Generative Adversarial Networks for Parallel Transportation Systems

Abstract

Generative Adversaria Networks (GANs) have emerged as a promising and effective mechanism for machine learning due to its recent successful applications. GANs share the same idea of producing, testing, acquiring, and utilizing data as well as knowledge based on artificial systems, computational experiments, and parallel execution of actual and virtual scenarios, as outlined in the theory of parallel transportation. Clearly, the adversarial concept is embedded implicitly or explicitly in both GANs and parallel transportation systems. In this article, we first introduce basics of GANs and parallel transportation systems, and then present an approach of using GANs in parallel transportation systems for traffic data generation, traffic modeling, traffic prediction and traffic control. Our preliminary investigation indicates that GANs have a great potential and provide specific algorithm support for implementing parallel transportation systems.