Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network

Jutury, Dheeraj; Kumar, Neetesh; Sachan, Anuj; Daultani, Yash; Dhakad, Naveen

doi:10.1007/s10489-022-03827-3

Cited by 15 publications

(5 citation statements)

References 44 publications

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“…Adaptive Neural Fuzzy Inference System (ANFIS) for building LFC in multiconnected areas with energy storage systems, which may reduce both control time and frequency variation during active power system operation [8]. Artificial neural networks and fuzzy reasoning come together to make neurofuzzy systems [9].…”

Section: Proposed Schemementioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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The main objective of Load Frequency Control (LFC) is to effectively manage the power output of an electric generator at a designated site, in order to maintain system frequency and tie-line loading within desired limits, in reaction to fluctuations. The adaptive neuro-fuzzy inference system (ANFIS) is a controller that integrates the beneficial features of neural networks and fuzzy networks. The comparative analysis of Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Proportional-Integral-Derivative (PID)-based methodologies demonstrates that the suggested ANFIS controller outperforms both the PID controller and the ANN controller in mitigating power and frequency deviations across many regions of a hybrid power system. Two systems are analysed and represented using mathematical models. The initial system comprises a thermal plant alongside photovoltaic (PV) grid-connected installations equipped with maximum power point trackers (MPPT). The second system comprises hydroelectric systems. The MATLAB/Simulink software is employed to conduct a comparative analysis of the outcomes produced by the controllers.

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Section: Proposed Schemementioning

confidence: 99%

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Nireekshana,

Ramachandran,

Narayana

2024

E3S Web Conf.

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“…Antonio G-P et al proposed a traffic control method based on 5G communication and IoV to address the priority traffic control of EVs in urban transportationnet-works, effectively reducing the travel and congestion time of EVs [20]. Jutury D et al proposed an intelligent EV priority control method based on neural fuzzy to effectively improve the driving efficiency of EVs due to the exacerbation of traffic congestion speed, which led to lower actual operating efficiency [21]. Hajiloo R et al proposed an integrated vehicle effective traffic control method based on IoV for the comprehensive traffic control problem of vehicles in emergency situations, thereby effectively improving the rescue efficiency of EVs [22].…”

Section: Related Workmentioning

confidence: 99%

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Wang,

Geng

2024

IEEE Access

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The current emergency vehicle priority control methods in road traffic net-works are difficult to cope with the increasing traffic demand. Therefore, a traffic control method based on multi-vehicle collaborative lane change strategy, fleet convergence and gap adjustment model is proposed and its effectiveness is verified. These experiments confirmed that the speed of vehicle Cj+1 under strategy 1 showed a positive correlation with time at 0-5s, reaching an extreme value of 16.85 m/s around 4s, and a negative correlation after 5s. Under strategy 2, its speed showed a negative correlation with time at 0-4s, and a parallel relation-ship after 4s. The multi-vehicle col-laborative lane changing strategy was validated. Except for fleet C with a density of +0.0333m-1, the actual adjustment time of vehicle C1 was gradually increasing at all other densities. The maximum time for B1 adjustment was 9.358s, and the maximum time for C1 adjustment was 10.798s. The longitudinal relative dis-placement of C1 was larger than that of B1. In addition, compared with Model T, the research method increased the average vehicle speed by 12.64% under four different flow rates. Compared with Model Y, the average flow rate of the research method under the four experimental flows was 1.45%. Overall, the research method is effective and feasible in the priority selection control of road traffic net-works. It improves the operational efficiency of emergency vehicle sections and can be effectively applied in actual traffic net-works.INDEX TERMS Internet of Vehicles, Emergency situations, Multi-vehicle col-laborative lane changing strategy, Gap adjustment model, Flow

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“…It is a type of supervised learning algorithm that takes a set of input features and produces a decision tree as its output. This decision tree is essentially a flowchart-like structure where each internal node represents a decision based on a particular feature, each branch represents the outcome of that decision, and each leaf node represents a class label or a predicted value [53].…”

Section: Decision Treementioning

confidence: 99%

Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques

Khasawneh,

Awasthi

2023

Electronics

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This research examines worldwide concerns over traffic congestion, encompassing aspects such as security, parking, pollution, and congestion. It specifically emphasizes the importance of implementing appropriate traffic light timing as a means to mitigate these issues. The research utilized a dataset from Montreal and partitioned the simulated area into various zones in order to determine congestion levels for each individual zone. A range of prediction algorithms has been employed, such as Long Short-Term Memory (LSTM), Decision Tree (DT), Recurrent Neural Network (RNN), Auto-Regressive Integrated Moving Average (ARIMA), and Seasonal Auto-Regressive Integrated Moving Average (SARIMA), to predict congestion levels at each traffic light. This information was used in a mathematical formulation to minimize the average waiting time for vehicles inside the road network. Many meta-heuristics were analyzed and compared, with the introduction of an Enhanced Bat Algorithm (EBAT) suggested for addressing the traffic signal optimization problem. Three distinct scenarios are described: fixed (with a constant green timing of 40 s), dynamic (where the timing changes in real-time based on the current level of congestion), and adaptive (which involves predicting congestion ahead of time). The scenarios are studied with low and high congestion scenarios in the road network. The Enhanced Bat Algorithm (EBAT) is introduced as a solution to optimize traffic signal timing. It enhances the original Bat algorithm by incorporating adaptive parameter tuning and guided exploration techniques that are informed by predicted congestion levels. The EBAT algorithm provides a more effective treatment for congestion problems by decreasing travel time, enhancing vehicle throughput, and minimizing pollutant emissions.

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Adaptive neuro-fuzzy enabled multi-mode traffic light control system for urban transport network

Cited by 15 publications

References 44 publications

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Novel Intelligence ANFIS Technique for Two-Area Hybrid Power System’s Load Frequency Regulation

Priority Selection of Road Traffic Net-Works in Emergency Situations Based on Internet of Vehicles

Intelligent Meta-Heuristic-Based Optimization of Traffic Light Timing Using Artificial Intelligence Techniques

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