Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University) · 2014 · 11 citations · 2 references
ManagementHospitality ManagementTourism ManagementEvent ManagementTourismTravel Demand ModelingDiffusion ProcessesTourism AttractionsTourist FlowsTravel BehaviorTravel BehaviourVisitor ManagementCross-validationCross-validation StudiesDiffusion ModelsMachine Learning In TourismTourist Behavior AnalyticsTourist Flow ModellingTravel Behavior ModelingVisitor Use Modeling
In this work, we focus on the novel application of learn-ing the diffusion dynamics of visitors among attractions at a large theme park using only aggregate information about waiting times at attractions. Main contributions include for-mulating optimisation models to compute diffusion dynam-ics. We also developed algorithm capable of dealing with noise in the data to populate parameters in the optimization model. We validated our approach using cross validation on a real theme park data set. Our approach provides an accu-racy of about 80 % for popular attractions, providing solid empirical support for our diffusion models.
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