Modelling and Lagrangian control of mixed traffic: platoon coordination, congestion dissipation and state reconstruction
Time: Fri 2021-03-12 16.00
Location: zoom (English)
Subject area: Electrical Engineering
Doctoral student: Mladen Čičić , Reglerteknik
Opponent: Professor Miroslav Krstić, Department of Mechanical and Aerospace Engineering, University of California, San Diego
Supervisor: Professor Karl Henrik Johansson, Reglerteknik
Traffic congestion is a constantly growing problem, with a wide array of negative effects on the society, from wasted time and productivity to elevated air pollution and reduction of safety. The introduction of connected, autonomous vehicles enables a new, Lagrangian paradigm for sensing andcontrolling the traffic, by directly using connected vehicles inside the traffic flow, as opposed to the classical, Eulerian paradigm, which relies on stationary equipment on the road. By using control methods specifically tailored to the Lagrangian paradigm, we are able to influence the traffic flow even if the penetration rate of connected vehicle is low. This allows us to answer one of the central impending questions of the traffic control using emerging technologies: How can we influence the overall traffic by using only a smallportion of vehicles that we can control directly?
Traffic phenomena such as moving bottlenecks and stop-and-go waves are particularly pertinent to Lagrangian traffic control, and therefore need to be captured in traffic models. In this thesis we introduce the influence of these phenomena into the cell transmission model, multi-class cell transmission model, and tandem queueing model. We also propose a transition system model based on front tracking, which captures the relevant phenomena, and show under which conditions it corresponds to the Lighthill-Whitham-Richards model. Moving bottlenecks are introduced as a moving zone in which a reduced flux function describes the traffic flow, and their influence on the surrounding traffic is given by solving the Riemann problems at the flux function boundaries. Stop-and-go waves are introduced by constraining the wave speed of rarefaction, resulting in constant stop-and-go wave propagation speed and discharging flow lower than the road capacity, which is consistent with the empirical observations.
We use the proposed traffic models to design control laws that address three problems: platoon merging coordination, congestion reduction, and traffic state reconstruction. We study the case when two trucks are closing the distance and merging into a platoon on a public road, and propose an optimal control algorithm which accounts for the mutual influence between the trucks and the surrounding traffic. The proposed control law minimizes the total fuel consumption of the trucks, and improves the reliability of platooning. Then, we consider two forms of the congestion reduction problem: stationary bottleneck decongestion, and stop-and-go wave dissipation. In both cases, connected vehicles are used as moving bottlenecks to restrict the traffic flow enough to let the congestion dissipate. By applying these control laws, the throughput of the road is increased and the total travel time of all vehiclesis reduced. Finally, we generalize the stop-and-go wave dissipation problem by dropping the assumption that the full traffic state is known, and instead propose traffic state reconstruction algorithms which use local measurements originating from the connected vehicles. We show that the proposed control laws can also be implemented using the reconstructed traffic state. In this case, as the number of available connected vehicles increases, the control performance approaches the full-information control case.