An adaptive neuro-fuzzy interface system model for traffic classification and noise prediction

Abstract

In present study, two adaptive neuro-fuzzy models have been developed for traffic classification and noise prediction, respectively. The traffic classification model (ANFIS-TC) classifies extracted sound features of different categories of vehicles based on their acoustic signatures. The model also compute total number of vehicles passes through a particular sampling point. The results have been used for the estimation of the equivalent traffic flow ($Q_\mathrm{E})$. The noise prediction model (ANFIS-TNP) has three inputs, namely equivalent traffic flow ($Q_\mathrm{E})$, equivalent vehicle speed ($S_\mathrm{E})$ and honking. The equivalent traffic flow ($Q_\mathrm{E})$ is the output of ANFIS-TC model, while equivalent vehicle speed ($S_\mathrm{E})$ and honking are computed from observed averaged speed of different categories of vehicles and number of recorded horns blow per minute. The model assumes that the distance between sound level meter and road centerline is fixed for particular sampling point. The performance of both the models has been validated by field observations. The results show that traffic classification is 100% accurate, while correlation coefficients between observed and predicted traffic noise range from 0.75 to 0.96. Both the models are validated with random samples of data, and it is observed that both the models are generalized and could be employed for traffic classification and traffic noise prediction in small urban heterogeneous traffic environment for noise pollution assessment and control.