  When there are significant service disruptions in public transit systems,
passengers usually need guidance to find alternative paths. This paper proposes
a path recommendation model to mitigate the congestion during public transit
disruptions. Passengers with different origin-destination and departure times
are recommended with different paths such that the system travel time is
minimized. We model the path recommendation as an optimal flow problem with
uncertain demand information. To tackle the non-analytical formulation of
travel times due to left behind, we propose a simulation-based first-order
approximation to transform the original problem into linear programming.
Uncertainties in demand are modeled with robust optimization to protect the
path recommendation strategies against inaccurate estimates. A real-world rail
disruption scenario in the Chicago Transit Authority (CTA) system is used as a
case study. Results show that even without considering uncertainty, the nominal
model can reduce the system travel time by 9.1% (compared to the status quo),
and outperforms the benchmark capacity-based path recommendation. The average
travel time of passengers in the incident line (i.e., passengers receiving
recommendations) is reduced more (-20.6% compared to the status quo). After
incorporating the demand uncertainty, the robust model can further reduce the
system travel time. The best robust model can decrease the average travel time
of incident-line passengers by 2.91% compared to the nominal model.
