  Itinerary recommendation is a complex sequence prediction problem with
numerous real-world applications. This task becomes even more challenging when
considering the optimization of multiple user queuing times and crowd levels,
as well as numerous involved parameters, such as attraction popularity, queuing
time, walking time, and operating hours. Existing solutions typically focus on
single-person perspectives and fail to address real-world issues resulting from
natural crowd behavior, like the Selfish Routing problem. In this paper, we
introduce the Strategic and Crowd-Aware Itinerary Recommendation (SCAIR)
algorithm, which optimizes group utility in real-world settings. We model the
route recommendation strategy as a Markov Decision Process and propose a State
Encoding mechanism that enables real-time planning and allocation in linear
time. We evaluate our algorithm against various competitive and realistic
baselines using a theme park dataset, demonstrating that SCAIR outperforms
these baselines in addressing the Selfish Routing problem across four theme
parks.
