Mining Skier Transportation Patterns From Ski Resort Lift Usage Data

Abstract

Descriptive data analysis is used for mining spatial and temporal patterns from ski lift entrance data. The data, collected through radio-frequency identification scanners, cover one skiing season with approximately 1.2 million recorded ski lift transportations. Cluster analysis was performed on ten subsamples, and the obtained clusters were cross-validated. Several types of skier behavior were found. Temporal clustering revealed that skier patterns differ according to time of maximal performance and length of stay in the ski lift transportation system. Spatial clustering revealed that it is reasonable to have as many clusters as ski lifts in a ski resort, since skiers tend to choose a dominant ski lift during a skier-day. The detected patterns reveal valuable information that can be used for potential improvement of products and services offered by ski resorts.