  Public bus transit systems provide critical transportation services for large
sections of modern communities. On-time performance and maintaining the
reliable quality of service is therefore very important. Unfortunately,
disruptions caused by overcrowding, vehicular failures, and road accidents
often lead to service performance degradation. Though transit agencies keep a
limited number of vehicles in reserve and dispatch them to relieve the affected
routes during disruptions, the procedure is often ad-hoc and has to rely on
human experience and intuition to allocate resources (vehicles) to affected
trips under uncertainty. In this paper, we describe a principled approach using
non-myopic sequential decision procedures to solve the problem and decide (a)
if it is advantageous to anticipate problems and proactively station transit
buses near areas with high-likelihood of disruptions and (b) decide if and
which vehicle to dispatch to a particular problem. Our approach was developed
in partnership with the Metropolitan Transportation Authority for a mid-sized
city in the USA and models the system as a semi-Markov decision problem (solved
as a Monte-Carlo tree search procedure) and shows that it is possible to obtain
an answer to these two coupled decision problems in a way that maximizes the
overall reward (number of people served). We sample many possible futures from
generative models, each is assigned to a tree and processed using root
parallelization. We validate our approach using 3 years of data from our
partner agency. Our experiments show that the proposed framework serves 2% more
passengers while reducing deadhead miles by 40%.
