Professor Trogdon and his collaborator used random matrix theory to model the New York subway system (MTA). Much of random matrix theory concerns understanding spacing distributions of the eigenvalues. These distributions have been found to accurately describe spacings in real-world systems. They are able to use random matrix theory to describe stops within the MTA system where train arrivals are more regularly spaced, and efficient. There also exists stops within the MTA where statistics are close to Poissonian, loosely meaning that the probability of a train arriving in the next 5 minutes is the same regardless of time you have waited at the station --- an undesirable feature!