Arduino Based traffic congestion control with automatic signal clearance for emergency vehicles and Stolen Vehicle Detection

Prakash, N.; Udayakumar, E.; Kumareshan, N.

doi:10.1109/iccci48352.2020.9104120

Cited by 23 publications

(5 citation statements)

References 2 publications

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“…15 Moreover, introducing the idea of vagueness, fuzzy approaches were used to traffic control. 16 Typically, fuzzy techniques are used to model expert opinion when developing a mathematical formula that is impractical or extremely complex. 17 Fuzzy set theory has the potential to emulate human intellect.…”

Section: F I G U R E 1 Urban Intersection Traffic Flowmentioning

confidence: 99%

“…Moreover, ABO is trained in the system for path searching with effective learning parameters. Equation does the searching of the path (16),…”

Section: Classification Processmentioning

confidence: 99%

“…Simultaneously, it is hard to develop a predetermined formula for congestion control 15 . Moreover, introducing the idea of vagueness, fuzzy approaches were used to traffic control 16 . Typically, fuzzy techniques are used to model expert opinion when developing a mathematical formula that is impractical or extremely complex 17 .…”

Section: Introductionmentioning

confidence: 99%

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A novel optimized intelligent green signal timing system for urban corridor

Malathi

Kumar

2022

Concurrency and Computation

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Summary In the real world, fixed traffic time signal control is commonly used due to the low implementation cost. However, these signal systems do not achieve the best performance, especially when the unbalanced traffic demand. Hence, a novel buffalo based recurrent fuzzy green timing system (BRFGTS) is proposed to enhance the green timing. Moreover, traffic speed data was initially collected to implement the proposed model. Consequently, after neural layer training, the preprocessing was performed to eliminate the unwanted data. After that, feature extraction is done to take the required features from the preprocessed data. After extracting the features, the data are moved through the classification process. A recurrent network‐based approach trains the data in the classification process. Furthermore, the fitness of buffalo is updated in the classification layer of the recurrent model to reduce the traveling time by improving the shortest route prediction to identify the other path to travel vehicle. The proposed green timing system is implemented in the tool named MATLAB. Subsequently, the results from the proposed model are compared with different techniques. Thus the recorded minimum traveling time by the presented BRFGTS is 20.5 s; compared to other models, it has minimized the traveling time by 40%. Also, the proposed model has minimized the queue length by 38% than the compared models. Hence, the robustness of the novel BRFGTS has been proved.

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Section: F I G U R E 1 Urban Intersection Traffic Flowmentioning

confidence: 99%

“…Moreover, ABO is trained in the system for path searching with effective learning parameters. Equation does the searching of the path (16),…”

Section: Classification Processmentioning

confidence: 99%

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A novel optimized intelligent green signal timing system for urban corridor

Malathi

Kumar

2022

Concurrency and Computation

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“…Recently in 2020, a hybrid approach was proposed [5] that made use of infrared sensors and RFID technology. The IR sensors will detect road traffic density which will be used to trigger an interrupt dynamically.…”

Section: Related Workmentioning

confidence: 99%

Cloud-Based Smart Traffic Lights Monitoring and Controlling Using IoT

Naik

Sundar

Agrawal

et al. 2021

Advances in Intelligent Systems and Computing

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Emergency vehicles like ambulance and fire brigades getting stuck in the traffic often results in casualties. Many approaches have been proposed for the problem that includes use of RFID tags, computer vision, manual override of traffic signals, etc. These approaches have disadvantages in terms of number of components, complexity, price, range, etc. In this paper, we propose a new architecture CSTMC which stands for "cloud-based smart traffic monitoring and controlling" and two approaches on top of CSTMC, namely next light green (NLG) and all lights green (ALG), respectively. CSTMC addresses the limitations faced by the previous methods by using cloud computing and IoT cluster. This helps the system to be much simpler and reliable and reduce potential points of failure. Our NLG approach allowed us to achieve an improvement of 15% in the travel time of emergency vehicles based on the simulations performed.

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“…Presently, quite a few countries are suffering from the negative consequences of traffic congestion, which, in turn, negatively contribute to the transportation system in urban areas and cause serious problems [3]. Regardless of whether the traffic authorities and the flagmen are replaced with programmed traffic systems, the repair of the overburdened road is still insufficient to alleviate traffic congestion and time delays [4].…”

Section: Introductionmentioning

confidence: 99%