My main research interests lie within the field of modeling and control of networks, with applications in transportation networks.
Feedback-based Traffic Light Control
This research aims to develop feedback control policies for traffic lights. With only information about the number of vehicles queueing up at each junction, the proposed control strategy determines both the cycle length of the upcoming cycle and how large fraction of the cycle each phase should be activated. Our control policy does not require any information about the average arrival rate, how the vehicles propagate through the network or the network topology but it is yet still able to keep the queue lengths bounded whenever any controller can do so. That the controller only needs information about the local queue lengths makes it both scalable and robust.
- G. Nilsson and G. Como. "Generalized Proportional Allocation Policies for Robust Control of Dynamical Flow Networks", Accepted for publication in IEEE Transaction of Automatic Control. Dec 2020. (preprint).
- G. Nilsson and G. Como. "A Micro-Simulation Study of the Generalized Proportional Allocation Traffic Signal Control", IEEE Transactions on Intelligent Transportation Systems 21.4,
pp. 1705–1715. Apr. 2020. (preprint).
- G. Nilsson and G. Como. "On Robustness of the Generalized Proportional Controller for
Traffic Signal Control". 2020 American Control Conference, ACC.
- G. Nilsson and G. Como, "Evaluation of Decentralized Feedback Traffic Light Control with Dynamic Cycle Length", 15th IFAC Symposium on Control in Transportation Systems, 2018, Savona, Italy.
- G. Nilsson and G. Como, “On generalized proportional allocation policies for traffic signal control”, (INVITED) IFAC World Congress 2017, Toulouse, France.
- G. Nilsson, P. Hosseini, G. Como, and K. Savla, "Entropy-like Lyapunov Functions for the Stability Analysis of Adaptive Traffic Signal Controls," (INVITED) in Proc. of IEEE Control Decision Conference, (Osaka, Japan), December 15-18, 2015.
Dynamic Routing of Multicommodity Flows
In this work, we study the dynamics when different classes of particles propagate through a shared network. The particles can, for instance, be vehicles, where each class of cars is aiming for one specific destination. The particles propagate along the links (roads) in the network, and at each node (junction) the particles decision about which link to follow next depends on the current congestion level on the outgoing links. Under the assumption that the route choices are congestion avoiding, i.e., if the congestion level increases on one outgoing link, the particle is more likely to choose one of the others outgoing links, we show that the dynamical flow network converges to a unique equilibrium. While it has previously been shown that the flow network is resilient to perturbation for a single commodity flow with these routing policies, we show that the heterogeneity of the dynamic route choice behavior may have a negative impact on the network's resilience.
- G. Nilsson, G. Como, and E. Lovisari, "On Resilience of Multicommodity Dynamical Flow Networks," in Proc. of 2014 Control Decision Conference, (Los Angeles, CA, USA), December 15-17, 2014.