Open AccessA Sound-based Machine Learning to Predict Traffic Vehicle Density
Author(s) -
Geoferleen Flores,
Eduardo Jr Piedad,
Anzeneth Figueroa,
Romari Tumamak,
Nesrah Jane Marie Berdon
Publication year - 2021
Publication title -
recoletos multidisciplinary research journal (online)/recoletos multidisciplinary research journal (usj-r. print).
Language(s) - English
Resource type - Journals
eISSN - 2423-1398
pISSN - 2408-3755
DOI - 10.32871/rmrj2109.01.05
Subject(s) - sound intensity , intensity (physics) , artificial neural network , sound (geography) , traffic flow (computer networking) , support vector machine , computer science , random forest , mean squared error , simulation , machine learning , artificial intelligence , acoustics , statistics , mathematics , quantum mechanics , physics , computer security
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.