Intelligent Traffic Signal Control System Using Machine Learning Techniques

Ali, Mohammad; Devi, G. Lavanya; Neelapu, Ramesh

doi:10.1007/978-981-15-3828-5_63

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…Ali et al overcame the issue of traffic congestion and challenges by implementing a support vector machine-(SVM-) based model [35]. This model further optimized the configuration signal of high-traffic zones to medium or low-traffic zones.…”

Section: Literature Reviewmentioning

confidence: 99%

A Distributed Emergency Vehicle Transit System Using Artificial Intelligence of Things (DEVeTS-AIoT)

Mahmud

Hussain

Sarwar

et al. 2022

Wireless Communications and Mobile Computing

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The advent of the 4th industrial revolution has realized smart environments composed of cloud and fog. These have been improved to enable intelligence at the fog, where numerous, inexpensive devices communicate with each other and provide computation capabilities to solve domain-specific problems in a distributed fashion. One of these domains is smart traffic flow with a focus on emergency vehicle (EV) transit. The increase in traffic congestion in third-world countries is a hindrance in EV transit to save human lives. This paper proposes an Artificial Intelligence of Things– (AIoT–) based, distributed, EV transit system developed on Raspberry PI as a rule-based system with minimal sensors. The sensors include an infrared sensor to detect emergency light and directional microphones to detect siren. The departure direction of the EV is shared with adjacent intersections to further reduce the transit time.

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Section: Literature Reviewmentioning

confidence: 99%

A Distributed Emergency Vehicle Transit System Using Artificial Intelligence of Things (DEVeTS-AIoT)

Mahmud

Hussain

Sarwar

et al. 2022

Wireless Communications and Mobile Computing

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“…Proposed work in [18] uses the IoT concept to control the traffic congestion as vehicles and traffic lights can be able to communicate directly with the help of IoT. In the proposed work in [19], artificial intelligence and machine learning is used to develop an intelligent traffic system. The approach in [20] estimates the waiting duration based on the number of vehicles which detected using video processing beside statistical methods such as cluster analysis, multiple regression analysis, and factor analysis.…”

Section: Related Workmentioning

confidence: 99%

A Fog Based Smart Traffic Management System

Hussein

Zaki

2022

Nile Journal of Communication and Computer Science

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Recently, urban mobility has become one of the most pressing issues in today's cities, and it must be addressed with caution. The exponential increase in the number of cars has a negative influence on the transportation system that most communities rely on. One of the most important aspects of transportation system is traffic control, which is reliant on a series of coordinated traffic lights. Smart traffic lights not only can receive and analyses the real time traffic data but also can help to alleviate traffic congestion by accurately predicting the waiting time for each traffic lane at the intersections. This can help to enhance traffic flow and, as a result, the overall performance of the transportation system. The proposed Smart Traffic System (STS) not only an automated IoT based traffic measuring system but it also calculates the ideal waiting time for each traffic lane. Calculating the optimal waiting time of each lane of the intersections can reduce the average waiting time. The objective is to provide accurately real-time traffic updates on traffic congestion according to the size of vehicles and their location relative to the traffic lights. Urgent cases for emergency vehicles also has been taken into consideration. Ultrasonic sensors and a lateral scanning approach are employed in the proposed STS which is suitable for using on real traffic roads in various roadway environments. STS adjusted to accurately measure traffic volumes according to the size of vehicles and their locations relative to the traffic light in real time. A prototype is implemented to evaluate the feasibility of the model. Simulation results show good accuracy in vehicles detection, low relative error in road occupancy estimation, the least delay, and highest throughput compared to other works.

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