A Sound-based Machine Learning to Predict Traffic Vehicle Density

Keywords: vehicle density, sound features, sound intensity, machine learning, traffic prediction, random forest, artificial neural network, support vector machine


Traffic flow mismanagement is a significant challenge in all countries especially in crowded cities. An alternative solution is to utilize smart technologies to predict traffic flow. In this study, frequency spectrum describing traffic sound characteristics is used as an indicator to predict the next five-minute vehicle density. Sound frequency and vehicle intensity are collected during a thirteen-hour data gathering. The collected sound intensity and frequency are then used to learn three machine-learning models - support vector machine, artificial neural network, and random forest and to predict vehicle intensity. It was found out that the performances of the three models based on root-mean-square-error values are 12.97, 16.01, and 10.67, respectively. These initial and satisfactory results pave a new way to predict traffic flow based on traffic sound characteristics which may serve as a better alternative to conventional features.

Author Biography

Eduardo Jr. Piedad, University of San Jose-Recoletos, Cebu City, Philippines

Instructor I, Department of Electrical Engineering


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How to Cite
FloresG., PiedadE. J., FigueroaA., TumamakR., & BerdonN. J. M. (2021). A Sound-based Machine Learning to Predict Traffic Vehicle Density. Recoletos Multidisciplinary Research Journal, 9(1), 55-62. https://doi.org/10.32871/rmrj2109.01.05