Efficient Model to Predict the Traffic Congestion through Deep Learning

Abstract

Abstract: Traffic in urban centers has considered a worsening issue across the world, impacting development and people's daily lives. It also occurs in large and medium regions of India, posing a serious threat to the country's progress. Humans could gain knowledge from the administration and management of urban vehicular networks that road congestion can be ameliorated or diminished if we can anticipate road congestion which will occur within few moments or has already occurred in a few seconds and implement prompt, adequate traffic abatement methodologies. As a consequence, traffic congestion prediction is crucial for improving the transportation system's energy consumption and dependability. A variety of strategies to this problem have been investigated and discussed in this research article for this goal. This has enabled the successful implementation of our solution for a traffic congestion prediction system based on Artificial Neural Networks and Decision Making. The result analysis has been crucial in the realization of the improved accuracy of the prescribed approach. Keywords: Artificial Neural Network, Deep learning, Traffic Congestion prediction ,Preprocessing , Segarigation