  Mobility service route design requires demand information to operate in a
service region. Transit planners and operators can access various data sources
including household travel survey data and mobile device location logs.
However, when implementing a mobility system with emerging technologies,
estimating demand becomes harder because of limited data resulting in
uncertainty. This study proposes an artificial intelligence-driven algorithm
that combines sequential transit network design with optimal learning to
address the operation under limited data. An operator gradually expands its
route system to avoid risks from inconsistency between designed routes and
actual travel demand. At the same time, observed information is archived to
update the knowledge that the operator currently uses. Three learning policies
are compared within the algorithm: multi-armed bandit, knowledge gradient, and
knowledge gradient with correlated beliefs. For validation, a new route system
is designed on an artificial network based on public use microdata areas in New
York City. Prior knowledge is reproduced from the regional household travel
survey data. The results suggest that exploration considering correlations can
achieve better performance compared to greedy choices in general. In future
work, the problem may incorporate more complexities such as demand elasticity
to travel time, no limitations to the number of transfers, and costs for
expansion.
