[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116942-en":3,"doc-seo-116942-105":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},116942,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",6,"Technology","Congestion Articulation Control Using Machine Learning Technique","Congestion is addressed across mobile ad hoc networking and road traffic systems through intelligent vehicle communication and routing. The study describes how VANETs, as a subset of MANETs, support real-time traffic guidance using GPS and shortest-path recalculation with multiple algorithms. It further connects traffic congestion forecasting with machine learning by analyzing multiple traffic metrics and recent AI research, proposing a framework for real-time prediction, architecture, and protocols while aiming to reduce jams, delays, and safety risks.","Congestion Articulation Control Using Machine Learning Technique  \nPriyanka Kaushik  \nChandigarh University , Punjab , INDIA  \n[kaushik.priyanka17@gmail.com](kaushik.priyanka17@gmail.com)  \nI. Abstract  \nCongestion is the most serious issue in both Adhoc mobile networking and regular road traffic systems. The definition of a vehicle is changing as the automotive industry advances. Nowadays, all automobiles are outfitted with the most up-to-date sensors and communication capabilities. Mobile Ad Hoc Network that avoids traffic jams and articulation issues while also saving time by receiving direction from the GPS system on the shortest path using various algorithms. It also provides information on road safety and where to go. It repeatedly recalculates the shortest way using multiple algorithms to ensure that the user does not become stuck and stranded in traffic. From the point of view of research, this paper defines the architecture and protocols. However, VANETs are a subset of MANETs and constitute the future of Intelligent Transportation Systems. The development of big data, the latest sensors and probing vehicle data, as well as the widespread use of machine learning technologies, has given articulation control measurement in the traffic congestion area a completely new and different direction. By examining multiple traffic metrics. With machine learning, it is straightforward to forecast traffic congestion. This study is based on traffic congestion forecasting in real-time. This paper presents a summary of recent research conducted using various AI approaches and machine learning models.  \nKEYWORDS: Ad-hoc networks, sensors, IOT, vehicular ad hoc networks, Cloud Computing, Sharing-Resources, Auto-detect, Traffic clear, Scaling, Hidden Markov Model.  \nII. INTRODUCTION  \nThe Most Recent Technology The automotive business has been completely transformed by artificial intelligence, machine learning, and mobile communication systems. Now Communication between multiple devices is possible thanks to sensors. In the field of traffic networking, it has created a new paradigm. Vehicle Ad-hoc Network use has created new opportunities. and opened the door to traffic applications that are fault and congestion free. [1] The ad-hoc network was invented by VANET and comprises of many moving cars and other connecting devices that form a temporary network over a wireless medium and exchange data. Simultaneously, a small temporary network is built, with cars and other equipment acting as nodes in the peer network. It works in the same way that topologies do. Where all nodes communicate with each other. [3] Similarly, all nodes that pass their data receive the data that other nodes are transmitting. Nodes generate useful information after summarising and aggregating data and establish a link by communicating the data to other gadgets.  \n[2][4] . It is an open network because device communication grows in a way that nodes  \nare free to join and depart at any time. [5] Advanced sensors are now standard on new vehicles, making it simple for them to join and traverse the network.  \nMANET has a subset called VANET. An inter-vehicle link enables data to be transmitted back and forth, enhancing network efficiency, boosting traffic efficiency, monitoring road conditions, detecting congestion and jams, lowering collisions, and identifying emergency situations. VANET can transfer data via many hops to far-off devices. [6] . On the VANET, a dynamic topology was used. Vehicles in a dynamic topology move at varying speeds and directions, which makes communication difficult. When a connection between two devices that are exchanging information changes often, such as when it is possible for the connection to break at any time, this is known as intermittent connectivity. It has a number of Mobility Patterns, which are large groupings of vehicles that move according to predetermined patterns as a result of, among other things, traffic signs, speed limits, hi","cbCaitrMXT6Wg2rb","https://ap.wps.com/l/cbCaitrMXT6Wg2rb","pdf",320596,1,7,"English","en",105,"# Abstract\n## Introduction\n## Protocols for Transmission","[{\"question\":\"What problem does the paper focus on?\",\"answer\":\"The paper focuses on reducing traffic congestion and articulation-related issues in both mobile ad hoc networks and road traffic systems.\"},{\"question\":\"How do VANETs contribute to congestion control in the approach?\",\"answer\":\"VANETs enable inter-vehicle communication using routing protocols, helping vehicles exchange data to monitor road conditions and detect congestion and jams.\"},{\"question\":\"How is machine learning used in traffic congestion forecasting?\",\"answer\":\"Machine learning is used to forecast traffic congestion in real time by examining multiple traffic metrics and leveraging recent AI approaches and models.\"}]","Congestion Articulation Control Using Machine Learning Technique | PDF",1785672651,18,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"congestion-articulation-control-using-machine-learning-technique","",{"@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/technology/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/congestion-articulation-control-using-machine-learning-technique/116942/",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","2026-08-02",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 problem does the paper focus on?","Question",{"text":75,"@type":76},"The paper focuses on reducing traffic congestion and articulation-related issues in both mobile ad hoc networks and road traffic systems.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How do VANETs contribute to congestion control in the approach?",{"text":80,"@type":76},"VANETs enable inter-vehicle communication using routing protocols, helping vehicles exchange data to monitor road conditions and detect congestion and jams.",{"name":82,"@type":73,"acceptedAnswer":83},"How is machine learning used in traffic congestion forecasting?",{"text":84,"@type":76},"Machine learning is used to forecast traffic congestion in real time by examining multiple traffic metrics and leveraging recent AI approaches and models.","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},[93,97,101,105,110,113,117,122,127,130,134],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":111,"slug":112},50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":114,"show_sort_weight":115,"slug":116},"Healthcare",40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]