Modelling road traffic Noise under heterogeneous traffic conditions using the graph-theoretic approach

Abstract

A traffic noise system involves several subsystems like road traffic subsystem, human subsystem, environment subsystem, traffic network subsystem, and urban prosperity subsystem. The study’s main aim was to develop road traffic noise models using a graph theory approach involving the parameters related to road traffic subsystem. The road traffic subsystem variables selected for the modeling purposes included vehicular speed, traffic volume, carriageway width, number of heavy vehicles, and number of honking events. The interaction of the selected variables considered in the form of permanent noise function is given in the matrix form. Eigenvalues and corresponding eigenvectors are calculated for removing any human judgmental error. The permanent noise function matrix was then updated using the eigenvectors, which was ultimately utilized for obtaining the permanent noise index. Data regarding the selected variables were collected for three months, and the noise parameters included in the study were equivalent noise level (L eq,1h ), maximum noise level (L 10,1h ), and background noise level (L 90,1h ). A logarithmic transformation was applied to the permanent noise index and linear regression models were developed for L eq,1h , L 90,1h , and L 10,1h respectively. The models were validated using the data collected from the same locations for nine months. The models were found to provide satisfactory results, although the results were somewhat overestimated. The method can prove beneficial for estimating future noise levels, given the expected changes in values for the independent variables considered in the study.

Predicting traffic induced noise using artificial neural network and multiple linear regression approach

“…According to Gilani and Mir (2021) the traffic noise system is composed of the road traffic subsystem, the human subsystem, the environment subsystem, the traffic network subsystem [6]. The type of fuel has a significant impact on noise generation.…”

Vehicular traffic noise modelling of urban area—a contouring and artificial neural network based approach

“…Neural Network (ANN) (Cammarata et al 1995), time series models including autoregressive models (ARIMA) (Hamed et al 1995), deep learners (Lv et al 2015), tensor completion (Tan et al 2016), pattern discovery (Habtemichael and Cetin 2016), space-temporal correlations (Cai et al 2016), Bayesian approach (Wang et al 2014) and graph-theoretic approaches (Gilani and Mir 2021c) to cater road conditions in the respective countries which establishes relation between variables with fairly good results. ANNs are used extensively in the fields ranging from finance to medicine, engineering and science due to accurate predictivity and definite relationship between dependent and independent variables unless it found to be more complex with traditional techniques of correlations and group differences (Givargis and Karimi 2010).…”

Prediction and environmental appraisal of traffic noise intensity by auto-regressive integrated moving average technique

“…Also, faulty vehicles and defective mufflers contribute to this TNI, and these put together adversely affect the roadside dwellers in cities around the world. Road traffic noise models have been developed for a heterogeneous traffic condition by using a graph theory approach deploying some selected parameters related to road traffic systems and subsystems [5]. In this work, the interaction and consistency between traffic volume, carriageway dimensions, number of heavy vehicles, speed of vehicles, and frequency of honking events were studied for a period of 3 months and validated with 9-month data from the surrounding environment [5].…”