  Increasing urban concentration raises operational challenges that can benefit
from integrated monitoring and decision support. Such complex systems need to
leverage the full stack of analytical methods, from state estimation using
multi-sensor fusion for situational awareness, to prediction and computation of
optimal responses. The FASTER platform that we describe in this work, deployed
at nation scale and handling 1.5 billion public transport trips a year, offers
such a full stack of techniques for this large-scale, real-time problem. FASTER
provides fine-grained situational awareness and real-time decision support with
the objective of improving the public transport commuter experience. The
methods employed range from statistical machine learning to agent-based
simulation and mixed-integer optimization. In this work we present an overview
of the challenges and methods involved, with details of the commuter movement
prediction module, as well as a discussion of open problems.
