Hybrid Optimal Traffic Control: Combining Model-Based and Data-Driven Approaches

We study different approaches to use real-time communication between vehicles, in order to improve and to optimize traffic flow in the future. A leading example in this contribution is a virtual version of the prominent ring road experiment in which realistic, human-like driving generates stop-and-go waves. To simulate human driving behavior, we consider microscopic traffic models in which single cars and their longitudinal dynamics are modeled via coupled systems of ordinary differential equations. Whereas most cars are set up to behave like human drivers, we assume that one car has an additional intelligent controller that obtains real-time information from other vehicles. Based on this example, we analyze different control methods including a nonlinear model predictive control (MPC) approach with the overall goal to improve traffic flow for all vehicles in the considered system. We show that this nonlinear controller may outperform other control approaches for the ring road scenar io but intensive computational effort may prevent it from being real-time capable. We therefore propose an imitation learning approach to substitute the MPC controller. Numerical results show that, with this approach, we maintain the high performance of the nonlinear MPC controller, even in set-ups that differ from the original training scenarios, and also drastically reduce the computing time for online application.