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Relevant bibliographies by topics / TRAFFIC CONGESTION DETECTION

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Journal articles

Academic literature on the topic 'TRAFFIC CONGESTION DETECTION'

Author: Grafiati

Published: 11 September 2023

Last updated: 12 June 2025

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Journal articles on the topic "TRAFFIC CONGESTION DETECTION"

1

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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2

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 presents various techniques and methodologies. This paper presents an overview of traffic congestion detection and classification methods of various related techniques in VANET, organized from the research perspective. Qualitative analysis is presented to classify these strategies in its system architecture, detection and classification methods, as well as its simulated mobility environment and simulation tools used. The analysis is useful in understanding all the techniques and methods applied in resolving this issue in the research domain. </span>

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3

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 empirical prediction and analytical verification. First, we collect a range of traffic images that correspond to specific traffic snapshots which are vulnerable to potential data spoofing attacks. Based on these traffic images, an improved generative adversarial network is trained to predict whether a forthcoming attack will cause congestion with a high probability. Meanwhile, we define a group of traffic flow features. After exploring features and conducting a thorough analysis, a TGRU (tree-regularized gated recurrent unit)-based approach is proposed to verify whether congestion occurs. When we find a possible attack that can cause congestion with high probability and subsequent traffic flows also prove congestion, we can say there is a congestion attack. Thus, we can realize timely and accurate congestion attack detection by integrating empirical prediction and analytical verification. Extensive experiments demonstrate that our approach performs well in congestion attack detection accuracy and timeliness.

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4

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 the data in the congestion areas can be transferred to noncongestion areas as soon as possible. Results: The simulation results indicate that the proposed scheme can reduce the number of loss packets, improve the throughput, stabilize the average transmission rate of source node and reduce the end-to-end delay. Conclusion: : So the proposed scheme can enhance the overall performance of the network. Keywords: wireless sensor network; congestion control; congestion detection; congestion mitigation; traffic assignment; traffic reassignment.

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5

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 communication channels very efficiently and irrespective of any kind of overload. This proposed system aims to introduce a novel framework for identifying traffic jam on Vehicular Ad-hoc Networks. In order to detect and minimize the level of congestion our approach will use a fuzzy logic based approach to notify the drivers about available routes during the traffic congestion. An experimental prototype will be set up to enable the graphical simulation.

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6

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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7

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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8

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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9

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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10

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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