Cumulative Acoustic Signal Based Traffic Density State Estimation

Based on the information present in cumulative acoustic signal acquired from a roadside-installed single microphone, this paper considers the problem of vehicular traffic density state estimation. The occurrence and mixture weightings of traffic noise signals (Tyre, Engine, Air Turbulence, Exhaust, and Honks etc) are determined by the prevalent traffic density conditions on the road segment. In this work, we extract the short-term spectral envelope features of the cumulative acoustic signals using LPC (Linear Predictive Coding). Support Vector Machines (SVM) is used as classifier is used to model the traffic density state as Low (40 Km/h and above), Medium (20-40 Km/h), and Heavy (0-20 Km/h). For the developing geographies where the traffic is non-lane driven and chaotic, other techniques (magnetic loop detectors) are inapplicable. SVM classifier with different kernels are used to classify the acoustic signal segments spanning duration of 20-40 s, which results in average classification accuracy of 98.33% and 96.67% for quadratic and polynomial kernel functions respectively.