Traffic Flow Prediction on Road using Machine Learning

Abstract

The Intelligent Transportation System (ITS) plays a vital role in numerous smart city applications, particularly in improving transportation and commuting processes. A primary goal of ITS is to tackle traffic-related challenges, especially the issue of traffic congestion. The prediction system for road traffic flow has significant relevance in urban transportation and area management. Many urban centers grapple with the daunting task of effective traffic management. However, the incorporation of predictive modeling that considers environmental and weather conditions, such as rainfall and thunderstorms, has proven to be remarkably effective. In response to this challenge, we have introduced a road traffic flow prediction model specifically designed to forecast traffic conditions at hourly intervals extending up to 24 hours. Although various algorithms have been applied in previous research, there is a notable absence of accessible and user-friendly platforms dedicated to road traffic flow prediction.