A novel optimized intelligent green signal timing system for urban corridor

Abstract

SummaryIn 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.