Smart controlling for traffic light time

Zaid, Alaa Abu; Suhweil, Yazan; Yaman, Musa Al

doi:10.1109/aeect.2017.8257768

Cited by 24 publications

(11 citation statements)

References 9 publications

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“…Some studies with a focus only on recognizing vehicles using images, with no sensors [64], are concerned only with determining the waiting time of every vehicle. Another traffic light proposal used AI [65], and the study focused only on obtaining a shorter waiting time for queues at intersections, based on the entry of vehicles at these intersections.…”

Section: Traffic Light Solutions Using Artificial Intelligencementioning

confidence: 99%

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Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Barbosa

Ayub

Rosa

et al. 2020

Sensors

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Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

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Section: Traffic Light Solutions Using Artificial Intelligencementioning

confidence: 99%

“…Another study [67] proposed a system to control the density of traffic in real time using digital image processing, obtaining better efficiency in general than existing systems. It is important to note that some studies [64][65][66][67] did not include priority vehicles.…”

Section: Traffic Light Solutions Using Artificial Intelligencementioning

confidence: 99%

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Barbosa

Ayub

Rosa

et al. 2020

Sensors

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show abstract

“…Li et al later proposed a new control system based on real-time traffic flow [4], which can intelligently set the duration of traffic lights by sensing the number of vehicles through the sensor network. In the same year, with the rise of artificial intelligence technology, Zaid et al [5] developed an automatic algorithm to control the time of traffic lights based on artificial intelligence technology and images on traffic lights, which can significantly increase the reliability and stability of intelligent traffic lights; according to Jin's team [6], a group based framework algorithm with adaptive learning ability can change the adaptive learning ability according to the traffic demand in real time and play a greater role in coordination; in the following year, Jin's team proposed a multi-level stage control scheme based on collective learning [7], emphasizing the use of reinforcement learning algorithm to train the system model, so as to improve the traffic performance. After that, intelligent transportation began to introduce the concept of Fog Computing (FC) [8].…”

Section: Literature Reviewmentioning

confidence: 99%

A Scheme of Intelligent Traffic Light System Based on Distributed Security Architecture of Blockchain Technology

Zeng

Wang

et al. 2020

IEEE Access

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In recent years, under the background that the rapid development of traffic volume makes the current traffic lights far from meeting the urban traffic demand, intelligent traffic lights based on the centralized architecture began to appear. However, in the traffic network with complex structure and private data flow, there are many malicious attacks against the centralized architecture, such as Sybil and ghost car attacks, which undoubtedly brings great security risks to the traditional intelligent traffic lights. Blockchain technology is a popular security framework nowadays. Based on its outstanding characteristics in the distributed architecture and the development of Edge Intelligence (EI) technology, this paper proposes a distributed security architecture scheme based on blockchain technology for the existing intelligent traffic light system. At the same time, based on the model cutting technology proposed by EI, the smart contract is improved to achieve redundant cutting of ledger data in the process of block consensus, which greatly reduces the pressure of blockchain ledger data transmission. In the end of this paper, the superiority of this scheme compared with the traditional intelligent traffic light scheme in communication cost and time cost is demonstrated by simulation experiment.INDEX TERMS Blockchain, intelligent transportation, distributed architecture, edge intelligence.PENGJIE ZENG is currently pursuing the bachelor's degree from the Hunan University of Science and Technology. His research field is the application of blockchain in the Internet of Things, cyberspace security, privacy protection, and large data processing and analysis. He received the Second Prize at the National College Students Innovative Electronic Design Competition and the Third Prize at the School-Level Financial Management Data Analysis Competition. He is currently in charge of leading the Technical Team to complete the development of a smart agriculture project and an intelligent traffic light project.

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“…In recent years, the rapid development of artificial intelligence [8] technology, coupled with traffic road perception technology [9], has become quite mature. Therefore, the application of artificial intelligence and transportation can develop rapidly.…”

Section: Related Workmentioning

confidence: 99%

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

et al. 2019

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In recent years, within large cities with a high population density, traffic congestion has become more and more serious, resulting in increased emissions of vehicles and reducing the efficiency of urban operations. Many factors have caused traffic congestion, such as insufficient road capacity, high vehicle density, poor urban traffic planning and inconsistent traffic light cycle configuration. Among these factors, the problems of traffic light cycle configuration are the focal points of this paper. If traffic lights can adjust the cycle dynamically with traffic data, it will reduce degrees of traffic congestion significantly. Therefore, a modified mechanism based on Q-Learning to optimize traffic light cycle configuration is proposed to obtain lower average vehicle delay time, while keeping significantly fewer processing steps. The experimental results will show that the number of processing steps of this proposed mechanism is 11.76 times fewer than that of the exhaustive search scheme, and also that the average vehicle delay is only slightly lower than that of the exhaustive search scheme by 5.4%. Therefore the proposed modified Q-learning mechanism will be capable of reducing the degrees of traffic congestions effectively by minimizing processing steps.

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Smart controlling for traffic light time

Cited by 24 publications

References 9 publications

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

A Scheme of Intelligent Traffic Light System Based on Distributed Security Architecture of Blockchain Technology

Traffic Light Cycle Configuration of Single Intersection Based on Modified Q-Learning

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