Traffic Density Classification Using Sound Datasets: An Empirical Study on Traffic Flow at Asymmetric Roads

Abstract

Recently, with the rapid growth of Deep Learning models for solving complicated classification problems, urban sound classification techniques have been attracted more attention. In this paper, we take an investigation on how to apply this approach for the transportation domain. Specifically, traffic density classification based on the road sound datasets, which have been recorded and preprocessed on the urban road network, is taken into account. In particular, state-of-the-art methods for analyzing and extracting sound datasets have taken into account for the classification problem of traffic flow. Consequently, this study focuses on three main processes which are: i) generating image representation for the sequences of the road sound datasets; ii) proposing a convolutional neural network model for the feature extraction; iii) adopting a hybrid approach for the classification stage by combining convolutional neural network with other machine learning models. Regarding the experiment, the road sound dataset has been collected at an urban asymmetric road with different time periods (e.g., morning and evening) in order to evaluate our proposed method. Specifically, the implementations show promising results in which the accuracies are able to achieve from 92% to 95% for classifying traffic densities with different time periods.