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Journal articles on the topic 'TRAFFIC CONGESTION DETECTION'

Author: Grafiati

Published: 11 September 2023

Last updated: 25 July 2025

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1

Jian, Cheng, Chenxi Lin, Xiaojian Hu, and Jian Lu. "Selective Scale-Aware Network for Traffic Density Estimation and Congestion Detection in ITS." Sensors 25, no. 3 (2025): 766. https://doi.org/10.3390/s25030766.

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Traffic congestion detection in surveillance video is crucial for road traffic condition monitoring and improving traffic operation efficiency. Currently, traffic congestion is often characterized through traffic density, which is obtained by detecting vehicles or using holistic mapping methods. However, these traditional methods are not effective in dealing with the vehicle scale variation in surveillance video. This prompts us to explore density-map-based traffic density detection methods. Considering the dynamic characteristics of traffic flow, relying solely on the spatial feature of traff

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2

S, Sneha, Sriranjini S, Himasai T, and Balaji M. "IoT Based Traffic Congestion Management and Accident Detection System." Journal of Electrical Engineering and Automation 6, no. 1 (2024): 63–71. http://dx.doi.org/10.36548/jeea.2024.1.005.

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This review proposes a traffic congestion management and accident detection system to reduce congestion at junctions and to provide emergency assistance during accidents. The proposed system employs advanced computer vision and image processing techniques like You Only Look Once (YOLO) to monitor and analyze real-time traffic conditions and accidents. The pivotal feature of this system lies in its adaptive decision-making capability, automatically adjusting traffic signal timings based on observed density patterns and updating and reporting about the congestions and accidents for which Interne

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Wiseman, Yair. "Computerized Traffic Congestion Detection System." International Journal of Transportation and Logistics Management 1, no. 1 (2017): 1–8. http://dx.doi.org/10.21742/ijtlm.2017.1.1.01.

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4

Sternford, Mavuchi, Magadza Tirivangani, and Chikoore Racheal. "Deep Learning for Traffic Congestion Detection: A Survey Paper." International Journal of Innovative Science and Research Technology (IJISRT) 8, no. 11 (2024): 5. https://doi.org/10.5281/zenodo.10877599.

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Traffic congestion is a major problem in urban areas, leading to increased travel time, economic losses, and environmental pollution. By analyzing traffic data from traffic cameras, we can detect and predict traffic congestion with high accuracy. In this survey, we explore the use of deep learning techniques for traffic congestion detection. Deep learning models, such as convolutional neural networks and recurrent neural networks, have shown promising results in traffic congestion detection. We also discuss the challenges and future directions of this field, including the need for high-quality

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Nurshahrily, Idura Ramli, and Izani Mohamed Rawi Mohd. "An overview of traffic congestion detection and classification techniques in VANET." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 437–44. https://doi.org/10.11591/ijeecs.v20.i1.pp437-444.

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Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which presents variou

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Indra Bayu Pangestu, Maimunah Maimunah, and Mukhtar Hanafi. "Traffic Congestion Detection Using YOLOv8 Algorithm With CCTV Data." PIKSEL : Penelitian Ilmu Komputer Sistem Embedded and Logic 12, no. 2 (2024): 435–44. http://dx.doi.org/10.33558/piksel.v12i2.9953.

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Community development and growth according to data from the Central Java Statistics Agency regarding the number of vehicles in Central Java Province in 2021 is 20 320 743. The increasing growth of society has caused vehicle density which is a serious problem in urban areas. This study developed a congestion detection system using the YOLOv8 algorithm to analyze traffic density from CCTV footage. Automated detection of traffic congestion is a critical challenge in urban transport management. YOLOv8, a fast and accurate object detection algorithm, is used to identify vehicles and count their num

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Idura Ramli, Nurshahrily, and Mohd Izani Mohamed Rawi. "An overview of traffic congestion detection and classification techniques in VANET." Indonesian Journal of Electrical Engineering and Computer Science 20, no. 1 (2020): 437. http://dx.doi.org/10.11591/ijeecs.v20.i1.pp437-444.

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<span>Vehicular traffic congestion has been and still is a major problem for many countries and knowledge about the traffic condition is important in order to schedule, plan and avoid traffic congestion. With recent development in technology, various efforts and methods are proposed in mitigating traffic congestion. Vehicular Ad-hoc NETwork (VANET) is very much in the hype in addressing this issue due to its capabilities and adaptation to scalability, highly dynamic topology as well as cooperative communication. A popular focus is in detecting and classisying traffic congestion which pre

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Xiang, Yingxiao, Wenjia Niu, Endong Tong, et al. "Congestion Attack Detection in Intelligent Traffic Signal System: Combining Empirical and Analytical Methods." Security and Communication Networks 2021 (October 31, 2021): 1–17. http://dx.doi.org/10.1155/2021/1632825.

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The intelligent traffic signal (I-SIG) system aims to perform automatic and optimal signal control based on traffic situation awareness by leveraging connected vehicle (CV) technology. However, the current signal control algorithm is highly vulnerable to CV data spoofing attacks. These vulnerabilities can be exploited to create congestion in an intersection and even trigger a cascade failure in the traffic network. To avoid this issue, timely and accurate congestion attack detection and identification are essential. This work proposes a congestion attack detection approach by combining empiric

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9

Wang, Chao. "An Effective Congestion Control Algorithm based on Traffic Assignment and Reassignment in Wireless Sensor Network." Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering) 13, no. 8 (2020): 1166–74. http://dx.doi.org/10.2174/2352096513999200628095848.

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Background: It is important to improve the quality of service by using congestion detection technology to find the potential congestion as early as possible in wireless sensor network. Methods: So an improved congestion control scheme based on traffic assignment and reassignment algorithm is proposed for congestion avoidance, detection and mitigation. The congestion area of the network is detected by predicting and setting threshold. When the congestion occurs, sensor nodes can be recovery quickly from congestion by adopting reasonable method of traffic reassignment. And the method can ensure

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10

Jiang, Shan, Yuming Feng, Wei Zhang, Xiaofeng Liao, Xiangguang Dai, and Babatunde Oluwaseun Onasanya. "A New Multi-Branch Convolutional Neural Network and Feature Map Extraction Method for Traffic Congestion Detection." Sensors 24, no. 13 (2024): 4272. http://dx.doi.org/10.3390/s24134272.

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With the continuous advancement of the economy and technology, the number of cars continues to increase, and the traffic congestion problem on some key roads is becoming increasingly serious. This paper proposes a new vehicle information feature map (VIFM) method and a multi-branch convolutional neural network (MBCNN) model and applies it to the problem of traffic congestion detection based on camera image data. The aim of this study is to build a deep learning model with traffic images as input and congestion detection results as output. It aims to provide a new method for automatic detection

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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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Fazekas, Zoltán, Mohammed Obaid, Lamia Karim, and Péter Gáspár. "Urban Traffic Congestion Alleviation Relying on the Vehicles’ On-board Traffic Congestion Detection Capabilities." Acta Polytechnica Hungarica 21, no. 6 (2024): 7–31. http://dx.doi.org/10.12700/aph.21.6.2024.6.1.

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13

Kayarga, Tanuja, and H. M. Navyashree. "A Novel Framework to Control and Optimize the Traffic Congestion Issue in VANET." International Journal of Engineering & Technology 7, no. 2.31 (2018): 245. http://dx.doi.org/10.14419/ijet.v7i3.31.18234.

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In the recent times due to the increase of vehicular nodes in a vehicular communication network, there is a need of developing efficient systems in order to optimize the vehicular traffic congestion issues in urban areas. The current research trends shows that most of the conventional studies focused on developing fuzzy inference systems based vehicular traffic congestion model which has gained lots of attention on detecting and minimizing the congestion levels.We have proposed a new approach towards detection and controlling of traffic congestion in VANET. The proposed system utilizes the com

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14

Khan, Zahid, Anis Koubaa, and Haleem Farman. "Smart Route: Internet-of-Vehicles (IoV)-Based Congestion Detection and Avoidance (IoV-Based CDA) Using Rerouting Planning." Applied Sciences 10, no. 13 (2020): 4541. http://dx.doi.org/10.3390/app10134541.

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Massive traffic jam is the top concern of multiple disciplines (Civil Engineering, Intelligent Transportation Systems (ITS), and Government Policy) presently. Although literature constitutes several IoT-based congestion detection schemes, the existing schemes are costly (money and time) and, as well as challenging to deploy due to its complex structure. In the same context, this paper proposes a smart route Internet-of-Vehicles (IoV)-based congestion detection and avoidance (IoV-based CDA) scheme for a particular area of interest (AOI), i.e., road intersection point. The proposed scheme has tw

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15

Zhang, Xue Li. "Path Reconstruction of Intelligent Traffic Based on Positive Feedback System." Applied Mechanics and Materials 513-517 (February 2014): 3160–64. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3160.

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Traffic congestion are prevalent in worldwide cities. The imbalance between demand and supply of urban traffic is the root cause of this problem. So taking effective measures to regulate traffic demand, and guiding the traffic problems of the supply and demand balance is the best way to solve traffic congestion. This paper improves the TDM measure, and combines with intelligent information platform for the design of a new urban transport demand management adaptability of dynamic traffic data analysis platform. The platform supported by the technology of wireless sensor communications, intellig

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16

Yang, Xinghai, Fengjiao Wang, Zhiquan Bai, Feifei Xun, Yulin Zhang, and Xiuyang Zhao. "Deep Learning-Based Congestion Detection at Urban Intersections." Sensors 21, no. 6 (2021): 2052. http://dx.doi.org/10.3390/s21062052.

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In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the ve

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Zaitouny, Ayham, Athanasios D. Fragkou, Thomas Stemler, et al. "Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan." Sensors 22, no. 8 (2022): 2933. http://dx.doi.org/10.3390/s22082933.

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Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver frustration. Traffic incidents are one of the six causes of non-recurrent congestion. Early and accurate detection helps reduce incident duration, but it remains a challenge due to the limitation of current sensor technologies. In this paper, we employ a recurrence-based technique, the Quadrant Scan, to analyse time series traffic volume data for incident detect

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18

Anbaroglu, B., B. Heydecker, and T. Cheng. "HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 159–64. http://dx.doi.org/10.5194/isprs-archives-xli-b2-159-2016.

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Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revi

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Anbaroglu, B., B. Heydecker, and T. Cheng. "HOW TRAVEL DEMAND AFFECTS DETECTION OF NON-RECURRENT TRAFFIC CONGESTION ON URBAN ROAD NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 159–64. http://dx.doi.org/10.5194/isprsarchives-xli-b2-159-2016.

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Occurrence of non-recurrent traffic congestion hinders the economic activity of a city, as travellers could miss appointments or be late for work or important meetings. Similarly, for shippers, unexpected delays may disrupt just-in-time delivery and manufacturing processes, which could lose them payment. Consequently, research on non-recurrent congestion detection on urban road networks has recently gained attention. By analysing large amounts of traffic data collected on a daily basis, traffic operation centres can improve their methods to detect non-recurrent congestion rapidly and then revi

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20

H S, Lokeshwari. "Density Based Traffic Management and Ambulance Detection Using RFID." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46680.

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Abstract-Traffic congestion is a major issue in many Indian and global cities due to signal failures, poor law enforcement, and ineffective traffic management. It negatively impacts the economy, environment, and quality of life. This project proposes a smart traffic management system using the Internet of Things and decentralized algorithms to optimize traffic flow. It predicts traffic density to reduce congestion and prioritizes emergency vehicles by turning red lights green along their route. By improving traffic control and reducing delays, especially in emergencies, the system enhances saf

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21

El-Sersy, Heba, and Ayman El-Sayed. "Survey of Traffic Congestion Detection using VANET." Communications on Applied Electronics 1, no. 4 (2015): 14–20. http://dx.doi.org/10.5120/cae-1520.

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22

Cherkaoui, Badreddine, Abderrahim Beni-Hssane, Mohamed El Fissaoui, and Mohammed Erritali. "Road traffic congestion detection in VANET networks." Procedia Computer Science 151 (2019): 1158–63. http://dx.doi.org/10.1016/j.procs.2019.04.165.

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23

Kalinic, Maja, and Jukka M. Krisp. "Fuzzy inference approach in traffic congestion detection." Annals of GIS 25, no. 4 (2019): 329–36. http://dx.doi.org/10.1080/19475683.2019.1675760.

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24

Bhanja, Urmila, Anita Mohanty, and Bhagyashree Das. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.094976.

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Mohanty, Anita, Bhagyashree Das, and Urmila Bhanja. "Embedded based real time traffic congestion detection." International Journal of Vehicle Information and Communication Systems 3, no. 4 (2018): 267. http://dx.doi.org/10.1504/ijvics.2018.10016393.

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26

Es Swidi, A., S. Ardchir, A. Daif, and M. Azouazi. "Road users detection for traffic congestion classification." Mathematical Modeling and Computing 10, no. 2 (2023): 518–23. http://dx.doi.org/10.23939/mmc2023.02.518.

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One of the important problems that urban residents suffer from is Traffic Congestion. It makes their life more stressful, it impacts several sides including the economy: by wasting time, fuel and productivity. Moreover, the psychological and physical health. That makes road authorities required to find solutions for reducing traffic congestion and guaranteeing security and safety on roads. To this end, detecting road users in real-time allows for providing features and information about specific road points. These last are useful for road managers and also for road users about congested points

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Wang, Chishe, Yuting Chen, Jie Wang, and Jinjin Qian. "An Improved CrowdDet Algorithm for Traffic Congestion Detection in Expressway Scenarios." Applied Sciences 13, no. 12 (2023): 7174. http://dx.doi.org/10.3390/app13127174.

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Traffic congestion detection based on vehicle detection and tracking algorithms is one of the key technologies for intelligent transportation systems. However, in expressway surveillance scenarios, small vehicle size and vehicle occlusion present severe challenges for this method, including low vehicle detection accuracy and low traffic congestion detection accuracy. To address these challenges, this paper proposes an improved version of the CrowdDet algorithm by introducing the Involution operator and bi-directional feature pyramid network (BiFPN) module, which is called IBCDet. The proposed

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Wang, Wan-Xiang, Rui-Jun Guo, and Jing Yu. "Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example." Advances in Mechanical Engineering 10, no. 6 (2018): 168781401878148. http://dx.doi.org/10.1177/1687814018781482.

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Traffic congestion index reflects the state of traffic flow. The detection and analysis on traffic congestion index can be used to estimate the operation status of roads, to plan and organize road traffic for traffic managers, and to make the reasonable decisions of travelers to travel. The traffic conditions of several evaluation indexes were analyzed. Based on the theory of fuzzy mathematics, some membership functions of the evaluating indexes were designed. Three calculation methods of traffic congestion index were proposed. Their calculation results were compared mutually. The conclusion r

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Ge, Ling Yan, and Bi Feng Zhu. "Analysis and Optimization of Hangzhou East Area Traffic Based on the Congestion Index Detection Platform." Advanced Materials Research 1030-1032 (September 2014): 2182–86. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.2182.

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With the rapid development of urbanization in China and the motorization’s fast pace of high speed as well as the national automobile industry process, many cities in our country have been facing a huge problem - traffic congestion in recent years. And the essence of the problem is the imbalance between road traffic supply and traffic demand in the process of urban development. Aimed at the problem of traffic congestion, this paper based on Hangzhou city’s traffic congestion index of monitoring data from testing platform and statistical data from field survey , studied the Hangzhou east area o

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Balasubramanian, Saravana Balaji, Prasanalakshmi Balaji, Asmaa Munshi, et al. "Machine learning based IoT system for secure traffic management and accident detection in smart cities." PeerJ Computer Science 9 (March 8, 2023): e1259. http://dx.doi.org/10.7717/peerj-cs.1259.

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In smart cities, the fast increase in automobiles has caused congestion, pollution, and disruptions in the transportation of commodities. Each year, there are more fatalities and cases of permanent impairment due to everyday road accidents. To control traffic congestion, provide secure data transmission also detecting accidents the IoT-based Traffic Management System is used. To identify, gather, and send data, autonomous cars, and intelligent gadgets are equipped with an IoT-based ITM system with a group of sensors. The transport system is being improved via machine learning. In this work, an

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Pillai, Arjun, Kajal Chourasia, and Bhavya Agarwal. "Neural Network Based Traffic Monitoring using UAVs." International Journal of Engineering and Advanced Technology 8, no. 4s2 (2020): 45–50. http://dx.doi.org/10.35940/ijeat.d1003.0484s219.

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In large and growing metropolitan areas, the rise in traffic congestion is becoming an inescapable problem. It is estimated that the traffic congestion in metro cities costs the nation approximately 1.5 lakh crore rupees every year. With the increase in congestion, accident rate increases proportionally. The reckless driving and increased speed are the root cause of road accidents. We propose a speed detection algorithm to detect and monitor the speed of vehicles crossing a certain threshold speed limit. On national highways, the long queues at toll booths lead to loss of time and money. We pr

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Mohanty, Anita, Sudipta Mahapatra, and Urmila Bhanja. "Traffic congestion detection in a city using clustering techniques in VANETs." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 884. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp884-891.

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<p>Road traffic congestion, a serious illness in developing regions, is one of the biggest problems in our day-to-day life, resulting in delays, wastage of fuel and money. In this paper, a new model is developed using Simulation of Urban Mobility (SUMO) simulator for simulating a realistic traffic scenario for a large city like Bhubaneswar where, traffic congestion is a critical issue. In a city, traffic congestion is characterised by many parameters such as rapid growth of population, number of four wheelers, inadequate and poor road infrastructures and shortage of physical plan to gove

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Anita, Mohanty, Mahapatra Sudipta, and Bhanja Urmila. "Traffic congestion detection in a city using clustering techniques in VANETs." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 884–91. https://doi.org/10.11591/ijeecs.v13.i3.pp884-891.

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Road traffic congestion, a serious illness in developing regions, is one of the biggest problems in our day-to-day life, resulting in delays, wastage of fuel and money. In this paper, a new model is developed using Simulation of Urban Mobility (SUMO) simulator for simulating a realistic traffic scenario for a large city like Bhubaneswar where, traffic congestion is a critical issue. In a city, traffic congestion is characterised by many parameters such as rapid growth of population, number of four wheelers, inadequate and poor road infrastructures and shortage of physical plan to govern the de

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Fahrezi, Marchel Maulana, and Eka Angga Laksana. "TRAFFIC FLOW AND CONGESTION DETECTION WITH YOLOV8 AND BYTETRACK-BASED MULTI OBJECT TRACKING." Jurnal Teknik Informatika (Jutif) 5, no. 4 (2024): 253–61. https://doi.org/10.52436/1.jutif.2024.5.4.2063.

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The rapid urbanization witnessed in cities like Bandung, Indonesia, has emerged as a pressing issue, precipitating severe traffic congestion that poses challenges to economic growth and diminishes overall quality of life. This study endeavors to confront these multifaceted challenges through the development of a sophisticated real-time traffic surveillance and control system. The proposed system utilizes the current CCTV infrastructure in the city and incorporates advanced technologies like YOLOv8 for accurate vehicle detection and ByteTrack for dynamic real-time vehicle tracking. This system

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Xu, Ling, Qun Ba, and Shan Hu. "Reserch on Traffic Congestion Detection Using Realtime Video." Applied Mechanics and Materials 241-244 (December 2012): 2100–2106. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.2100.

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To detection the realtime information of the traffic congestion on the road, a method based on realtime video analysis was present. The method, firstly figure out the density of the vehicles on the lane, and then calculates optical flow velocity vetors of corner points on vehicles, finnaly, judges the current condition of the traffic flow by fuzzy logic based on the conditions of denisty and velocity. The proposed method is capable to accurately and timely detect the status of traffic congestion.

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Peng, Ming Long, Xin Rong Liang, Chao Jun Dong, and Yan Yan Liu. "Freeway Traffic Congestion Identification Based on Fuzzy Logic Inference." Applied Mechanics and Materials 397-400 (September 2013): 2227–30. http://dx.doi.org/10.4028/www.scientific.net/amm.397-400.2227.

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Traffic congestion detection is the basis of dynamic traffic control and real time guidance. This study proposes a fuzzy logic based traffic congestion identification method. The components of a fuzzy logic inference are firstly formulated. According to such information as the speed and occupancy of freeway traffic flow, and the weather conditions on the freeway, a congestion identification method based on fuzzy logic inference is then designed. Gauss curves are assumed for the membership functions of the input and output variables, and 45 fuzzy rules are also established. Finally, the congest

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Sheikh, Muhammad Sameer, Jun Liang, and Wensong Wang. "An Improved Automatic Traffic Incident Detection Technique Using a Vehicle to Infrastructure Communication." Journal of Advanced Transportation 2020 (January 13, 2020): 1–14. http://dx.doi.org/10.1155/2020/9139074.

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Traffic incident detection is one of the major research areas of intelligent transportation systems (ITSs). In recent years, many mega-cities suffer from heavy traffic flow and congestion. Therefore, monitoring traffic scenarios is a challenging issue due to the nature and the characteristics of a traffic incident. Reliable detection of traffic incidents and congestions provide useful information for enhancing traffic safety and indicate the characteristics of traffic incidents, traffic violation, driving pattern, etc. This paper investigates the estimation of traffic incident from a hybrid ob

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Liudmyla, Abramova, Shyrin Valerii, Ptytsia Hennadii, and Kapinus Serhii. "DYNAMIC CONTROL OVER TRAFFIC FLOW UNDER URBAN TRAFFIC CONDITIONS." Eastern-European Journal of Enterprise Technologies 4, no. 3 (106) (2020): 34–43. https://doi.org/10.15587/1729-4061.2020.210170.

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This article deals with solving topical issues of improving traffic effectiveness in major cities. The main traffic problem in major cities is a decrease in the throughput capacity of a street-road network and an increase in unpredictable travel time. The conducted study determined that the main reason for a decrease in the throughput capacity of a street-road network is the existence of traffic congestion modes and the formation of a "shock wave" with its spreading toward the oncoming traffic flow. To solve this problem, the dynamics of a change of parameters of a transport flow bas

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Yang, Yuan Feng, Xue Feng Xian, Li Li Liao, and Min Ya Zhao. "A Feature Extraction Approach of Traffic Congestion from Video." Advanced Materials Research 490-495 (March 2012): 1058–62. http://dx.doi.org/10.4028/www.scientific.net/amr.490-495.1058.

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To avoid the difficulty of collecting accurate traffic flow data, this paper proposes a novel approach for congestion features extraction from traffic video. The approach firstly segments the traffic video into shots and the shot motion content feature is extracted. Then, we extract the key frames applying an improved global k-means clustering algorithm. The last congestion feature of the global optical flow energy is computed based on the key frames. The numerical experiments on traffic surveillance video show the validity and high accuracy for traffic congestion detection using the propose m

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Urvi, Garewal, and Upadhyay Pooja. "The implementation and result of a vehicle detection and smart traffic management system using IoT." International Journal of Advance Research in Multidisciplinary 1, no. 1 (2023): 827–31. https://doi.org/10.5281/zenodo.14242312.

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One of the most important parts of a smart city is its traffic management system. Road congestion is a common problem in major cities due to the increasing number of people living there and the ease with which they can move about. This article presents a smart traffic management system that utilises the Internet of Things (IoT) to address many road traffic management concerns and assist authorities with effective planning. An algorithm is developed to effectively handle different traffic scenarios, and a hybrid method is used to optimise road traffic flow. The system operates the traffic light

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Dongare, Tejas, Dhiraj Huljute, Pranit Jadhav, Anuj Lad, and Prof Sheetal Marawar. "A Review on Traffic Management and Road Analysis of Porwal Road." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 881–84. http://dx.doi.org/10.22214/ijraset.2023.48508.

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Abstract: Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement, and bad traffic management has to lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on the economy, the environment, and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. In cities, where the number of vehi

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Muhammad, Hamdan, Omran Khalifah Othman, and Surya Gunawan Teddy. "Measuring the Road Traffic Intensity Using Neural Network with Computer Vision." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 1 (2018): 184–90. https://doi.org/10.11591/ijeecs.v10.i1.pp184-190.

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Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things (IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another pr

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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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Anees, Khan, and Sarwes Site Prof. "Study of Congestion Control Scheme with Decentralized Threshold Function in VANETs." International Journal of Trend in Scientific Research and Development 3, no. 5 (2019): 398–401. https://doi.org/10.5281/zenodo.3589841.

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With the constant increase in vehicular traffic, existing traffic management solutions have become inefficient. Urbanization has led to an increase in traffic jams and accidents in major cities. In order to accommodate the growing needs of transport systems today, there is a need for an Intelligent Transport System. Vehicular Ad hoc Network VANET is a growing technology that assists in Intelligent Transport Systems. VANETs enable communication between vehicles as well as fixed infrastructure called Road Side Units RSU . We propose a distributed, collaborative traffic congestion detection and d

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V, Mahalakshmi, and Dr Manjunath S. "Automatic Detection of Pedestrian Crossing Platform using Congestion Monitoring." International Journal for Research in Applied Science and Engineering Technology 11, no. 8 (2023): 275–79. http://dx.doi.org/10.22214/ijraset.2023.55178.

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Abstract: One of the primary problems faced globally is the amount of traffic on the roads and the number of pedestrian accidents. The risk when crossing or walking on roads in urban and rural regions with significant traffic is a major factor in these incidents. A novel idea is put forth to avert such situations. Using congestion monitoring, automatic detection of pedestrian crossing platforms. An IR (Infrared) sensor module is used to continuously monitor pedestrian and traffic congestion. When there are more pedestrians on the road, the traffic light for automobiles turns red, allowing pede

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kumar, G. Ashish. "AI Traffic Monitoring." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50329.

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Introduction: This project leverages artificial intelligence to monitor and manage traffic in real time. The system uses Computer vision and machine learning algorithms to analyze video feeds from traffic cameras, detect congestion, track vehicle movements, and optimize traffic flow. 2. Objectives: Automate traffic monitoring using AI. Detect traffic congestion, rule violations, and accidents. Generate real-time alerts and reports. Improve traffic flow and safety. 3. System Overview: Data Input: Live video feed or recorded footage from traffic cameras. Processing Unit: AI model for object dete

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Zhang, Chaokai, Hao Cheng, Rui Wu, Biyun Ren, Ye Zhu, and Ningbo Peng. "Development of a Traffic Congestion Prediction and Emergency Lane Development Strategy Based on Object Detection Algorithms." Sustainability 16, no. 23 (2024): 10232. http://dx.doi.org/10.3390/su162310232.

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With rapid economic development and a continuous increase in motor vehicle numbers, traffic congestion on highways has become increasingly severe, significantly impacting traffic efficiency and public safety. This paper proposes and investigates a traffic congestion prediction and emergency lane development strategy based on object detection algorithms. Firstly, the YOLOv11 object detection algorithm combined with the ByteTrack multi-object tracking algorithm is employed to extract traffic flow parameters—including traffic volume, speed, and density—from videos at four monitoring points on the

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P Hridaynath, Hridaynath, Niraj N. Patil, Arjun L. Lugade, and Gurunath S. Matugade. "TRAFFIC ANALYSIS USING IOT." International Journal of Engineering Applied Sciences and Technology 8, no. 1 (2023): 127–31. http://dx.doi.org/10.33564/ijeast.2023.v07i12.022.

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Traffic congestion is a major problem in many cities of India along with other countries. Failure of signals, poor law enforcement and bad traffic management has lead to traffic congestion. One of the major problems with Indian cities is that the existing infrastructure cannot be expanded more, and thus the only option available is better management of the traffic. Traffic congestion has a negative impact on economy, the environment and the overall quality of life. Hence it is high time to effectively manage the traffic congestion problem. There are various methods available for traffic manage

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Budiharto, Widodo, and Heri Ngarianto. "The Framework of Vehicle Detection and Counting System for Handling of Toll Road Congestion using YOLOv8." International Journal of Computer Science and Humanitarian AI 2, no. 1 (2025): xx. https://doi.org/10.21512/ijcshai.v2i1.13020.

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The Global COVID-19 pandemic and the increasing number of vehicles have exacerbated traffic congestion, particularly in developing countries. In Jakarta, Indonesia, congestion on toll roads is a significant issue that needs to be addressed through an Intelligent Transportation System (ITS). One of the key solutions proposed is vehicle detection and traffic prediction on toll roads. This study introduces a computer vision-based approach utilizing YOLOv8 to detect, track, and count vehicles to predict traffic congestion. The system operates by identifying vehicles (cars and trucks), preprocessin

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Bretherton, David, Keith Wood, and Neil Raha. "Traffic Monitoring and Congestion Management in the SCOOT Urban Traffic Control System." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (1998): 118–22. http://dx.doi.org/10.3141/1634-15.

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The SCOOT Urban Traffic Control system is now operating in over 170 cities worldwide, including 7 systems in North America. Since the first system was installed, there has been a continuous program of research and development to provide new facilities to meet the requirement of the traffic manager. The latest version of SCOOT (Version 3.1) incorporates a traffic information database, ASTRID, and an incident-detection system, INGRID, and provides a number of facilities for congestion control. The traffic monitoring facilities of SCOOT, including a new facility to estimate emissions from vehicle

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