Abstract:
Traffic congestion can affect different socio-economic aspects of a country. In order to
perform better than traditional human effort in traffic control, an automated system must
be developed. For reducing the traffic congestion, traffic conditions classification can be
considered as the first way to monitor the traffic control system. So, detection of traffic
conditions is crucial for building a smart traffic control system to prevent traffic jam
escalation. Deep neural network is the area of machine learning that is being widely used
for interpreting and analyzing visual data. This branch has an extensive use over image
classification. So, modern deep learning approaches can help in detection of road traffic
conditions especially Convolutional Neural Network (CNN). We suggest a novel model
in this paper that can classify road traffic conditions using CNN. Our proposed model
‘TrafficNN’ classifies five different road traffic conditions with an accuracy of 82%. To
train and test our model, we use our own traffic conditions images dataset. To verify the
efficiency of our model, we compare it with several pre-trained models like- VGG16,
ResNet50, InceptionV3 and DenseNet121. The comparison result proves the significance
of our model to extend its successful application for developing an automated traffic
controlling system in the near future.