Interpolating Urban Tourists’ Itineraries Based on Low-Fidelity Positioning Data and Domain Knowledge

Abstract

This paper proposed and tested an interpolation method that took advantage of low-fidelity passive positioning data, such as mobile phone (MP) data, to estimate urban tourist visits to tourism sites, which utilized the domain knowledge of regularities in tourist behavior and preferences. The central component of the approach was a model of tourists choosing itineraries. A dataset of 80 Shanghai (China) tourist trajectories was collected through simultaneous MP and global positioning system (GPS) tracking. The results showed that the itinerary choice models that used both types of tracking data were quite similar and behaviorally reasonable. The most important finding was that visits that were estimated with the MP data and model were as accurate as those that were estimated via the GPS data and model.