Dynamic traffic light optimization and Control System using model-predictive control method

Chen, You-Ren; Chen, Keng-Pin; Hsiungy, Pao-Ann

doi:10.1109/itsc.2016.7795937

Cited by 10 publications

(4 citation statements)

References 7 publications

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“…The search for 2015-2020 resulted in a total of 10 such papers [76,84,88,91,[101][102][103][104][105][106] utilizing an analytical approach. Only 3 papers [84,103,105] considered a network problem, and the remaining 7 considered a single network problem. But the size of the realistic problems and the amount of interaction that needs to be included in the model for them to be interesting, make use of analytical methods significantly less practical (promising) for TST problems.…”

Section: Analytical Vs Simulation-optimization and CI Methodsmentioning

confidence: 99%

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

118

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Introduction Due to the menacing increase in the number of vehicles on a daily basis, abating road congestion is becoming a key challenge these years. To cope-up with the prevailing traffic scenarios and to meet the ever-increasing demand for traffic, the urban transportation system needs effective solution methodologies. Changes made in the urban infrastructure will take years, sometimes may not even be feasible. For this reason, traffic signal timing (TST) optimization is one of the fastest and most economical ways to curtail congestion at the intersections and improve traffic flow in the urban network. Purpose Researchers have been working on using a variety of approaches along with the exploitation of technology to improve TST. This article is intended to analyze the recent literature published between January 2015 and January 2020 for the computational intelligence (CI) based simulation approaches and CI-based approaches for optimizing TST and Traffic Signal Control (TSC) systems, provide insights, research gaps and possible directions for future work for researchers interested in the field. Methods In analyzing the complex dynamic behavior of traffic streams, simulation tools have a prominent place. Nowadays, microsimulation tools are frequently used in TST related researches. For this reason, a critical review of some of the widely used microsimulation packages is provided in this paper. Conclusion Our review also shows that approximately 77% of the papers included, utilizes a microsimulation tool in some form. Therefore, it seems useful to include a review, categorization, and comparison of the most commonly used microsimulation tools for future work. We conclude by providing insights into the future of research in these areas.

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Section: Analytical Vs Simulation-optimization and CI Methodsmentioning

confidence: 99%

State-of-art review of traffic signal control methods: challenges and opportunities

Qadri

Gökçe

Öner

2020

Eur. Transp. Res. Rev.

118

View full text Add to dashboard Cite

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“… Ata et al (2019) proposed Artificial Backpropagation Neural Network for the Smart Road Traffic Congestion, which predicts time delay based on traffic speed, humidity, wind speed, and air temperature. Furthermore, Chen, Chen & Hsiungy (2016) refined this method using Genetic Algorithms. The number of vehicles heading towards the green light and the vehicles halted at the red light are used as parameters.…”

Section: Literature Reviewmentioning

confidence: 99%

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Dave

Chandarana

Goel

et al. 2021

PeerJ Computer Science

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The traffic congestion and the rise in the number of vehicles have become a grievous issue, and it is focused worldwide. One of the issues with traffic management is that the traffic light’s timer is not dynamic. As a result, one has to remain longer even if there are no or fewer vehicles, on a roadway, causing unnecessary waiting time, fuel consumption and leads to pollution. Prior work on smart traffic management systems repurposes the use of Internet of things, Time Series Forecasting, and Digital Image Processing. Computer Vision-based smart traffic management is an emerging area of research. Therefore a real-time traffic light optimization algorithm that uses Machine Learning and Deep Learning Techniques to predict the optimal time required by the vehicles to clear the lane is presented. This article concentrates on a two-step approach. The first step is to obtain the count of the independent category of the class of vehicles. For this, the You Only Look Once version 4 (YOLOv4) object detection technique is employed. In the second step, an ensemble technique named eXtreme Gradient Boosting (XGBoost) for predicting the optimal time of the green light window is implemented. Furthermore, the different implemented versions of YOLO and different prediction algorithms are compared with the proposed approach. The experimental analysis signifies that YOLOv4 with the XGBoost algorithm produces the most precise outcomes with a balance of accuracy and inference time. The proposed approach elegantly reduces an average of 32.3% of waiting time with usual traffic on the road.

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“…To achieve the goals, it was eligible for this study the Particle Swarm Optimization (PSO) [2,5,8], the Python Programming Language, and the Simulation of Urban Mobility (SUMO) [3,7], that is a portable microscopic road traffic simulation package that offers the possibility to simulate the traffic moving on the road networks. Based on these methodologies, different simulations are performed to define the number of vehicles that cause traffic congestion.…”

Section: Introductionmentioning

confidence: 99%

Traffic Light Optimization of an Intersection: A Portuguese Case Study

Silva

Rocha

Witeck

et al. 2022

Communications in Computer and Information Science

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Smart cities aim to rise strategies that reduce issues caused by the urban population growth and fast urbanization. Thus, traffic light optimization emerges as an important option for urban traffic management. The main goal of this study is to improve traffic light management at a specific intersection, in the City of Guimarães (Portugal), where high-intensity traffic and an active pedestrian area were observed, generating traffic queues. To achieve the goals, a simulation-based optimization strategy using the Particle Swarm Optimization combined with the Simulation of Urban Mobility software was used to minimize the average waiting time of the vehicles by determining the optimal value of the traffic light cycle. The computational results showed it is possible to decrease by 78.2% the average value of the waiting time. In conclusion, by better managing the traffic light cycle time, traffic flow without congestion or queues can be achieved.

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Dynamic traffic light optimization and Control System using model-predictive control method

Cited by 10 publications

References 7 publications

State-of-art review of traffic signal control methods: challenges and opportunities

State-of-art review of traffic signal control methods: challenges and opportunities

An amalgamation of YOLOv4 and XGBoost for next-gen smart traffic management system

Traffic Light Optimization of an Intersection: A Portuguese Case Study

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