Predicting Fine Spatial Scale Traffic Noise Using Mobile Measurements and Machine Learning.

ABSTRACT: Environmental noise has been associated with a variety of health endpoints including cardiovascular disease, sleep disturbance, depression, and psychosocial stress. Most population noise exposure comes from vehicular traffic, which produces fine-scale spatial variability that is difficult to characterize using traditional fixed-site measurement techniques. To address this challenge, we collected A-weighted, equivalent noise (LAeq in decibels, dB) data on hour-long foot journeys around 16 locations throughout Long Beach, California and trained four machine learning models, linear regression, random forest, extreme gradient boosting, and a neural network, to predict noise with 20 m resolution. Input variables to the models included traffic metrics, road network features, meteorologica

SUBMITTER: Yin X 

PROVIDER: S-EPMC11842164 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature