Application of Fuzzy Mamdani Model in Traffic Flow of Vehicles at Signalized Road Intersections

EasyChair Preprint 5770

Abstract

Traffic congestion is a pre-existing problem globally, which threatens the wider community, especially in developing countries. Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modeling, planning, and development of road traffic control systems. Traditional techniques like MCM have been used to reduce vehicular flow and traffic congestions. Nowadays, artificial intelligence techniques, have been recognized for solving traffic congestions and multivariate problems. The application of ANN in traffic flow prediction performance yielded positive results. The present study dwells on a comparison between the Markov Chain Model and the artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm using vehicular speed, distance, and time as input and output parameters, gave a good performance with root mean square error (RMSE) of 0.0025and average coefficient of determination (R2) of 0.96417. The ANN model was adjudged capable of modeling traffic flow at road intersections.

Keyphrases: Artificial Intelligence, Artificial Neural Network (ANN), Markov chain model, traffic congestion

@booklet{EasyChair:5770,
  author    = {Isaac Oyeyemi Olayode and Lagouge Kwanda Tartibu and Modestus Okechukwu Okwu},
  title     = {Application of Fuzzy Mamdani Model in Traffic Flow of Vehicles at Signalized Road Intersections},
  howpublished = {EasyChair Preprint 5770},
  year      = {EasyChair, 2021}}