TY - GEN
T1 - APPLICATION OF ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM MODEL ON TRAFFIC FLOW OF VEHICLES AT A SIGNALIZED ROAD INTERSECTIONS
AU - Olayode, O. I.
AU - Tartibu, L. K.
AU - Okwu, M. O.
N1 - Publisher Copyright:
Copyright © 2021 by ASME
PY - 2021
Y1 - 2021
N2 - In recent years, most traffic accidents and congestions usually occur at road intersections in urban areas where the vehicle speed is high. This has necessitated the need for intelligent road transport systems and high-level algorithms to unravel the problem. In this study, the South Africa Road transportation system has been used as a case study to address traffic flow solutions at signalized road intersections using traffic flow variables such as traffic density, speed of vehicles, and traffic volume as decision variables. This paper focuses on using a hybrid creative algorithm based on signalized traffic flow to address the constant repetitive traffic congestion problem. The proposed hybrid algorithm is the adaptive neuro-fuzzy inference system (ANFIS). The speed of vehicles within the investigation period, the traffic density of the road network, and the traffic volume of vehicles on the road were used as input and output variables, respectively. Triangular membership function and Gaussian membership function were used for input and output variables, and rules were developed based on available traffic flow parameters. The result of the ANFIS model showed a training and testing performance of 0.8722 and 0.9370, respectively. This training and testing results showed that the ANFIS model is an effective model for optimizing traffic flow at signalized road intersections.
AB - In recent years, most traffic accidents and congestions usually occur at road intersections in urban areas where the vehicle speed is high. This has necessitated the need for intelligent road transport systems and high-level algorithms to unravel the problem. In this study, the South Africa Road transportation system has been used as a case study to address traffic flow solutions at signalized road intersections using traffic flow variables such as traffic density, speed of vehicles, and traffic volume as decision variables. This paper focuses on using a hybrid creative algorithm based on signalized traffic flow to address the constant repetitive traffic congestion problem. The proposed hybrid algorithm is the adaptive neuro-fuzzy inference system (ANFIS). The speed of vehicles within the investigation period, the traffic density of the road network, and the traffic volume of vehicles on the road were used as input and output variables, respectively. Triangular membership function and Gaussian membership function were used for input and output variables, and rules were developed based on available traffic flow parameters. The result of the ANFIS model showed a training and testing performance of 0.8722 and 0.9370, respectively. This training and testing results showed that the ANFIS model is an effective model for optimizing traffic flow at signalized road intersections.
KW - Adaptive neuro-fuzzy inference system
KW - Signalized Road intersection
KW - Traffic congestion
KW - Traffic flow
UR - http://www.scopus.com/inward/record.url?scp=85124457025&partnerID=8YFLogxK
U2 - 10.1115/IMECE2021-70956
DO - 10.1115/IMECE2021-70956
M3 - Conference contribution
AN - SCOPUS:85124457025
T3 - ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE)
BT - Engineering Education
PB - American Society of Mechanical Engineers (ASME)
T2 - ASME 2021 International Mechanical Engineering Congress and Exposition, IMECE 2021
Y2 - 1 November 2021 through 5 November 2021
ER -
