  We explore the use of deep learning and deep reinforcement learning for
optimization problems in transportation. Many transportation system analysis
tasks are formulated as an optimization problem - such as optimal control
problems in intelligent transportation systems and long term urban planning.
Often transportation models used to represent dynamics of a transportation
system involve large data sets with complex input-output interactions and are
difficult to use in the context of optimization. Use of deep learning
metamodels can produce a lower dimensional representation of those relations
and allow to implement optimization and reinforcement learning algorithms in an
efficient manner. In particular, we develop deep learning models for
calibrating transportation simulators and for reinforcement learning to solve
the problem of optimal scheduling of travelers on the network.
