A Deep Learning and Image Content Analysis enabled approach for Sustainable Transportation

Traffic accidents and congested roads constitute a pervasive and concerning global issue, contributing substantially to numerous fatal incidents on a worldwide scale. Moreover, alleviating traffic congestion is essential for improving the efficiency and safety of transportation systems. To address these challenges, modern technologies such as video surveillance and intelligent traffic systems have been widely adopted. The proposed approach leverages the power of Deep Learning to devise an advanced strategy that can automatically identify and classify different traffic scenarios based on image content. The strategy involves training a Deep Learning model on a diverse dataset that covers various traffic conditions, enabling it to learn and generalize the patterns of different scenarios. The model will be designed to recognize factors such as vehicle types, road conditions, and the presence of pedestrians or obstacles. This sustainable approach achieved an accuracy of 81% on a test set of traffic images, aiming to provide accurate and real-time evaluations of traffic situations, which can help facilitate prompt interventions and transport management strategies.