Traffic Flow Prediction at Signalized Road Intersections: a Case of Markov Chain and Artificial Neural Network Model

EasyChair Preprint 6300

Abstract

Traffic congestion is a prevailing problem globally, which threatens the wider community, especially in developing countries. The Markov chain model (MCM) is a widely acknowledged and applied method used in traffic modelling, planning, and road traffic control systems. Classical 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 artificial neural network model for predicting traffic flow of vehicles at signalized road intersections. Analysis of dataset collected at Mikros traffic monitoring (MTM) firm, with vehicular speed and distance as input variables and time as output, gave a good performance with root mean square error (RMSE) of 0.0025 and coefficient of determination (R2 ) of 0.96417. The ANN model was adjudged capable of modelling traffic flow at road intersections

Keyphrases: Artificial Intelligence, Artificial Neural Network (ANN), Congestion, Markov chain model, Traffic

@booklet{EasyChair:6300,
  author    = {Isaac Olayode and Lagouge Kwanda Tartibu and Modestus Okwu},
  title     = {Traffic Flow Prediction at Signalized Road Intersections: a Case of Markov Chain and Artificial Neural Network Model},
  howpublished = {EasyChair Preprint 6300},
  year      = {EasyChair, 2021}}