Adoptive Neuro-Fuzzy Inference System for Traffic Noise Prediction

Abstract

An adaptive neuro-fuzzy inference system (ANFIS) is implemented to evaluate traffic noise under heterogeneous traffic conditions of Nagpur city, India. The major factors which affect the traffic noise are traffic flow, vehicle speed and honking. These factors are considered as input parameters to ANFIS model for traffic noise estimation. The proposed ANFIS model has implemented for traffic noise estimation at eight locations. The results have been compared and analyzed with observed noise levels and the coefficient of co-relation between observed and predicted noise level was found to be in range of 0.70 to 0.95. The model performance has also been compared with Federal Highway Administration (FHWA), Calculation of road traffic noise (CRTN) and regression noise models and it is observed that the model performs better than conventional statistical noise model. The proposed noise model is completely generalized and problem independent so it can be easily modified to prediction traffic noise under various traffic criteria and serve as first hand tool for traffic noise assessment. General Terms Back propagation algorithm KeywordsHeterogeneous traffic, adaptive neural network based fuzzy inference system, noise prediction, honking.

Assessment of honking impact on traffic noise in urban traffic environment of Nagpur, India

“…Honking is a frequent phenomenon in Indian road context therefore it was observed that honking has significant impact on traffic noise besides traffic volume and vehicular speed. Previous studies also confirmed the effect of honking on traffic noise [ 18 , 21 , 26 , 29 , 30 ] and used as one of the input parameter in traffic noise prediction [ 31 , 32 ]. These studies do not provide quantification of honking noise in heterogeneous traffic while present research provides quantification of noise due to honking based on frequency analysis of traffic noise.…”

Application of the ANFIS model in road traffic and transportation: A literature review from 1993 to 2018

“…Since traffic is an important source of noise, Sharma et al (2014) present an ANFIS model for predicting the value of the mentioned variable. Vehicle speeds, traffic flows and the use of siren can be listed as the main influencing factors.…”

Development of an ANFIS Model for the Optimization of a Queuing System in Warehouses

“…This involves reading out external data on the current state of the intersection, and forwarding them to the model that processes them and reacts in accordance with the learned rules [22]. From an ecological point of view, it is possible to estimate the noise level, as indicated in [23]. Traffic flow density, vehicle speed, and the noise level of horns can be taken as input independent variables.…”