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Relevant bibliographies by topics / Traffic congestion control using Image Processing

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Academic literature on the topic 'Traffic congestion control using Image Processing'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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Journal articles on the topic "Traffic congestion control using Image Processing"

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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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Book chapters on the topic "Traffic congestion control using Image Processing"

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Baroni, Rafael, Sthefanie Premebida, Marcella Martins, Diego Oliva, Erikson Freitas de Morais, and Max Santos. "Traffic Control Using Image Processing and Deep Learning Techniques." In Metaheuristics in Machine Learning: Theory and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70542-8_14.

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Bhardwaj, Vedansh, Yaswanth Rasamsetti, and Vipina Valsan. "Traffic Control System for Smart City Using Image Processing." In AI and IoT for Smart City Applications. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-7498-3_6.

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Mohanty, Anita, Bhagyalaxmi Jena, and Subrat Kumar Mohanty. "Real Time Density–Based Traffic Congestion Detection System Using Image Processing and Fuzzy Logic Controller." In Advanced Sensing in Image Processing and IoT. CRC Press, 2022. http://dx.doi.org/10.1201/9781003221333-9.

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Srivastava, Sandesh Kumar, Anshul Singh, Ruqaiya Khanam, Prashant Johri, Arya Siddhartha Gupta, and Gaurav Kumar. "Smart Traffic Control for Emergency Vehicles Using the Internet of Things and Image Processing." In Trends and Advancements of Image Processing and Its Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75945-2_3.

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Rafiq, Shuja, Mohammadi Akheela Khanum, and Faiyaz Ahamad. "Minimization of Ambulance Response Time Using Image Processing and Critical Path Mapping Based on Traffic Control." In Computer Vision and Robotics. Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8225-4_23.

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Mohanty, Anita, Subrat Kumar Mohanty, and Jitesh Kumar. "A Prototype of Density-Based Intelligent Traffic Light Control System Using Image Processing Technique and Arduino Microcontroller in Lab VIEW Environment." In Advances in Electrical Control and Signal Systems. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5262-5_56.

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Majumder, Prakash, and Saurabh Chaudhury. "Vehicle Detection and Counting the Number of Vehicles for Intelligent Traffic Control Using LabVIEW—An Image Processing Approach." In Lecture Notes in Electrical Engineering. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5089-8_49.

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Rout, Jigyansha Jeevan, Aruna Tripathy, and Ananya Dastidar. "Network Congestion Control Using Deep Convolutional Neural Networks." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8602-3.ch010.

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Network congestion is getting more and more severe day by day with the deployment of long-term evaluation (LTE) and 5G as more users get added to the networks. The issue gets even more severe with the rapid growth of e-commerce, online banking and entertainment platforms like Netflix, Amazon Prime, YouTube that generate a huge amount of traffic worldwide. In this chapter, the authors address this traffic congestion issue with a hand-held solution. Here they proposed a method that can be used for network congestion control. Simulation studies show the performance of the proposed method that wor

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M., SureshKumar, and Anu Valliammai R. "Intelligent Traffic Signal Monitoring System Using Image Processing." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6721-0.ch008.

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This project aims at making an intelligent traffic signal monitoring system that makes decisions based on real-time traffic situations. The choices will be such that the traditional red, green, or amber lighting scheme is focused on the actual number of cars on the road and the arrival of emergency services rather than using pure timing circuits to control car traffic by using what the traffic appears like via smart cameras to capture real-time traffic movement pictures of each direction. The control system will modify the traffic light control parameters dynamically in various directions due

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Pandey, Kavita, Akshansh Narula, Dhiraj Pandey, and Ram Shringar Raw. "An Approach Towards Intelligent Traffic Environment Using Machine Learning Algorithms." In Cloud-Based Big Data Analytics in Vehicular Ad-Hoc Networks. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-2764-1.ch001.

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To make an optimal movement of vehicles and to reduce the accident rate, the government has installed traffic lights at almost every intersection. Traffic lights are intended to decrease congestion. However, the dynamic nature of traffic movement causes congestion always. This congestion leads to increased waiting times for every vehicle. In this chapter, two machine learning-based approaches used to improve in the congested traffic environment. The first part of the work is Deep-Learning based traffic signaling, which identifies the congestion on all sides of the intersection with the help of

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Conference papers on the topic "Traffic congestion control using Image Processing"

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Sivakumar, Ashmitha Jaysi, L. Pragati, Sambhavi Roy, and Lelitha Devi Vanajakshi. "Automated Detection of Roadway and Traffic Control Conditions Using On-Board Image Processing." In 2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS). IEEE, 2025. https://doi.org/10.1109/comsnets63942.2025.10885612.

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Qiu, Yujia. "Real-time object detection of traffic signs using SSDNet: a case study in Shanghai." In 4th International Conference on Image Processing and Intelligent Control (IPIC 2024), edited by Kelin Du and Azlan bin Mohd Zain. SPIE, 2024. http://dx.doi.org/10.1117/12.3038824.

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Sadrisjain B, V., B. Anushree T, and N. Manohar. "Traffic Congestion Management based on Vehicle Density Using Image Processing Techniques." In 2022 International Conference on Futuristic Technologies (INCOFT). IEEE, 2022. http://dx.doi.org/10.1109/incoft55651.2022.10094495.

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Bouzar, S. "Traffic measurement: image processing using road markings." In Eighth International Conference on Road Traffic Monitoring and Control. IEE, 1996. http://dx.doi.org/10.1049/cp:19960300.

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Prakash, Uthara E., K. T. Vishnupriya, Athira Thankappan, and Arun A. Balakrishnan. "Density Based Traffic Control System Using Image Processing." In 2018 International Conference on Emerging Trends and Innovations In Engineering And Technological Research (ICETIETR). IEEE, 2018. http://dx.doi.org/10.1109/icetietr.2018.8529111.

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Banu, Asha S. M., Soundarya A. S. Lakchida, V. S. Shanthini, and S. L. Stinsha. "Smart Traffic Light Control System Using Image Processing." In 2022 International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2022. http://dx.doi.org/10.1109/icaiss55157.2022.10010764.

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Umair, Muhammad, Hanzala Nadeem, and Junaid Mir. "Density Control Smart Traffic Signal Using Image Processing." In 2024 International Conference on Engineering & Computing Technologies (ICECT). IEEE, 2024. http://dx.doi.org/10.1109/icect61618.2024.10581232.

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Yin, J. H. "Incident detection in pedestrian traffic using image processing." In Eighth International Conference on Road Traffic Monitoring and Control. IEE, 1996. http://dx.doi.org/10.1049/cp:19960302.

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Frank, Anilloy, Yasser Salim Khamis Al Aamri, and Amer Zayegh. "IoT based Smart Traffic density Control using Image Processing." In 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC). IEEE, 2019. http://dx.doi.org/10.1109/icbdsc.2019.8645568.

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Khushi. "Smart Control of Traffic Light System using Image Processing." In 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). IEEE, 2017. http://dx.doi.org/10.1109/ctceec.2017.8454966.

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