  Crash events identification and prediction plays a vital role in
understanding safety conditions for transportation systems. While existing
systems use traffic parameters correlated with crash data to classify and train
these models, we propose the use of a novel sensory unit that can also
accurately identify crash events: microphone. Audio events can be collected and
analyzed to classify events such as crash. In this paper, we have demonstrated
the use of a deep Convolutional Neural Network (CNN) for road event
classification. Important audio parameters such as Mel Frequency Cepstral
Coefficients (MFCC), log Mel-filterbank energy spectrum and Fourier Spectrum
were used as feature set. Additionally, the dataset was augmented with more
sample data by the use of audio augmentation techniques such as time and pitch
shifting. Together with the feature extraction this data augmentation can
achieve reasonable accuracy. Four events such as crash, tire skid, horn and
siren sounds can be accurately identified giving indication of a road hazard
that can be useful for traffic operators or paramedics. The proposed
methodology can reach accuracy up to 94%. Such audio systems can be implemented
as a part of an Internet of Things (IoT) platform that can complement
video-based sensors without complete coverage.
