Identification and modeling of stop activities at the destination from GPS tracking data

This paper aims at analysing tourist behaviour at destination by focusing on the main determinants of their stop activities. A density-based cluster algorithm identifies the stops from GPS tracking data on cruise passengers starting from data on individual trajectories. A Poisson regression model analyses the effects of socio-demographic, and itinerary characteristics on the number of stops made. The results are of interest both from a methodological perspective, related to the analysis and synthesis of GPS tracking data and from an applied perspective concerning tourists' knowledge of spatial behaviour and its implications for destination management.