  There is a rapidly growing demand for itinerary planning in tourism but this
task remains complex and difficult, especially when considering the need to
optimize for queuing time and crowd levels for multiple users. This difficulty
is further complicated by the large amount of parameters involved, i.e.,
attraction popularity, queuing time, walking time, operating hours, etc. Many
recent works propose solutions based on the single-person perspective, but
otherwise do not address real-world problems resulting from natural crowd
behavior, such as the Selfish Routing problem, which describes the consequence
of ineffective network and sub-optimal social outcome by leaving agents to
decide freely. In this work, we propose the Strategic and Crowd-Aware Itinerary
Recommendation (SCAIR) algorithm which optimizes social welfare in real-world
situations. We formulate the strategy of route recommendation as Markov chains
which enables our simulations to be carried out in poly-time. We then evaluate
our proposed algorithm against various competitive and realistic baselines
using a theme park dataset. Our simulation results highlight the existence of
the Selfish Routing problem and show that SCAIR outperforms the baselines in
handling this issue.
