Network Traffic Prediction for Intelligent Transportation Systems: A Reinforcement Learning Approach

Abstract

Vehicular Ad-Hoc Networks (VANETs), as the cru-cial support of Intelligent Transportation Systems (ITS), have received a great attention in recent years. Network traffic prediction is useful for network management and security in VANETs, such as network planning and anomaly detection. Due to the movement of nodes, the traffic flow in VANETs consists of a great number of irregular fluctuations, which is the main challenge for network traffic prediction. This paper proposes a novel algorithm, which combines Deep Q-Learning (DQN) and Generative Adversarial Networks (GAN) for network traffic prediction. We use DQN to carry out network traffic prediction, in which GAN is involved to represent Q-network. Meanwhile, the generative network can increase the number of samples to improve the prediction error. We evaluate the performance of our method by implementing it on two real network traffic data sets. Finally, we compare the two state-of-the-art competing methods with our method.