An Adaptive Method for an Isolated Intersection Under Mixed Traffic Conditions in Hanoi Based on ANFIS Using VISSIM-MATLAB

Vuong, Xuan Can; Mou, Ruifang; Vu, Trong Thuat; Nguyen, Hoang

doi:10.1109/access.2021.3135418

Cited by 5 publications

(4 citation statements)

References 28 publications

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“…The proposed BRFGTS performance is compared with other techniques like the time‐slot signal scheme method with the incorporation of generalized cycle‐based structure (TSS‐GCS), 19 real‐time based signal control approach dependent upon nonlinear‐decision rule (RSC‐NDR), 20 adaptive neuro‐FIS (ANFIS) 26 and fuzzy logic system (FLS) 27 . The performance comparison was performed to determine the proposed methods effectiveness.…”

Section: Resultsmentioning

confidence: 99%

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: Resultsmentioning

confidence: 99%

A novel optimized intelligent green signal timing system for urban corridor

Malathi

Kumar

2022

Concurrency and Computation

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“… The overall vehicle operating state was improved after using the proposed approach. [ 190 ] Evaluating the effectiveness of the suggested approach. Adaptive neuro-fuzzy inference system (ANFIS) Adaptive traffic signal control method for an isolated intersection under mixed traffic conditions Vehicle delay, average travel time and maximum queue length are reduced after using the ANFIS model.…”

Section: Vissim Application Literature Reviewmentioning

confidence: 99%

“…optimised the traffic signal using estimation of distribution algorithms and tested the obtained result using a simulated environment. After developing an adaptive neuro-fuzzy inference system (ANFIS) model to estimate the extent of traffic emissions and optimise the cycle time [ 184 , 190 ]. used VISSIM simulation to evaluate the performance of intersections both before and after optimisation [ 186 ].…”

Section: Vissim Application Assessment and Evaluationmentioning

confidence: 99%

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

Al-Msari,

Koting,

Ahmed

et al. 2024

Heliyon

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“…ANFIS (Adaptive Neuro-Fuzzy Inference System) controls traffic lights by observing vehicle waiting times and queue lengths. As a result, the traffic signal timing can be adjusted based on traffic density and the number of vehicles on each street, making traffic flow more smoothly [19].…”

Section: Green Time Extension Modulementioning

confidence: 99%

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

İnağ,

Arıkan

2024

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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The escalating global population and increased vehicle usage have worsened traffic congestion in metropolitan areas, a significant urban challenge. Addressing this, adaptive traffic light control methods, especially at intersections, are being developed to improve traffic flow and reduce waiting times. This study significantly contributes to this field by implementing Fuzzy Logic in intelligent traffic light systems, focusing on Ankara's Polatlı Refik Cesur intersection. Using the SUMO simulation platform and Python programming, it analyzed waiting times and queue lengths. The initial phase used queue length for each intersection arm as an input. Fuzzy logic rules then determined the output, prioritizing street or phase order for optimal flow. The study further proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS)-based control plan. ANFIS merges neural network capabilities with fuzzy logic, using waiting time and queue length as inputs to regulate the green light duration. Compared to existing traffic systems, this model showed a substantial improvement. It achieved a 36.5% reduction in waiting times, underlining the efficiency of the Fuzzy Logic-based method. This approach not only enhances traffic management but also contributes significantly to the literature on intelligent traffic light control systems. By addressing key urban traffic issues, the study paves the way for future advancements in traffic management technologies. The findings highlight the potential of combining advanced computational methods, like ANFIS, with traditional traffic control techniques to optimize urban traffic flow, offering a blueprint for similar challenges in other metropolitan areas.

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An Adaptive Method for an Isolated Intersection Under Mixed Traffic Conditions in Hanoi Based on ANFIS Using VISSIM-MATLAB

Cited by 5 publications

References 28 publications

A novel optimized intelligent green signal timing system for urban corridor

A novel optimized intelligent green signal timing system for urban corridor

Review of driving-behaviour simulation: VISSIM and artificial intelligence approach

A Fuzzy Based Intelligent Traffic Light Control (ITLC) Method: An Implementation in Ankara City

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