Inference and Control in Routing Games
SessionInvited Talks 4
Presenter
Event Type
Invited Talk
TimeWednesday, November 15th4:15pm -
5pm
LocationMile High Ballroom
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.
