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.

本文提出了一种名为SCAIR的策略和关注群体的行程建议算法，利用马尔科夫决策过程模型和状态编码机制对线性时间进行实时规划和分配，从而优化了多个用户排队和拥挤问题，处理了自私路由问题，并证明了其在主题公园数据集上超越了其他现有算法。

行程规划中优化群体效应：一种战略且关注群众的方法