  Rising urban populations have led to a surge in vehicle use and made traffic
monitoring and management indispensable. Acoustic traffic monitoring (ATM)
offers a cost-effective and efficient alternative to more computationally
expensive methods of monitoring traffic such as those involving computer vision
technologies. In this paper, we present MVD and MVDA: two open datasets for the
development of acoustic traffic monitoring and vehicle-type classification
algorithms, which contain audio recordings of moving vehicles. The dataset
contain four classes- Trucks, Cars, Motorbikes, and a No-vehicle class.
Additionally, we propose a novel and efficient way to accurately classify these
acoustic signals using cepstrum and spectrum based local and global audio
features, and a multi-input neural network. Experimental results show that our
methodology improves upon the established baselines of previous works and
achieves an accuracy of 91.98% and 96.66% on MVD and MVDA Datasets,
respectively. Finally, the proposed model was deployed through an Android
application to make it accessible for testing and demonstrate its efficacy.
