Abstract: Traffic congestion occurs when the number of the vehicles increases more than the existing space of the road. This deleterious problem is increasing at an alarming rate in the whole world. For any effective Intelligent Transportation System, early detection of traffic congestion is very important to take corrective action. Several techniques have been developed to detect traffic congestion, most of which are infrastructure based. Even though these techniques are widely used, but they have many downsides as well. They require large capital input for installation as well as for maintenance. In this paper, we propose an efficient and cost-effective method using smartphones to determine the traffic state of the road. The acoustic data collected from commuter’s smartphone is segmented into fixed size frames. Various time and frequency based features such as (MFCC, Delta & Delta-Delta, ZCR, STE, and RMS) are extracted from each frame and used for detecting traffic state as ’busy street’ or ’quiet street’. We have compared the accuracy of two classifiers Support Vector Machines and Neural Network by using acoustic data collected from 320 different recording sessions. Experiments have shown that feature set having features MFCC, STE and RMS, results in better classification accuracy of 91.8% with Neural Network and 93% with SVM. Furthermore, various relevant factors affecting the classification accuracy are also tested like frame size, window functions, overlapping size and different combination of features. The frame size of 8192 and hamming window function proved to be more efficient than others.
