian walking behaviors that produce the fastest results under two crosswalk configurations. An agent-based model identifies efficient walking behavior sets, while machine-learning regression derives polynomial relationships between crossing times and key behaviors. The combined methods indicate pedestrians benefit from adopting a relaxed walking style for both configurations. The study also finds scramble crossings generally yield lower crossing times when pedestrian traffic is sufficient, while other conditions show no significant improvement or potential increases when adding diagonal routes.","Northern Illinois University  \nHuskie Commons  \n\n| Honors Capstones | Undergraduate Research & Artistry |\n| --- | --- |\n| Spring 5-5-2025\u003Cbr>Estimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling\u003Cbr>Sho Takami\u003Cbr>Northern Illinois University\u003Cbr>Follow this and additional works at: [https://huskiecommons.lib.niu.edu/studentengagement](https://huskiecommons.lib.niu.edu/studentengagement)honorscapstones\u003Cbr> Part of the Applied Mathematics Commons, and the Mechanical Engineering Commons |  |\n\nRecommended Citation  \nTakami, Sho, \"Estimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling\" (2025) . Honors Capstones. 1561.  \n[https://huskiecommons.lib.niu.edu/studentengagement-honorscapstones/1561](https://huskiecommons.lib.niu.edu/studentengagement-honorscapstones/1561)  \nThis Student Project is brought to you for free and open access by the Undergraduate Research & Artistry at Huskie Commons. It has been accepted for inclusion in Honors Capstones by an authorized administrator of Huskie Commons. For more information, please contact [jschumacher@niu.edu](jschumacher@niu.edu).  \nNorthern Illinois University  \nSpring 2025  \nEstimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling  \nA Capstone Submitted to the  \nUniversity Honors Program  \nIn Partial Fulfillment of the  \nRequirements of the Baccalaureate Degree  \nWith Honors  \nDepartment of  \nMechanical Engineering  \nBy  \nSho Takami  \nDeKalb, Illinois  \n05/10/2025  \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","cbCainskjYmIAIPg","https://ap.wps.com/l/cbCainskjYmIAIPg","pdf",8805959,1,21,"English","en",105,"# Abstract\n# List of Figures\n# List of Tables\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\":\"How does the study estimate pedestrian crossing times at scramble crossings?\",\"answer\":\"It uses an agent-based model to represent pedestrian walking behaviors and machine-learning regression to generate polynomial formulas relating crossing times to prominent behaviors.\"},{\"question\":\"What walking behavior style improves crossing-time performance in both configurations?\",\"answer\":\"The results show pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient.\"},{\"question\":\"When does switching from a conventional crosswalk to a scramble design reduce crossing times?\",\"answer\":\"Scramble crossings generally lead to lower crossing times when there is sufficient pedestrian traffic; otherwise, the change shows no significant improvement and can increase crossing times.\"}]","Estimating Pedestrian Crossing Times at Scramble Crossings via Machine Learning and Agent-Based Modeling - Honors Capstone | PDF",1785937775,53,{"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},"estimating-pedestrian-crossing-times-at-scramble-crossings-via-machine-learning-and-agent-based-modeling-honors-capstone","",{"@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/estimating-pedestrian-crossing-times-at-scramble-crossings-via-machine-learning-and-agent-based-modeling-honors-capstone/127256/",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},"How does the study estimate pedestrian crossing times at scramble crossings?","Question",{"text":75,"@type":76},"It uses an agent-based model to represent pedestrian walking behaviors and machine-learning regression to generate polynomial formulas relating crossing times to prominent behaviors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What walking behavior style improves crossing-time performance in both configurations?",{"text":80,"@type":76},"The results show pedestrians must adopt a relaxed walking style to make each crosswalk configuration efficient.",{"name":82,"@type":73,"acceptedAnswer":83},"When does switching from a conventional crosswalk to a scramble design reduce crossing times?",{"text":84,"@type":76},"Scramble crossings generally lead to lower crossing times when there is sufficient pedestrian traffic; 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