A critical review of some traffic noise prediction models

In India, the transportation sector is growing rapidly and the number of vehicles on Indian roads is increasing at a very fast rate leading to overcrowded roads and noise pollution. The traffic scenario is typically different from other countries due to predominance of a variety of two-wheelers which has doubled in the last decade and forms a major chunk of heterogeneous volume of vehicles. Also tendency of not following the traffic norms and poor maintenance adds to the noise generation. In the present study, Multilayer feed forward back propagation (BP) neural network has been trained by Levenberg–Marquardt (L–M) algorithm to develop an Artificial Neural Network (ANN) model for predicting highway traffic noise. The developed ANN model is used to predict 10 Percentile exceeded sound level (L10) and Equivalent continuous sound level (Leq) in dB (A). The model input parameters are total vehicle volume/hour, percentage of heavy vehicles and average vehicle speed. The predicted highway noise descriptors, Leq and L10 from ANN approach and regression analysis have also been compared with the field measurement. The results show that the percentage difference is much less using ANN approach as compared to regression analysis. Further goodness-of-fit of the models against field data has been checked by statistical t-test at 5% significance level and proved the Artificial Neural Network (ANN) approach as a powerful technique for traffic noise modeling.

Vehicular traffic noise modeling using artificial neural network approach

This paper relies on vehicle trajectory collection on a corridor, to compare different traffic representations used for the estimation of the sound power of light vehicles and the resulting sound pressure levels. Four noise emission models are tested. The error introduced when the emissions are calculated based on speeds measured at regular intervals along the road network are quantified and explained. The current noise emission models might in particular misestimate noise levels under congestion. This bias can be reduced by introducing additional traffic variables in the modeling. In addition, significant differences within the models are highlighted, especially concerning their accounting of vehicle accelerations. Models that rely on a binary representation of acceleration regimes (a vehicle or a road segment is accelerating or not) can lead to errors in practice. Models under use in Europe have a very low sensitivity to acceleration values. These results help underlying the further required improvements of dynamic road traffic noise models.

Estimation of road traffic noise emissions: the influence of speed and acceleration

Autoregressive integrated moving averages (arima) modelling of a traffic noise time series

Purpose
Several damages have been associated with the exposure of human beings to noise. These include auditory effects, i.e., hearing impairment, but also non-auditory physiological ones such as hypertension and ischemic heart disease, or psychological ones such as annoyance, depression, sleep disturbance, limited performance of cognitive tasks or inadequate cognitive development. Noise can also interfere with intended activities, both in daytime and nighttime. ISO 14'040 also indicated the necessity of introducing noise, together with other less developed impact categories, in a complete LCA study, possibly changing the results of many LCA studies already available. The attempts available in the literature focused on the integration of transportation noise in LCA. Although being considered the most frequent source of intrusive impact, transportation noise is not the only type of noise that can have a malign impact on public health. Several other sources of noise such as industrial or occupational need to be taken into account to have a complete consideration of noise into LCA. Major life cycle inventories (LCI) typically do not contain data on noise emissions yet and characterisation factors are not yet clearly defined. The aim of the present paper is to briefly review what is already available in the field and propose a new framework for the consideration of human health impacts of any type of noise that could be of interest in the LCA practice, providing indications for the introduction of noise in LCI and analysing what data is already available and, in the form of a research agenda, what other resources would be needed to reach a complete coverage of the problem.

/pdf/towards-a-general-framework-for-including-noise-impacts-in-vn9cng9z7j.pdf

D.G. Harland

Papers

The prediction of noise levels l10 due to road traffic