Calvin Lab Rm 116
Congestion in Large-Scale Transportation Networks: Analysis and Control Perspectives
Fluid-like models and their discretizations, like the Cell Transmission Model, have proven successful in modeling traffic networks. However, given the complexity of the dynamics, it is not surprising that the stability properties of these models, especially in congested regimes, are not yet well characterized. The first half of this talk will propose a new modeling paradigm where an analogy between discetized fluid-like traffic flow models and a class of chemical reaction networks is constructed by suitable relaxations of key conservation laws. This framework allows us to draw upon powerful structural results and entropy-like Lyapunov functions from chemical reaction network theory to study the existence and stability of congested steady states in networks with arbitrary toplogies. The second half of this talk will motivate compositional design approaches to mitigate congestion in large-scale transportation networks by describing a scalable distributed design that uses only local information to limit the propagation of congestion in the network.
Sivaranjani Seetharaman is a doctoral candidate in the Department of Electrical Engineering at the University of Notre Dame. She obtained her undergraduate and Masterís degrees in Electrical Engineering from the PES Institute of Technology and the Indian Institute of Science, in 2011 and 2013, respectively. Sivaranjani's research interests are in the area of distributed control for large-scale infrastructure networks, with emphasis on transportation networks and power grids. She is a recipient of the prestigious international Schlumberger Foundation Faculty for the Future fellowship (2015-present) and the Zonta International Amelia Earhart fellowship (2015-2016). She was also a Notre Dame (NSF) Ethical Leaders in STEM fellow for the year 2016-17.
Anyone who would like to give one of the weekly seminars on the RTDM program can fill in the survey at https://goo.gl/forms/Li5jQ0jm01DeYZVC3.