A Review of Traffic State Prediction (TSP) Methods in Intelligent Transportation Systems (ITS)

Abstract

In today's world, traffic congestion is a major problem in almost all metropolitans. This problem is even becoming more crucial due to increasing numbers of vehicles. Mobility of people, travel time duration, quality of life, transportation planning systems and traffic management are examples which bear the effects of traffic congestion The modern smart technology such as Artificial Intelligence (AI) has reduced traffic congestion by improving traffic monitoring and management technologies. These technologies require sufficient and accurate traffic data such as flow, velocity, and traffic density. Several machine learning-based methods have been proposed to predict the traffic state. Providing accurate prediction is an important stage in the successful implementation of Intelligent Transportation Systems (ITS). In this paper, we summarize the latest approaches in enhancing traffic state prediction, and possible developments in future, which potentially can transform many aspects of traffic management.