  A fast recovery from disruptions is of vital importance for the reliability
of transit systems. This study presents a new attempt to tackle the transit
disruption mitigation problem in a comprehensive and hierarchical way. A
network level strategy selection optimization model is formulated as a joint
routing and resource allocation (nJRRA) problem. By constraining the problem
further into an epsilon-constrained nJRRA problem, classic solution algorithms
can be applied to solve the quadratically constrained quadratic program (QCQP).
On top of this "basic model", we propose adding a decision to delay the
resource allocation decisions up to a maximum initiation time when the incident
duration is stochastic. To test the models, a quasi-dynamic evaluation program
with a given incident duration distribution is constructed using discretized
time steps and discrete distributions. Five different demand patterns and four
different disruption duration distributions (20 combinations) are tested on a
toy transit network. The results show that the two models outperform benchmark
strategies such as using only line level adjustment or only bus bridging. They
also highlight conditions when delaying the decision is preferred.
