DescriptionThis talk presents inference, control, and game-theoretic algorithms developed to improve traffic flow in transportation networks, implemented on HPC platforms. First, traffic estimation algorithms using crowdsourced mobile data are presented. These rely on applications of convex optimization to inverse modeling problems involving partial differential equations (PDEs). The implementation of these algorithms on mobile phones increased the accuracy of traffic information. Second, the talk presents algorithms to control transportation infrastructure assets (metering lights, traffic lights in the arterial networks, variable speed limits, etc.). These algorithms rely on adjoint-based optimization of PDEs in discretized form. Finally, we investigate disruptions in demand due to the rapid expansion of the use of “selfish routing” apps. These disruptions cause congestion and make traditional approaches of traffic management less effective. Game theoretic approaches to demand modeling are presented. These models encompass heterogeneous users (some using routing information, some not) that share the same network and compete for the same commodity (capacity). Results will be presented for static loading, based on Nash-Stackelberg games, and in the context of repeated games, to account for the fact that routing algorithms learn the dynamics of the system over time when users change their behavior. HPC implementations on the NERSC cluster at LBNL will be used to demonstrate the ability to scale up algorithms for the entire LA Basin or the City of Chicago, using a parallel version of the Frank-Wolfe algorithm.