[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122653-en":3,"doc-seo-122653-105":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},122653,687197100911,"Himbo","https://ap-avatar.wpscdn.com/avatar/a000239b6f1da00475?x-image-process=image/resize,m_fixed,w_180,h_180&k=1785132997149421697",6,"Technology","Adaptive traffic lights based on traffic flow prediction using machine learning models","Traffic congestion prediction plays a central role in intelligent transport systems, where rapid urban population growth increases the number of vehicles at intersections. This work predicts traffic flow from traffic data using multiple machine learning regressors, including linear regression, random forest, decision tree, gradient boosting, and K-nearest neighbor models. Using a public UK road traffic dataset, the approach reports strong performance metrics and validates feasibility for smart traffic light deployment. A random-forest-based adaptive signal controller is then implemented and evaluated via simulations.","Adaptive traffic lights based on traffic flow prediction using  \nmachine learning models  \nIdriss Moumen, Jaafar Abouchabaka, Najat Rafalia  \nDepartment of Computer Science, Faculty of Sciences, Ibn Tofail University, Kenitra, Morocco  \nArticle history:  \nReceived Jan 9, 2023 Revised Mar 20, 2023 Accepted Apr 7, 2023  \nKeywords:  \nAdaptive traffic light system Intelligent transport systems Machine learning  \nTraffic prediction Traffic simulation  \nCorresponding Author:  \nTraffic congestion prediction is one of the essential components of intelligent transport systems (ITS) . This is due to the rapid growth of population and, consequently, the high number of vehicles in cities. Nowadays, the problem of traffic congestion attracts more and more attention from researchers in the field of ITS. Traffic congestion can be predicted in advance by analyzing traffic flow data. In this article, we used machine learning algorithms such as linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to predict traffic flow and reduce traffic congestion at intersections. We used the public roads dataset from the UK national road traffic to test our models. All machine learning algorithms obtained good performance metrics, indicating that they are valid for implementation in smart traffic light systems. Next, we implemented an adaptive traffic light system based on a random forest regressor model, which adjusts the timing of green and red lights depending on the road width, traffic density, types of vehicles, and expected traffic. Simulations of the proposed system show a 30.8% reduction in traffic congestion, thus justifying its effectiveness and the interest of deploying it to regulate the signaling problem in intersections.  \nThis is an open access article under the CC BY-SA license.  \nIdriss Moumen  \nDepartment of Computer Science, Faculty of Sciences, Ibn Tofail University B.P 133, University campus, Kenitra, Morocco  \nEmail: [idriss.moumen@uit.ac.ma](idriss.moumen@uit.ac.ma)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nThe intelligent transportation system is an essential part of the smart city [1]–[3] . A thorough understanding of how these systems work and the development of effective traffic management and control strategies are required to optimize their management. Traffic prediction is crucial in traffic systems. An accurate and efficient traffic flow prediction system is needed to achieve intelligent transport systems (ITS)  \n[4], [5] . The traffic flow prediction system is used to provide accurate road status information [6] . However, traffic congestion is a growing global problem as the population increases. Therefore, each traffic intersection is likely to gather a lot of vehicles, and the peak period will be more congested than usual. Consequently, to solve this problem, numerous research studies have been carried out to explore and manage traffic flow to reduce traffic congestion. Traffic flow data were collected from the Tokyo Expressway. These data were used as input to the predictive model [7]. In [8], the model predicted the value of traffic speed on each road section atthe next time stamp using data collected from Beijing. By including a multi-task layer at the conclusion of a deep belief network (DBN) [9] . Concurrently predicted traffic speed and flow. While in [10], to forecast traffic in the upcoming six timestamps simultaneously, the authors used both the iteration approach and the auto-correlation coefficient method. Regression models are easy to implement and suited for traffic prediction tasks on a simple traffic network. According to [11], the mathematical model between inputs and outputs and associated  \nparameters are predetermined, and the relationship between each parameter and input data is relatively certain. The experimental results also show that the linear regression model is more efficient and can provide satisfactory prediction results.  ","cbCaiqny4ZzfeOA4","https://ap.wps.com/l/cbCaiqny4ZzfeOA4","pdf",842860,1,11,"English","en",105,"# Introduction\n## Traffic prediction in intelligent transport systems\n## Machine learning approaches for traffic flow forecasting\n## Regression and KNN methods","[{\"question\":\"Why is traffic flow prediction important for adaptive traffic lights?\",\"answer\":\"Traffic prediction enables accurate road status information and supports timing decisions at intersections, helping address increasing congestion during peak periods.\"},{\"question\":\"Which machine learning models are used to predict traffic flow in the study?\",\"answer\":\"The study uses linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to forecast traffic flow.\"},{\"question\":\"How is the adaptive traffic light system implemented and evaluated?\",\"answer\":\"An adaptive controller based on a random forest regressor adjusts green/red timing according to road width, traffic density, vehicle types, and expected traffic, and simulations show a 30.8% congestion reduction.\"}]","Adaptive traffic lights based on traffic flow prediction using machine learning models | 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is traffic flow prediction important for adaptive traffic lights?","Question",{"text":75,"@type":76},"Traffic prediction enables accurate road status information and supports timing decisions at intersections, helping address increasing congestion during peak periods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are used to predict traffic flow in the study?",{"text":80,"@type":76},"The study uses linear regression, random forest regressor, decision tree regressor, gradient boosting regressor, and K-neighbor regressor to forecast traffic flow.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the adaptive traffic light system implemented and evaluated?",{"text":84,"@type":76},"An adaptive controller based on a random forest regressor adjusts green/red timing according to road width, traffic density, vehicle types, and expected traffic, and simulations show a 30.8% congestion 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