Crowd-aware itinerary recommendation: a game-theoretic approach to optimize social welfare
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The demand for Itinerary Planning grows rapidly in recent years as the economy and standard of living are improving globally. Nonetheless, itinerary recommendation remains a complex and difficult task, especially for one that is queuing time- and crowd-aware. This difficulty is due to the large amount of parameters involved, i.e., attraction popularity, queuing time, walking time, operating hours, etc. Many recent or existing works adopt a data-driven approach and propose solutions with single-person perspectives, but do not address real-world problems as a result of 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 takes a game-theoretic approach to address the Selfish Routing problem and optimize social welfare in real-world situations. To address the NP-hardness of the social welfare optimization problem, we further propose a Markov Decision Process (MDP) approach which enables our simulations to be carried out in poly-time. We then use real-world data to evaluate the proposed algorithm, with benchmarks of two intuitive strategies commonly adopted in real life, and a recent algorithm published in the literature. Our simulation results highlight the existence of the Selfish Routing problem and show that SCAIR outperforms the benchmarks in handling this issue with real-world data.
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