Traffic Density Investigation & Road Accident Analysis in India using Deep Learning

Abstract

Traffic congestion is a common affair in the big cities and towns. This issue is the outcome of the rapid increase in the population and increasing number of vehicles, so predicting the level of traffic congestion will be beneficial for every individual. However, interpretation and implementation of traffic state can be exceptionally tough. With this pace of increasing vehicles, existing algorithms may come up with some limitations due to various aspects of features which we cannot process. In this paper, we introduce a Hybrid Deep Neural Network (HDNN) for forecasting the traffic conditions on roads with the images using Convolutional Neural Network (CNN) and predicting road accident statistics of a particular area on a specific time. This model will exploit the development of algorithms in machine learning and majorly grasping over the Deep learning algorithm CNN. Experimental results show superior results of traffic conditions prediction and road accidentsanalysis, HDNN outshine the standard benchmark for the level of traffic congestion.