@article {Ladino Velásquez:2018:0736-2935:4542,
title = "Collaborative Traffic Data for Road Noise Mapping",
journal = "INTER-NOISE and NOISE-CON Congress and Conference Proceedings",
parent_itemid = "infobike://ince/incecp",
publishercode ="ince",
year = "2018",
volume = "258",
number = "3",
publication date ="2018-12-18T00:00:00",
pages = "4542-4557",
itemtype = "ARTICLE",
issn = "0736-2935",
url = "https://ince.publisher.ingentaconnect.com/content/ince/incecp/2018/00000258/00000003/art00057",
author = "Ladino Vel{\’a}squez, Anderson and Duque, Carolina and Id{\’a}rraga, Sergio Andr{\’e}s Castrill{\’o}n and Muriel, Andres Felipe Osorio and Infante, Jorge Mauricio Carranza and Vanegas, Claudia Elena Durango and G{\’o}mez, Diego Mauricio Murillo",
abstract = "This paper addresses the acquisition of non-authoritative collaborative traffic data to predict the noise generated by urban roads. The information about traffic is acquired by means of a API developed by Google, which provides data about travel time and speed over a start-end routes
matrix. To determine the traffic flow from these variables, a predictive model is proposed that takes into consideration the type of road, percentage of light/heavy vehicles, and time gap. Furthermore, an analysis of the relevance of the traffic flow data as an input variable for the estimation
of the road noise emission has been conducted by numerical simulations. The methodology has been tested in two areas of the city of Medellin. The results indicate that it is feasible to use Google Maps as a source of information to predict road urban noise. Nevertheless, further studies are
required to improve the estimation of traffic flow from the obtained collaborative data.",
}