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Articles de revues sur le sujet « Traffic congestion control using Image Processing »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Prof.Nitin.Kulkarni, Sanjana, Patil Shradha, Srinidhi, and Vaishnavi. "Traffic Congestion Control Using Image Processing." Recent Trends in Analog Design and Digital Devices 8, no. 2 (2025): 8–12. https://doi.org/10.5281/zenodo.15541895.

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<em>Traffic management is one of the most significant faced by urban areas globally. The growing number of vehicles and the inadequacy of conventional traffic control systems have exacerbated issues like traffic congestion, accidents, pollution, and inefficient emergency response times. This project proposes an innovative solution using image processing combined with Arduino-based traffic lights to enhance traffic management. Real-time traffic data is analyzed using python and OpenCV, while the control system, managed by Arduino Uno, adjusts traffic congestion, violations, and accidents, trigg

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Amin, Reuel. "Traffix : Efficient Traffic Control using IoT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31872.

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This paper aims to alleviate traffic congestion brought on by antiquated, ineffective traffic management systems that are based on a predefined countdown. Long red light delays are the result of these traditional systems, which have a predefined countdown regardless of the actual traffic on a given road. Our system makes sure that time set for the traffic lights reflects the traffic density in real time, which ensures efficient use of time. In order to do this, we first compute the traffic density, which is ascertained by combining image processing methods along with the use of ultrasonic sens

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Kalyan, G. "Design and Development of Traffic Control System using Image Processing." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (2021): 3247–52. http://dx.doi.org/10.22214/ijraset.2021.35806.

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Traffic congestion is now a big issue. Although it seems to penetrate throughout the world, urban towns are the ones which are most effected. And it is expanding in nature that it is necessary to understand the density of roads in real time to better regulate signals and efficient management of transport. Various traffic congestions, such as limited capacity, unrestricted demand, huge Red Light waits might occur. While insufficient capacity and unlimited demand are somehow interconnected, their delay in lighting is difficult to encode and not traffic dependant. The necessity to simulate and op

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Et. al., Lakshmanan M,. "Traffic Light Controller using Image Processing." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 405–11. http://dx.doi.org/10.17762/turcomat.v12i2.824.

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Traffic congestion at junctions is a serious issue on a daily basis. The prevailing traffic light controllers are unable to manage the different traffic flows. Most of the current systems operate on a timing mechanism that changes the signal after a particular interval of time. This may cause frustration and result in motorist's time waste. Traffic congestion is a major problem in the currently existing systems. Delays, safety, parking, and environmental problems are the main issues of current traffic systems that emit smoke and contribute to increasing Global Warming. Sensor-based systems red

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RAJA, Dr V. SAI SHANMUGA, Dr G. GUNASEKARAN, and CHINCHU NAIR. "OPTIMAL TRAFFIC CONTROL SYSTEM FOR TRAFFIC CONGESTION." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 03 (2024): 1–11. http://dx.doi.org/10.55041/ijsrem29177.

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A smart city's traffic management system is regarded as one of its primary components. Traffic jams are a common sight on the roadways in metropolitan areas due to the rapid increase in population and urban mobility. In order to address road traffic management issues and assist authorities with appropriate planning, an intelligent traffic management system utilizing the Yolo algorithm and Open CV approach is proposed in this project. A workable model for counting automobiles in traffic was developed using image processing as the basis. image processing methods classified and tallied moving veh

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Ram, Dr R. Bhargav. "Smart Control of Traffic Light Using Image detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 549–54. http://dx.doi.org/10.22214/ijraset.2024.63152.

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Abstract: Urban areas are increasingly grappling with the issue of traffic congestion, a problem exacerbated by growing populations and the proliferation of motor vehicles. This not only leads to delays and increased stress for commuters, but also contributes to greater fuel usage and environmental pollution. This issue is particularly pronounced in large metropolitan areas. The escalating nature of this problem underscores the necessity for real-time assessments of road traffic density, which can lead to more effective traffic management strategies and signal control. The role of the traffic

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Ms. N. M. Deshmukh, Mr. L. D. Girase, Ms. M. S. Kolhe, Mr. S. Y. Kumat, and Ms. V. V. Kulkarni. "Dynamic Traffic Control System Using Video and Image Processing." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 03 (2025): 825–28. https://doi.org/10.47392/irjaeh.2025.0116.

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Traffic congestion is a serious problem in cities, leading to delays, wasted fuel, and more accidents. Current traffic signal systems often use fixed timing, which doesn’t change based on traffic levels. This makes managing traffic less effective. The motivation behind this project is to create a better traffic control system that adapts to real-time traffic. By using live camera feeds and image processing, we can count vehicles at intersections and adjust traffic lights based on the number of cars present. This helps traffic flow more smoothly, reduces waiting times, and saves fuel. The outco

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Faraj, Mohammed Abdulmaged, and Najmadin Wahid Boskany. "Intelligent Traffic Congestion Control System using Machine Learning and Wireless Network." UHD Journal of Science and Technology 4, no. 2 (2020): 123–31. http://dx.doi.org/10.21928/uhdjst.v4n2y2020.pp123-131.

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Traffic congestion has become a big problem for most people because it increases noise, air pollution, and wasting time. Current normal traffic light system is not enough to manage the traffic problematic congestions because they operate on a fixed-time length plan. In recent years, internet of things led to introducing new models of intelligent traffic light systems; by utilizing different techniques such as predictive-based model, radiofrequency identification, and ultrasonic-based model. The most essential one of these techniques is depends of image processing and microcontroller communicat

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Duran, Elizalde J., Mizpha Joy Aclon, Kevin Clyde H. Chu, and Erwin Jason Lim. "Traffic light management system using image processing." University of the Visayas - Journal of Research 7, no. 1 (2013): 69–80. https://doi.org/10.5281/zenodo.1671185.

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Traffic control is important. Traffic congestion creates many problems in our everyday lives. Travel time increases which lead to waste of time and fuel. A system that can manage traffic through the efficient use of traffic lights by applying an algorithm to control the time for the signals of a traffic light would be beneficial. A system was developed by using a modified waterfall model. Data from the City Traffic Operations Management (CITOM) were utilized. Images were taken using a digital camera from the GMT building and the Montesclaros Building to acquire snapshots of actual traffic zone

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Philip, Armandio, Cheetah Savana Putri, and Putra Maula Arifanggi. "Traffic Light Timer Control Using Raspberry Pi." Aptisi Transactions On Technopreneurship (ATT) 1, no. 2 (2019): 134–43. http://dx.doi.org/10.34306/att.v1i2.37.

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As time goes by and the development of the times is very rapid increase in the number of vehicle volumes is increasing from year to year, coupled with automotive manufacturers who release their products at prices below the standard. This of course can increase the volume of congestion which is the main problem, very heavy traffic causes more time wasted and consumes fuel. The solution offered to overcome the congestion problem is a Timer Traffic Light control system, which is a traffic management system on each road segment used to reduce congestion in traffic lights that occur in big cities t

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Philip, Armandio, Cheetah Savana Putri, and Putra Maulana Arifanggi. "Traffic Light Timer Control Using Raspberry Pi." Aptisi Transactions On Technopreneurship (ATT) 1, no. 2 (2019): 134–43. http://dx.doi.org/10.34306/att.v1i2.77.

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As time goes by and the development of the times is very rapid increase in the number of vehicle volumes is increasing from year to year, coupled with automotive manufacturers who release their products at prices below the standard. This of course can increase the volume of congestion which is the main problem, very heavy traffic causes more time wasted and consumes fuel. The solution offered to overcome the congestion problem is a Timer Traffic Light control system, which is a traffic management system on each road segment used to reduce congestion in traffic lights that occur in big cities t

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R N, Rithesh, Vignesh R., and Anala M. R. "Autonomous Traffic Signal Control using Decision Tree." International Journal of Electrical and Computer Engineering (IJECE) 8, no. 3 (2018): 1522. http://dx.doi.org/10.11591/ijece.v8i3.pp1522-1529.

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<p class="normal">The objective of this paper is to introduce an effective and efficient way of traffic signal light control to optimize the traffic signal duration across each lanes and thereby, to minimize or completely eliminate traffic congestion. This paper introduces a new approach to resolve the traffic congestion problem at junctions by making use of decision trees. The vehicle count in the real time traffic video is determined by Image Processing technique. This information is fed to the decision tree based on which the decision is made regarding the status of traffic signal lig

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Narendra, Mohan, and Mishra Ghanshyam. "Density Based Traffic Control using Image Processing Methods: A Review." International Journal of Innovative Science and Research Technology 7, no. 3 (2022): 279–82. https://doi.org/10.5281/zenodo.6383533.

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Congestion on the roads can result in fatigue, increased fuel consumption, energy and time waste, increased depreciation and fuel costs, air pollution, accidents, and verbal and physical altercation among the commuters. Cost, untrustworthy technology, and the inability of the contemporary system to incorporate elements of the new technology are the few of the challenges which has to be dealt with. Advanced image processing algorithms, such as segmentation and edge detection, can be used to detect vehicle presence in different lanes and calculate traffic density. Thus, India's traffic manag

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JEEVARATHINAM, Mrs A., and KANISHKA S. "REAL-TIME TRAFFIC ANALYSIS AND PREDICTION USING YOLO ALGORITHM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–7. https://doi.org/10.55041/ijsrem42726.

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Currently the traffic control system in our country is non-flexible to the ever growing number of vehicles on the road. Traffic light is the basic element in traffic flow control through specified waiting and going time, fixed traffic light time systems is a bad control way. Intelligent traffic system includes smart way to control traffic light time based on number of vehicles in each lane. Improving traffic signal control system will increase safety, reliability, and traffic flow speed and reduce average travelling and waiting time for passengers. The objective is to design an efficient autom

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B, Sandeep. "Smart Traffic Signaling Using Machine Learning and IoT." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34607.

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The project, titled “Smart Traffic Signaling using Machine Learning and IoT," introduces an innovative solution for optimizing traffic signal control. By harnessing the power of image processing, IoT, and machine learning, this project will be a real-time system that accurately assesses vehicle density at intersections. The project focuses on training a machine learning model to recognize various vehicle types, including bikes, cars, trucks, and heavy vehicles. This adaptive control mechanism aims to enhance traffic flow efficiency, reduce congestion, and contribute to the advancement of intel

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T K, Supriya. "Smart Control of Traffic Lights Using Artificial Intelligence for Dynamic Road Environment." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–9. https://doi.org/10.55041/isjem03604.

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Abstract—Traffic congestion is becoming one of the critical issues with increasing population and automobiles in cities. Traffic jams not only cause extra delay and stress for the drivers, but also increase fuel consumption and air pollution. Although it seems to pervade everywhere, megacities are the ones most affected by it. And its ever-increasing nature makes it necessary to calculate the road traffic density in real-time for better signal control and effective traffic management. The traffic controller is one of the critical factors affecting traffic flow. Therefore, the need for optimizi

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Rao, Dr E. V. Krishna, N. Alekhya, K. Rupa, M. Sai Sujith, and Sk Abdulla Aman Ahmed. "Intelligent Traffic Control System Using ARM7 Controller and Image Processing Techniques." Revista Gestão Inovação e Tecnologias 11, no. 4 (2021): 2726–35. http://dx.doi.org/10.47059/revistageintec.v11i4.2314.

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The urban traffic congestion is being increased day by day due to large number of vehicles are used by dense people in cities. In the current model of the Traffic Control System, the time delay of each signal light is static which leads to lot of waiting time and was tage of fuel. To overcome this problem, intelligent traffic management system of controlling the traffic lights using the ARM 7 controller and camera sensor is proposed. The camera which is installed along the pavement captures the real time video of the road. The video is then processed indifferent stages to find the number of ve

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Sarada, Dr B., P.Bhavana, D.Indu, N.Satwika, and R.Lakshmi. "DENSITY BASED TRAFFIC CONTROL SYSTEM USING CANNY EDGE DETECTION ALGORITHM." Journal of Nonlinear Analysis and Optimization 14, no. 02 (2023): 92–99. http://dx.doi.org/10.36893/jnao.2023.v14i2.0092-0099.

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As the problem of urban traffic congestion intensifies, there is a pressing need for the introduction of advanced technology and equipment to improve the state-of-theart of traffic control. The current methods used such as timers or human control are proved to be inferior to alleviate this crisis. In this paper, a system to control the traffic by measuring the realtime vehicle density using canny edge detection with digital image processing is proposed. This imposing traffic control system offers significant improvement in response time, vehicle management, automation, reliability and overall

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Patil, Prof Dr Soumya. "To Develop an Efficient Critical Vehicle Seamless Movement Technique using AI and ML Methods." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 1559–64. http://dx.doi.org/10.22214/ijraset.2022.45520.

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Abstract: Traffic congestion is becoming one of the critical issues by posing significant hurdles to critical vehicles, to nature by posing significant pollution. Not only can traffic congestion delay the movement of essential vehicles, but it can also extend travel times, contribute to driver stress, and increase fuel consumption and pollution. Megacities struggle the most from traffic congestion, despite the fact that they seem to be everywhere. Because of this, real-time road traffic density calculations are necessary for better signal control and traffic management. The traffic controller

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Parate, Mr Saurabh. "IoT Based Smart Traffic Management System using Image Processing and Automated Street Lighting System." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 570–73. http://dx.doi.org/10.22214/ijraset.2024.64190.

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Through the integration of image processing, IoT, and automatic street lighting, this study presents a novel solution to urban traffic management. Real-time traffic data, such as vehicle counts and congestion levels, are continuously collected by smart cameras positioned at strategic intersections. A central control unit receives this data and uses sophisticated algorithms to dynamically modify traffic signal timings. Additionally, by including sensors to identify surrounding cars and pedestrians, an automatic street lighting system improves sustainability. This system helps save energy and mo

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Bathula Lakshmipathi and P Srujana. "Density Based Smart Traffic Control System Using Canny Edge Detection Algorithm for Congregating traffic information." international journal of engineering technology and management sciences 7, no. 5 (2023): 37–43. http://dx.doi.org/10.46647/ijetms.2023.v07i05.005.

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As the problem of urban traffic congestion intensifies, there is a pressing need for the introduction of advanced technology and equipment to improve the state-of-theart of traffic control. The current methods used such as timers or human control are proved to be inferior to alleviate this crisis. In this paper, a system to control the traffic by measuring the realtime vehicle density using canny edge detection with digital image processing is proposed. This imposing traffic control system offers significant improvement in response time, vehicle management, automation, reliability and overall

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Ganesh, Venkateshwaran, and C. Sujatha. "Ingenious Traffic Control System with Green Signal Timings Using Image Processing." Advanced Science, Engineering and Medicine 12, no. 3 (2020): 337–41. http://dx.doi.org/10.1166/asem.2020.2502.

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In metropolis, traffic congestion affects the daily routine of passengers and in the long run there will be a declination in productivity if such situation is left unaddressed. If an Ambulance, unfortunately, stuck in the middle of congested road, any delay can endanger the life of the patient and, such cases require intelligent, powerful and reliable traffic control system. In this paper, the Infra-Red (IR) Sensors keep track of vehicle density across the lane. The micro-controller in turn, generates the control signals to alter the traffic accordingly. During each transition phase, the Voice

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Ledalla, Sukanya, Vijendar Reddy Gurram, Gopala Krishna P, Saiteja Vodnala, Maroof Md, and Raviteja Reddy Annapuredddy. "Density based smart traffic control system using canny edge detection algorithm along with object detection." E3S Web of Conferences 391 (2023): 01061. http://dx.doi.org/10.1051/e3sconf/202339101061.

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It is urgently necessary to combine current advancements to work on the cutting edge inrush hour jam the executives, as urban congestion is one of the world’s biggest concerns. Existing methodologies, for example, traffic police and traffic lights are neither fulfilling nor viable. Consequently, a traffic management system that utilizes sophisticated edge detection and digital image processing to measure vehicle density in real time is developed in this setting. Computerizedimage processing should be used to detect edges. To extract significant traffic data from CCTV images, the edge recogniti

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Phand, Aniket, Shweta Bagade, Nikhil Bandgar, and Prof Ganesh Wayal. "Real-Time Traffic Light Optimization Using AI and IOT." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 3534–39. http://dx.doi.org/10.22214/ijraset.2024.60686.

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Abstract: Urban traffic congestion is a significant challenge globally, impacting transportation efficiency, environmental sustainability, and urban liveability. Traditional traffic control systems often struggle to adapt to changing traffic dynamics, leading to increased congestion and delays. This paper presents a novel traffic management system developed by our team, leveraging cutting-edge technologies such as computer vision, artificial intelligence (AI), and the Internet of Things (IoT). Deployed at intersections, our system utilizes real-time CCTV feeds for traffic analysis, employing a

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Thirupathi, D., Dasari Siddartha Reddy, Koppula Snehith, and Nuthanakanti Bhaskar. "Density Based Smart Traffic Control System Using Canny Edge Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 1024–31. http://dx.doi.org/10.22214/ijraset.2023.50271.

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Abstract: As the problem of traffic congestion intensifies, there is a pressing need for the introduction of advanced technology and equipment to improve state-of-the-art of traffic control. The current methods used such as timers or human control are proven to be inferior to alleviate this crisis. In this paper, a system to control the traffic by measuring the real-time vehicle density using canny edge detection with digital image processing is proposed. This imposing traffic control system offers significant improvement in response time, vehicle management, automation, reliability and overal

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P, Joyson Silva, Vignesh R, Sukumar Binu, and Kumar Nithish. "Adaptive Traffic Signal Timer for A Signal in Chennai Metropolitan City Using Python and OpenCV." Indian Journal of Science and Technology 16, no. 13 (2023): 1007–13. https://doi.org/10.17485/IJST/v16i13.2131.

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Abstract <strong>Background:</strong> The current traffic control system in India’s metropolises is ineffective because of the randomization of traffic density patterns throughout the day. For a predetermined period of time, the traffic signal timers switch traffic’s direction. Vehicles must therefore wait for a long time even when there is little traffic. <strong>Objectives:</strong> To continuously adjust the traffic signal timer based on the varying real-time traffic density and to significantly lessen traffic congestion. <strong>Methods:</strong> An ada

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Gopinaiak, R., B. Vijaya Chandra, B. Nagaraju, and K. V. Narayana. "A Survey on Settling Gridlock Utilizing Picture Handling Methods and Improvement of an Android Application." International Journal of Innovative Research in Engineering and Management 9, no. 4 (2022): 158–60. http://dx.doi.org/10.55524/ijirem.2022.9.4.30.

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Our current situation makes this a severe issue. Traffic congestion on both major and minor routes is constant. A strong traffic control system is necessary in order to address these problems. Human and material resources are lost as a result of poor traffic management. Due to these recurring issues, interest is growing in finding new ways to address the congestion issue. Inadequate capacity, unrestrained demand, and protracted delays caused by traffic signals are only a few of the factors that contribute to traffic congestion. Magnetic loop detectors, infrared sensors, wireless sensor network

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Richa, Gupta, Zafar Sherin, and Hussain Imran. "DESIGNING TRAFFIC MANAGEMENT SIMULATOR USING ARTIFICIAL INTELLIGENCE ALGORITHM." International Journal of Engineering Sciences & Emerging Technologies 11, no. 2 (2023): 171–76. https://doi.org/10.5281/zenodo.10441509.

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<em>Traffic congestion is a major problem in urban areas and has become a significant challenge for the transportation industry. Fuel consumption, air pollution and longer travel times are a result of the growth in the number of vehicles on the road. The traditional traffic management systems have become outdated and are not efficient in managing the traffic flow. A smart traffic control system that can instantly adjust to shifting traffic circumstances is required. The work presented here attempts to create a real-time, AI-based traffic control system that can recognise cars. Techniques relat

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Bhore, Abhishek. "Smart Traffic Light System." International Journal for Research in Applied Science and Engineering Technology 9, no. 10 (2021): 1904–9. http://dx.doi.org/10.22214/ijraset.2021.38292.

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Abstract: Wise city management, good road management is one of the most important things. Traffic congestion can be effectively controlled, provided that the number of vehicles passing through a traffic junction can be determined in advance. The proposed approach introduces a framework, which has the potential to continuously transmit traffic and generate alarms in the event of a major traffic congestion at the Sangli control station or in similar Indian cities. The number of vehicles passing through the area well before the required road junction can be estimated using the help of image proce

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Yousaf, Anosha Arooj, Najia Saher, Faisal Shahzad, and Sara Fareed. "A Framework to Reduce Road Congestion by means of Data Analytics." Review of Education, Administration & LAW 4, no. 1 (2021): 287–97. http://dx.doi.org/10.47067/real.v4i1.138.

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The density of vehicles on the road especially in urban areas keeps on increasing to large amount day by day. Especially during the peak hours of the day, large amount of people wastes much of their time in traffic signals. Not only they waste energy by burning excess fuel and releasing CO2 emissions in the environment as well as their time and money. An idea has been proposed to monitor the traffic congestion by means of data analytics on image data and solve the critical traffic congestion issue. The CCTV or surveillance cameras installed at the top points on the roads acts as a medium to pr

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V.Geetha, K. Gomathy C, Harshitha T., and Vijay Nagendra Varma P. "A Traffic Prediction for Intelligent Transportation System using Machine Learning." International Journal of Engineering and Advanced Technology (IJEAT) 10, no. 4 (2021): 166–68. https://doi.org/10.35940/ijeat.D2426.0410421.

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Traffic control has been an issue for a long time from the past. The modern world demands Technology. Now a days cars are one of the main methods of improvement in technology. Intelligent Traffic System is also known as Intelligent Transportation System apply communication and information technology to find the solution for the Traffic control issues. Intelligent Transportation System represents the main problem in transportation. ITS is a program .it is used to improve the efficiency of transportation through advanced technologies by using sensors and communication. Some of the problems like

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Whardana, Adithya Kusuma, and Parma Hadi Rentelinggi. "Deep Learning-Based Road Traffic Density Analysis and Monitoring Using Semantic Segmentation." JEECS (Journal of Electrical Engineering and Computer Sciences) 9, no. 1 (2024): 1–8. http://dx.doi.org/10.54732/jeecs.v9i1.1.

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Due to factors such as a growing population, more people using private vehicles, and outdated transportation infrastructure, Jakarta, the capital city of Indonesia, suffers from chronic traffic congestion. The environment, citizens' safety, productivity, and quality of life are all negatively impacted by these interruptions. In response to these difficulties, this study proposes a novel method for traffic monitoring. By combining YOLOv5, optical flow, and recurrent neural networks (RNN) with image processing and artificial neural networks, a unified traffic monitoring system can be achieved. W

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Swapna, A., and P. .Rajeswari. "Smart Traffic Management System Using Arduino and Sound Sensor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44455.

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Even today in some places the traffic drivers are still dependent on the traffic police to control the traffic based on the traffic density. Their had been a lot of research for controlling the traffic automatically like using image processing technique, IR sensors, laser sensors. These are cost efficient and are complex. So in order to avoid the traffic congestion we use Arduino and ultrasonic sensor which is simple and less cost. Ultrasonic sensor is a type of sensor, it measures the distance of an object with the help of transducer and receiver. Arduino is referred as a microcontroller. It

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Y., Himanth, V. Pavan Kumar B., and Lalitha Bhavani M. "Density Based Traffic Lightcontrol System using Raspberry Pi." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1281–85. https://doi.org/10.31142/ijtsrd23293.

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In present days, vehicular traffic is increasing throughout the world, especially in urban areas. As the number of road user's increase constantly a smart traffic control will become a very important issue in the future. Congestion in traffic is becoming a serious issue. Many vehicles are waiting at the signal for a long time due to this the time consumption is more for the human and there is a lot of problem for the people who go to their work and some to the business works. In existing system the traffic control is not according to the density, and is not reducing the effect of traffic i

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Mohamad, Norsyafiq Iman Norahim, Nizam Tahar Khairul, Raja Maharjan Gyanu, and C. Matos Jose. "Reconstructing 3D model of accident scene using drone image processing." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4087–100. https://doi.org/10.11591/ijece.v13i4.pp4087-4100.

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At the current stage, an investigation technique on the accident takes a longer time and this causes longer traffic congestion. The aim of this study is to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). The flight parameters that have been chosen are the circular method, the double grid method, and the single grid method. All these designs can produce a good 3D model to achieve the study’s objective. The methodology in this study is divided into 4 phases which are preliminary work, data acquisition, data processing, and data analysis. The main results

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Adhav, Hrutunjay Balasabeb, Sai Pramod Jadhav, Satyam Kamleshkumar Yadav, and Prof S. N. Bhadane. "Reduction of Ambulance Response Time and Accident Detection using IP and CPM." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (2022): 1757–61. http://dx.doi.org/10.22214/ijraset.2022.48190.

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Abstract: The usage of vehicles is rapidly increasing due to recent technological and economic development, and at the same time, the lack of infrastructure against the demand is leading to an increasing number of accidents and fatality of life. The trivial issues in our life system motivated us to come up with an application to automate this process and save lives. With a review of literature and brainstorming, I proposed the project on a smart traffic management system using image processing. The objective of this project is to develop a detect ambulance using image processing and machine le

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Venkatapathi, Pallam, Kondu Vinay, Kummathi Harshavardhan Reddy, Mulli Karthik, and Dr Sudhakar Alluri. "Traffic Signal Controller System and Vehicle Count-Based Road Congestion Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 4 (2024): 183–87. http://dx.doi.org/10.22214/ijraset.2024.59622.

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Abstract: Traffic congestion is one of the major modern-day crisis in every big city in the world. Recent study of World Bank has shown that average vehicle speed has been reduced from 21 km to 7 km per hour in the last 10 years in Dhaka. Inter metropolitan area studies suggest that traffic congestion reduces regional competitiveness and redistributes economic activity by slowing growth in county gross output or slowing metropolitan area employment growth. As more and more vehicles are commissioning in an already congested traffic system, there is an urgent need for a whole new traffic control

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Mohammad, Aleem, Thanmai Gaddami, Panduranga Reddy Sarikonda, et al. "Automated Traffic Density Optimization System." MATEC Web of Conferences 392 (2024): 01056. http://dx.doi.org/10.1051/matecconf/202439201056.

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Nowadays, there is an increase in vehicle traffic everywhere, particularly in cities. In the future, intelligent traffic control will become a critical issue as the number of road users continues to rise. Traffic congestion is starting to become a major problem. Many cars are stuck at traffic signals for extended periods of time, which increases human time consumption and causes numerous issues for those who commute to work and for business purposes. The current system does not regulate traffic in accordance with density and does not lessen the impact of traffic in urban areas. Nowadays, there

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Norahim, Mohamad Norsyafiq Iman, Khairul Nizam Tahar, Gyanu Raja Maharjan, and Jose C. Matos. "Reconstructing 3D model of accident scene using drone image processing." International Journal of Electrical and Computer Engineering (IJECE) 13, no. 4 (2023): 4087. http://dx.doi.org/10.11591/ijece.v13i4.pp4087-4100.

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At the current stage, an investigation technique on the accident takes a longer time and this causes longer traffic congestion. The aim of this study is to reconstruct a 3D model of an accident scene using an unmanned aerial vehicle (UAV). The flight parameters that have been chosen are the circular method, the double grid method, and the single grid method. All these designs can produce a good 3D model to achieve the study’s objective. The methodology in this study is divided into 4 phases which are preliminary work, data acquisition, data processing, and data analysis. The main results of th

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Silas, Abasiama I., Faith M. Jonathan, and Otuekong E. Ekong. "Traffic Management System for Prioritizing Emergency Over Time and Density." Advances in Multidisciplinary and scientific Research Journal Publication 29 (December 15, 2021): 23–36. http://dx.doi.org/10.22624/aims/abmic2021-v2-p3.

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An analysis of the current traffic management system in Uyo, an urban city in Nigeria showed a heavy dependence on the use of traffic wardens. Where traffic control lights operate, they are based on fixed time allocation which often results in traffic congestion on the more operational lane and hinders the rescue work of first responders. This study proposes a traffic management system (TMS-ETD) that prioritizes the passage of Emergency vehicles over the lane with the longest waiting Time, and lastly the lane with highest traffic Density. The Dynamic Systems Development Method (DSDM) guided th

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Dharamjit, Yadav Dheeraj Patil Kishan Panchal Vaibhav Patil Prof. John Kenny. "REAL TIME TRAFFIC LIGHT CONTROLLER." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 6, no. 4 (2017): 37–41. https://doi.org/10.5281/zenodo.495156.

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There is enormous increase in population of city and number of vehicles on the road day by day. With this increasing urban population and the number of vehicles, need to control streets, highways and roads become major issue. The main issue behind today’s traffic problem is the techniques that are used for traffic management. Today’s traffic management system has no prominence on live traffic scenario, which leads to ineffective traffic management systems. These traffic timers just have the preset time. This is similar to using open loop system. The proposed system incorporate a closed loop sy

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A. Marzouk, Marwa, and Amr Abd El Azeem. "Vehicles detection and counting based on internet of things technology and video processing techniques." IAES International Journal of Artificial Intelligence (IJ-AI) 11, no. 2 (2022): 405. http://dx.doi.org/10.11591/ijai.v11.i2.pp405-413.

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Recent studies have proven that vehicle tracking and detection play an important role in traffic density monitoring. Traffic overcrowding can be effectively controlled if the number of vehicles expected to pass through a congested intersection can be predicted ahead of time. To overcome such impact of traffic congestion the proposed system presents a framework, using motion detection algorithms and “ThingSpeak” internet of things (IoT) platform which is used in to calculate traffic density, the proposed system capturing video with wireless internet protocol (IP) cameras and broadcasting it to

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ENEH, Joy, George ADEJO, and Adebimpe ESAN. "Implementation of an Intelligent Traffic Light Control System Using Object-Counting Technique for Traffic Detection and Control." International Journal of Research and Innovation in Applied Science X, no. V (2025): 798–807. https://doi.org/10.51584/ijrias.2025.100500073.

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The application of object counting for a real-time traffic control system is presented in this work. The system, which uses computer vision techniques to detect and count automobiles at junctions, was created and implemented using a Raspberry Pi 400 using COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset widely used in computer vision research. It provides images, bounding boxes, segmentation masks, key points, and captions for real-world objects. This method drastically cuts down on needless waiting times by dynamically adjusting

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Asif, Muhammad, Tabarka Rajab, Samreen Hussain, et al. "Performance Evaluation of Deep Learning Algorithm Using High-End Media Processing Board in Real-Time Environment." Journal of Sensors 2022 (December 7, 2022): 1–13. http://dx.doi.org/10.1155/2022/6335118.

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Image processing-based artificial intelligence algorithm is a critical task, and the implementation requires a careful examination for the selection of the algorithm and the processing unit. With the advancement of technology, researchers have developed many algorithms to achieve high accuracy at minimum processing requirements. On the other hand, cost-effective high-end graphical processing units (GPUs) are now available to handle complex processing tasks. However, the optimum configurations of the various deep learning algorithms implemented on GPUs are yet to be investigated. In this propos

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Guzman, Javier Garau, and Victor Monzon Baeza. "Enhancing Urban Mobility through Traffic Management with UAVs and VLC Technologies." Drones 8, no. 1 (2023): 7. http://dx.doi.org/10.3390/drones8010007.

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This paper introduces a groundbreaking approach to transform urban mobility by integrating Unmanned Aerial Vehicles (UAVs) and Visible Light Communication (VLC) technologies into traffic management systems within smart cities. With the continued growth of urban populations, the escalating traffic density in large cities poses significant challenges to the daily mobility of citizens, rendering traditional ground-based traffic management methods increasingly inadequate. In this context, UAVs provide a distinctive perspective for real-time traffic monitoring and congestion detection using the YOL

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Wakkumbura, Randi Thakshila, Budditha Hettige, and Ananda Edirisuriya. "Real-Time Traffic Controlling System Using Multi-Agent Technology." Journal Européen des Systèmes Automatisés 54, no. 4 (2021): 633–40. http://dx.doi.org/10.18280/jesa.540413.

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With the development, almost all the sectors, countries tend to grow adapting to latest technologies. The transport sector also has a huge impact on this development sphere. When it comes to traffic, it is a huge problem in the world. In Sri Lanka traffic is a problem that exists for a long period. Annually there is a loss of Rs. 400 billion due to traffic congestions. Over the past years various solutions have been proposed using different methods for traffic control. These solutions are based on different trending technologies such as machine learning, image processing, fuzzy logic and the I

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NAIDU, T. D. V. A. "Automatic License Plate Recognition." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44494.

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Automatic license plate recognition using Image processing offer students a unique opportunity to gain hands-on experience in designing and optimizing the Embedded system. This typically involve working with industry professionals on actual projects, providing interns with valuable exposure to real-world challenges and best practices in the field of Embedded system. During the project, participants are often tasked with designing using specialized software tools like MATLAB. In recent times, the number of vehicles on road has exponentially risen due to which traffic congestion and violations a

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Zheng, Yanliu, Juan Luo, Ying Qiao, and Han Gao. "UAV-Assisted Traffic Speed Prediction via Gray Relational Analysis and Deep Learning." Drones 7, no. 6 (2023): 372. http://dx.doi.org/10.3390/drones7060372.

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Accurate traffic prediction is crucial to alleviating traffic congestion in cities. Existing physical sensor-based traffic data acquisition methods have high transmission costs, serious traffic information redundancy, and large calculation volumes for spatiotemporal data processing, thus making it difficult to ensure accuracy and real-time traffic prediction. With the increasing resolution of UAV imagery, the use of unmanned aerial vehicles (UAV) imagery to obtain traffic information has become a hot spot. Still, analyzing and predicting traffic status after extracting traffic information is n

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Humayun, Mamoona, Farzeen Ashfaq, Noor Zaman Jhanjhi, and Marwah Khalid Alsadun. "Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network." Electronics 11, no. 17 (2022): 2748. http://dx.doi.org/10.3390/electronics11172748.

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Detecting and counting on road vehicles is a key task in intelligent transport management and surveillance systems. The applicability lies both in urban and highway traffic monitoring and control, particularly in difficult weather and traffic conditions. In the past, the task has been performed through data acquired from sensors and conventional image processing toolbox. However, with the advent of emerging deep learning based smart computer vision systems the task has become computationally efficient and reliable. The data acquired from road mounted surveillance cameras can be used to train m

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Krish Saurabh Mehta, Kush Nihar Raj, and Keyur Nayankumar Brahmbhatt. "Machine learning solutions for adaptive traffic signal control: A review of image-based approaches." World Journal of Advanced Engineering Technology and Sciences 13, no. 1 (2024): 476–81. http://dx.doi.org/10.30574/wjaets.2024.13.1.0437.

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This paper gives an overview and performance evaluation of various machine learning models implemented in management of urban traffic congestions, more specifically, adaptive traffic signal control systems. It considers a review of deep learning algorithms including R- CNN, Fast R-CNN, Faster R-CNN, SSD, YOLO v4, and YOLOv8, with regard to their efficiencies for vehicle detection and traffic prediction under varying scenarios. Certain traffic conditions, camera placements, and environmental factors—related performance for each of the models are discussed. The major performance in most of the s

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