[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122900-en":3,"doc-seo-122900-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122900,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Smart Roads, Smarter Cities - Machine Learning Integration for Dynamic Traffic Management","As cities continue to urbanize, traffic congestion escalates into a critical challenge, while conventional methods fail to provide timely insights that enable effective control. This study proposes a machine learning–driven congestion control system combining YOLO object recognition with a Euclidean Distance Tracker. YOLO delivers fast and accurate real-time vehicle detection, and frame-to-frame linkage via Euclidean distance supports continuous motion understanding. Experimental results in real-world settings show high accuracy across vehicle classes, demonstrating a practical path toward intelligent traffic management and adaptive urban mobility.","Smart Roads, Smarter Cities: Machine Learning Integration for Dynamic Traffic Management  \nCecil Johny1 and Dr. Amrita Sharma2  \n1Indus University  \n[ceciljohny.20.rs@indusuni.ac.in](ceciljohny.20.rs@indusuni.ac.in)  \n2 Indus University  \n[amritasharma.dcs@indusuni.ac.in](amritasharma.dcs@indusuni.ac.in)  \nAbstract—As the world's cities become more urbanised, traffic congestion becomes a major problem. Conventional approaches are unable to deliver timely insights, which impedes the application of efficient congestion control strategies. This study presents a novel machine learning-based traffic congestion control system that combines a Euclidean distance tracker with the YOLO (You Only Look Once) object recognition framework. As cities struggle with the intricacies of increasing traffic, the need for intelligent technologies capable of real-time vehicle surveillance and congestion analytics is highlighted. To address this, the suggested solution goes beyond traditional constraints by using machine learning to accurately detect and track automobiles in urban environments. Utilizing the YOLO object detection framework, which is renowned for its speed and accuracy, the study builds on prior research in computer vision and transportation engineering. By connecting object detections between frames, the Euclidean Distance Tracker improves performance and allows a continuous comprehension of vehicle motions. The system's effectiveness in real-world circumstances is demonstrated by the results, which offer high accuracy across a range of vehicle classes. A major advancement in the development of urban mobility has been made with the integration of YOLO and the Euclidean Distance Tracker, which offers a viable solution for intelligent traffic management.  \nKeywords— Urbanization, Traffic congestion Machine learning ,Intelligent transport systems, YOLO (You Only Look Once),object detection, Euclidean Distance Tracker, Real-time vehicle tracking, Adaptive traffic control  \nI. INTRODUCTION  \nUrbanization's continuous growth has created previously unheard-of difficulties for traffic management, with congestion becoming a major problem in today's cities. Formost individuals, traffic congestion has become a major issue since it causes air pollution, noise, and time wastage. Because the current traffic signal system follows a predetermined time length schedule, it is insufficient to manage the problematic traffic congestions. With the advent of the internet of things, new models of intelligent traffic light systems have been introduced recently. These models use a variety of approaches, including radiofrequency identification, ultrasonic modelling, and predictive modeling[27] . Addressing this challenge requires innovative approaches that harness the power of advanced technologies. This research introduces a novel system for traffic congestion control through the integration of machine learning, specifically employing the YOLO (You Only Look Once) object detection framework and a Euclidean Distance Tracker. The requirement for intelligent systems with realtime vehicle tracking, detection, and congestion analytics is growing as cities struggle to handle the complexity of additional vehicles on the road.  \nThe urgent need for precise and effective traffic control techniques that can adjust to changing urban surroundings is what motivated this work. It is frequently not possible to obtain real-time insights into traffic conditions using traditional approaches, which makes it more difficult to put responsive congestion control measures into place. By using machine learning to precisely identify and track automobiles as they travel through urban thoroughfares, the suggested system seeks to get beyond these constraints. The system provides a comprehensive solution for traffic flow monitoring and management by fusing the tracking capabilities of the Euclidean Distance Tracker with the effectiveness of YOLO for real-time object detection.  \nThe basis","cbCair8pgT68Umr0","https://ap.wps.com/l/cbCair8pgT68Umr0","pdf",424982,1,7,"English","en",105,"# Introduction\n## Related Work\n## Proposed System\n## Methodology and Data\n## Experimental Results\n## Discussion and Conclusion","[{\"question\":\"What problem does the study address in urban traffic management?\",\"answer\":\"The study targets the growing issue of traffic congestion in highly urbanized cities, where congestion control is difficult due to insufficient real-time insights from traditional signal systems.\"},{\"question\":\"How does the proposed system detect and track vehicles?\",\"answer\":\"It uses YOLO for real-time object detection and a Euclidean Distance Tracker to connect detections across frames, improving performance and enabling continuous understanding of vehicle motion.\"},{\"question\":\"Why combine YOLO with a Euclidean Distance Tracker?\",\"answer\":\"YOLO provides speed and accuracy for detection, while frame linkage with Euclidean distance supports consistent tracking and better congestion analytics for dynamic traffic control.\"}]","Smart Roads, Smarter Cities - Machine Learning Integration for Dynamic Traffic Management | PDF",1785813568,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"smart-roads-smarter-cities-machine-learning-integration-for-dynamic-traffic-management","",{"@graph":36,"@context":86},[37,54,69],{"@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/smart-roads-smarter-cities-machine-learning-integration-for-dynamic-traffic-management/122900/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What problem does the study address in urban traffic management?","Question",{"text":76,"@type":77},"The study targets the growing issue of traffic congestion in highly urbanized cities, where congestion control is difficult due to insufficient real-time insights from traditional signal systems.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the proposed system detect and track vehicles?",{"text":81,"@type":77},"It uses YOLO for real-time object detection and a Euclidean Distance Tracker to connect detections across frames, improving performance and enabling continuous understanding of vehicle motion.",{"name":83,"@type":74,"acceptedAnswer":84},"Why combine YOLO with a Euclidean Distance Tracker?",{"text":85,"@type":77},"YOLO provides speed and accuracy for detection, while frame linkage with Euclidean distance supports consistent tracking and better congestion analytics for dynamic traffic control.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},"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":107,"slug":138},19,"General","general"]