Toward a dynamic national transportation noise map: Modeling temporal variability of spectral traffic noise emission levels.

The National Transportation Noise Map predicts time-averaged road traffic noise across the continental United States (CONUS) based on annual average daily traffic counts. However, traffic noise can vary greatly with time. This paper outlines a method for predicting nationwide hourly varying source traffic sound emissions called the Vehicular Reduced-Order Observation-based Model (VROOM). The method incorporates three models that predict temporal variability of traffic volume, predict temporal variability of different traffic classes, and use Traffic Noise Model (TNM) 3.0 equations to give traffic noise emission levels based on vehicle numbers and class mix. Location-specific features are used to predict average class mix across CONUS. VROOM then incorporates dynamic traffic class mix data to obtain dynamic traffic class mix. TNM 3.0 equations then give estimated equivalent sound level emission spectra near roads with up to hourly resolution. Important temporal traffic noise characteristics are modeled, including diurnal traffic patterns, rush hours in urban locations, and weekly and yearly variation. Examples of the temporal variability are depicted and possible types of uncertainties are identified. Altogether, VROOM can be used to map national transportation noise with temporal and spectral variability.

Toward a dynamic national transportation noise map: Modeling temporal variability of traffic volume.

The National Transportation Noise Map (NTNM) gives time-averaged traffic noise across the continental United States (CONUS) using annual average daily traffic. However, traffic noise varies significantly with time. This paper outlines the development and utility of a traffic volume model which is part of VROOM, the Vehicular Reduced-Order Observation-based model, which, using hourly traffic volume data from thousands of traffic monitoring stations across CONUS, predicts nationwide hourly varying traffic source noise. Fourier analysis finds daily, weekly, and yearly temporal traffic volume cycles at individual traffic monitoring stations. Then, principal component analysis uses denoised Fourier spectra to find the most widespread cyclic traffic patterns. VROOM uses nine principal components to represent hourly traffic characteristics for any location, encapsulating daily, weekly, and yearly variation. The principal component coefficients are predicted across CONUS using location-specific features. Expected traffic volume model sound level errors-obtained by comparing predicted traffic counts to measured traffic counts-and expected NTNM-like errors, are presented. VROOM errors are typically within a couple of decibels, whereas NTNM-like errors are often inaccurate, even exceeding 10 decibels. This work details the first steps towards creation of a temporally and spectrally variable national transportation noise map.

Feature selection for a continental-scale geospatial model of environmental sound levels.

Modeling environmental sound levels over continental scales is difficult due to the variety of geospatial environments. Moreover, current continental-scale models depend upon machine learning and therefore face additional challenges due to limited acoustic training data. In previous work, an ensemble of machine learning models was used to predict environmental sound levels in the contiguous United States using a training set composed of 51 geospatial layers (downselected from 120) and acoustic data from 496 geographic sites from Pedersen, Transtrum, Gee, Lympany, James, and Salton [JASA Express Lett. 1(12), 122401 (2021)]. In this paper, the downselection process, which is based on factors such as data quality and inter-feature correlations, is described in further detail. To investigate additional dimensionality reduction, four different feature selection methods are applied to the 51 layers. Leave-one-out median absolute deviation cross-validation errors suggest that the number of geospatial features can be reduced to 15 without significant degradation of the model's predictive error. However, ensemble predictions demonstrate that feature selection results are sensitive to variations in details of the problem formulation and, therefore, should elicit some skepticism. These results suggest that more sophisticated dimensionality reduction techniques are necessary for problems with limited training data and different training and testing distributions.

Automatic classification and reduction of wind noise in spectral data.

Outdoor acoustic data often include non-acoustic pressures caused by atmospheric turbulence, particularly below a few hundred Hz in frequency, even when using microphone windscreens. This paper describes a method for automatic wind-noise classification and reduction in spectral data without requiring measured wind speeds. The method finds individual frequency bands matching the characteristic decreasing spectral slope of wind noise. Uncontaminated data from several short-timescale spectra can be used to obtain a decontaminated long-timescale spectrum. This method is validated with field-test data and can be applied to large datasets to efficiently find and reduce the negative impact of wind noise contamination.

Validating two geospatial models of continental-scale environmental sound levels.

Modeling outdoor environmental sound levels is a challenging problem. This paper reports on a validation study of two continental-scale machine learning models using geospatial layers as inputs and the summer daytime A-weighted L50 as a validation metric. The first model was developed by the National Park Service while the second was developed by the present authors. Validation errors greater than 20 dBA are observed. Large errors are attributed to limited acoustic training data. Validation environments are geospatially dissimilar to training sites, requiring models to extrapolate beyond their training sets. Results motivate further work in optimal data collection and uncertainty quantification.