A Network Traffic Model for the Control of Autonomous Vehicles Acting as Moving Bottlenecks

In this work we present a traffic model to simulate network-level traffic evolution under the impact of controlled autonomous vehicles acting as moving bottlenecks. We first extend the Newell-Daganzo method to track the trajectories of moving bottlenecks and calculate the cumulative number of vehicles passing each moving bottleneck. By integrating the solutions to the cumulative number of vehicles passing moving bottlenecks and link nodes as boundary conditions in the link-transmission model, we can incorporate the impact of moving bottlenecks into the flow of traffic at a network scale. We present numerical simulation results that illustrate the effectiveness of the developed model to track the trajectories of the moving bottlenecks and simulate their impact on freeway traffic. Lastly, we present control applications of the developed model to trajectory optimization. The reduced fuel consumption associated with the careful control of AV trajectories in the moving bottleneck framework indicates the potential to considerably improve the flow of traffic by controlling the AVs in a mixed human and autonomous environment.