Forecasting and Mitigating Disruptions in Public Bus Transit Services
Chaeeun Han Jose Paolo Talusan Dan Freudberg Ayan Mukhopadhyay Abhishek Dubey Aron Laszka
Public transportation systems often suffer from unexpected fluctuations in
demand and disruptions, such as mechanical failures and medical emergencies.
These fluctuations and disruptions lead to delays and overcrowding, which are
detrimental to the passengers' experience and to the overall performance of the
transit service. To proactively mitigate such events, many transit agencies
station substitute (reserve) vehicles throughout their service areas, which
they can dispatch to augment or replace vehicles on routes that suffer
overcrowding or disruption. However, determining the optimal locations where
substitute vehicles should be stationed is a challenging problem due to the
inherent randomness of disruptions and due to the combinatorial nature of
selecting locations across a city. In collaboration with the transit agency of
Nashville, TN, we address this problem by introducing data-driven statistical
and machine-learning models for forecasting disruptions and an effective
randomized local-search algorithm for selecting locations where substitute
vehicles are to be stationed. Our research demonstrates promising results in
proactive disruption management, offering a practical and easily implementable
solution for transit agencies to enhance the reliability of their services. Our
results resonate beyond mere operational efficiency: by advancing proactive
strategies, our approach fosters more resilient and accessible public
transportation, contributing to equitable urban mobility and ultimately
benefiting the communities that rely on public transportation the most.