Authors: Prashant Borkar; M.V. Sarode; Latesh Malik
Addresses: Department of Computer Science and Engineering, GH Raisoni College of Engineering, Nagpur, 440016, India ' Department of Computer Science and Engineering, JCOET, Yawatmal, 445001, India ' Department of Computer Science and Engineering, GH Raisoni College of Engineering, Nagpur, 440016, India
Abstract: In developing regions like Asia, where the traffic conditions are chaotic and non-lane driven, the intrusive techniques may be inapplicable. The vehicular acoustic signals and the occurrence and mixture weighting of these signals are determined by the prevalent traffic density state condition. This research work considers the problem of vehicular traffic density state estimation, based on the information present in the acoustic signal acquired from roadside-installed microphone. In this work a visual analytic for consideration of frame size and shift size, while extracting feature vectors using Mel Frequency Cepstral Coefficients (MFCC) for traffic density state estimation and corresponding experimental validation is provided. Different kernel functions of support vector machine (SVM) from single acoustic frame to multiple contiguous frames were used to classify the density state as low, medium and heavy. The system results in enhanced classification performance when observed time increases or when multiple contiguous frames were considered.
Keywords: acoustics; vehicle noise; support vector machines; SVM; acoustic signature; vehicular traffic density; state estimation; frame size; shift size; feature extraction; classification performance.
International Journal of Vehicle Noise and Vibration, 2016 Vol.12 No.1, pp.77 - 100
Available online: 29 Jun 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article