An Online Approach to Solving Public Transit Stationing and Dispatch Problem

Jose Paolo Talusan, Chaeeun Han, Ayan Mukhopadhyay, Aron Laszka, Dan Freudberg, Abhishek Dubey

Abstract:

Public bus transit systems provide critical transportation services for large sections of modern communities. Ontime 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 nonmyopic 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%.