Vehicular Traffic Density State Estimation Based on Cumulative Road Acoustics

Abstract

This paper considers the problem of vehicular traffic density estimation, utilizing the information cues present in the cumulative acoustic signal acquired from a roadside-installed single microphone. This cumulative signal comprises several noise signals such as tire noise, engine noise, engine-idling noise, occasional honks, and air turbulence noise of multiple vehicles. The occurrence and mixture weightings of these noise signals are determined by the prevalent traffic density conditions on the road segment. For instance, under a free-flowing traffic condition, the vehicles typically move with medium to high speeds and thereby produce mainly tire noise and air turbulence noise and less engine-idling noise and honks. For slow-moving congested traffic, the cumulative signal will largely be dominated by engine-idling noise and honks; air turbulence and tire noises will be inconspicuous. Furthermore, these various noise signals have spectral content that are very different from each other and, hence, can be used to discriminate between the different traffic density states that lead to them. Therefore, in this work, we extract the short-term spectral envelope features of the cumulative acoustic signals and model their class-conditional probability distributions, conditioned on one of the three broad traffic density states, i.e., Jammed (0-10 km/h), Medium-Flow (10-40 km/h), and Free-Flow (40 km/h and above) traffic. While these states are coarse measures of the average traffic speed, they nevertheless can provide useful traffic density information in the often-chaotic and nonlane-driven traffic conditions of the developing geographies, where other techniques (magnetic loop detectors) are inapplicable. Based on these learned distributions, we use a Bayes' classifier to classify the acoustic signal segments spanning a duration of 5-30 s, which results in a high classification accuracy of ~95%. Using a discriminative classifier such as a support vector machine (SVM) results in further classification accuracy gains over the Bayes' classifier.