[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118805-en":3,"doc-seo-118805-105":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},118805,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities - read online","Smart city initiatives aim to reduce vehicle congestion by forecasting traffic using data from connected smart devices. The study builds an Intelligent Traffic Congestion Management System (ITCMS) leveraging machine learning with Kaggle traffic data and applies feature selection to reduce dimensionality via information gain, correlation attributes, and principal component analysis. Models are trained on pre-processed data and evaluated by predictive performance, where principal component analysis combined with random forest achieves the best results with 95% accuracy, supporting improved traffic flow prediction and congestion mitigation.","Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in  \nSmart Cities  \nJ.Jenifer1, Dr. R. Jemima Priyadarsini2  \n1Research Scholar, Department of Computer Science,Bishop Heber College,  \nAffiliated with Bharathidasan University,  \nTrichy17,Tamilnadu,India  \n[jenifer.john3@gmail.com](jenifer.john3@gmail.com)  \n2Associate Professor, Department of Computer Science,Bishop Heber College,  \nAffiliated withBharathidasan University,  \nTrichy-17,Tamilnadu,India  \n[jemititus@gmail.com](jemititus@gmail.com)  \nAbstract—The development of smart cities has occurred over the past ten years. One primary goal of “smart city” initiatives is to lessen vehicle congestion. Several innovative technologies, including vehicular communications, navigation, and traffic control, have been created by Vehicle Networking System to address this problem. The traffic data gathered by smart devices aids in the forecasting of traffic in smart cities. This project created an Intelligent Traffic Congestion Management System (ITCMS) that uses machine learning techniques and traffic data from Kaggle to decrease the amount of time spent stuck in traffic. This study aims to assess feature selection methods and machine learning models for traffic forecasting in smart cities. The feature dimension is reduced using feature selection techniques, such information gain, correlation attribute, and principal component analysis. The recommended model successfully predicted traffic flow, assisting in the alleviation of congestion. The principal component analysis with random forest model outperforms the other machine learning models and has a 95% accuracy rate.  \nKeywords-IoT, machine learning, feature selection, PCA, Smart city.  \nI. INTRODUCTION  \nPeer-to-peer networking and Internet Mobile were replaced by the World Wide Web and the Internet of Things (IoT), respectively, over the course of several decades. Throughout the IoT, users and things are able to connect with one another at any time, from any location, and take advantage of any available network or resource. The IoT has the potential to completely transform the way in which people interact with their surroundings and provide intelligent services that offer value in a variety of different application areas [1] . Over the course of time, the amount of data that is unusually complete has increased from one hundred forty-five zettabytes in the year 2015 to eight hundred zettabytes in the year 2023.  \nIn addition, IoT is generating new applications while simultaneously delivering new kinds of devices. For instance, numerous modern Internet-based devices [2] are put to use, such as alarms, intelligent weather sensors, traffic lights, cameras, and meteorological stations [3] . In addition to this, it is becoming common practise to generate one-of-a-kind information for the categorization of intelligent applications, such as those used in the healthcare industry, other sectors, security breachers [4], and the transportation sector [5] .  \nHowever, most of these IoT devices have limited computational capabilities [6] and are unable to deal with the information that is created on the device itself [7] .  \nEdge computing is a workable solution because it enables fundamental systems on the system's periphery to concurrently approve the consistent processing of IoT information and offer suitable networks and computing resources [8] . However, as noted mentioned in the article [9], calculating the data insights is very difficult owing to many kinds of devices. The information related to the IoT is created because of the dynamic rotation process of the devices themselves. Due to the need that IoT access providers be used, there is a possibility that entire communication may be disrupted when there are a high number of IoT devices. Because of this, it is now abundantly evident how vital it is to anticipate various characteristics that will become available in IoT devices overtime ","cbCairUAAI1x8HcN","https://ap.wps.com/l/cbCairUAAI1x8HcN","pdf",261859,1,7,"English","en",105,"# Introduction\n# Related Work","[{\"question\":\"What goal does the study address in smart cities?\",\"answer\":\"It targets reducing vehicle congestion by improving traffic forecasting using data collected from smart devices.\"},{\"question\":\"Which feature selection methods are used to reduce data dimensionality?\",\"answer\":\"The approach applies information gain, correlation attribute analysis, and principal component analysis (PCA).\"},{\"question\":\"Which machine learning setup performs best and what accuracy is reported?\",\"answer\":\"PCA combined with a random forest model outperforms other models, achieving 95% accuracy in predicting traffic flow.\"}]","Empirical Research on Machine Learning Models and Feature Selection for Traffic Congestion Prediction in Smart Cities - 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