  We investigate the stochastic transfer synchronization problem, which seeks
to synchronize the timetables of different routes in a transit network to
reduce transfer waiting times, delay times, and unnecessary in-vehicle times.
We present a sophisticated two-stage stochastic mixed-integer programming model
that takes into account variability in passenger walking times between bus
stops, bus running times, dwell times, and demand uncertainty. Our model
incorporates new features related to dwell time determination by considering
passenger arrival patterns at bus stops which have been neglected in the
literature on transfer synchronization and timetabling. We solve a sample
average approximation of our model using a problem-based scenario reduction
approach, and the progressive hedging algorithm. As a proof of concept, our
computational experiments on two single transfer nodes in the City of Toronto,
with a mixture of low- and high-frequency routes, demonstrate the potential
advantages of the proposed model. Our findings highlight the necessity and
value of incorporating stochasticity in transfer-based timetabling models.
