Deep Reinforcement Learning for Dynamic Urban Transportation Problems

Anonymous

Abstract: 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. The models often used to represent the dynamics of a transportation system involve large data sets with complex input-output interactions and are difficult to use in the context of optimization. We explore the use of deep learning and deep reinforcement learning for such optimization problems in transportation. Use of deep learning meta-models 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 reinforcement learning to solve the problem of optimal scheduling of travelers on the network.

TL;DR: We explore the use of deep learning and deep reinforcement learning for optimization problems in transportation.

Keywords: deep reinforcement learning, transportation optimization