Deep Koopman Traffic Modeling for Freeway Ramp Metering

Ramp metering has been considered as one of the most effective approaches of dealing with the traffic congestion on the freeways. The modelling of the freeway traffic flow dynamics is challenging because of its non-linearity and uncertainty. Recently, Koopman operator, which transfers a non-linear system to a linear system in an infinite-dimensional space, has been studied for modelling complex dynamics. In this paper, we propose a data-driven modelling approach based on neural networks, denoted by deep Koopman model, to learn a finite-dimensional approximation of the Koopman operator. To consider the sequential relations of the ramps and main roads on the freeway, a long short-term memory network is applied. Furthermore, a model predictive controller with the trained deep Koopman model is proposed for the real-time control of the ramp metering on the freeway. To validate the performance of the proposed approach, experiments based on the simulation in the traffic simulation software Simulation of Urban MObility (SUMO) environment are conducted. The results demonstrate the effectiveness of the proposed approach on both the dynamics prediction and the real-time control of the ramp metering.