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

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. Tourists generate itineraries during their trips. Therefore, monitoring and understanding the itineraries is important for tourism planning and management. The universal mobile phone (MP) positioning data are valuable resources when collecting tourist itineraries. However, a major weakness of the data is the low positioning fidelity. Its position might deviate from an individual's true location by hundreds of meters or even kilometers, and the frequency of the data logging could be hours apart, which significantly limits the quality of the collected itineraries. An approach was developed to more reliably estimate urban tourist itineraries that was based on MP data, which considered the tourists' behavioral characteristics and tour preferences. The approaches could generate itineraries as accurately as when the high-fidelity GPS data were used, and the estimated visits to the tourism sites agreed moderately with the actual visits. The approaches could be further developed to continuously monitor urban tourist activities and provide a relatively low-cost and high-efficiency tool for tourism practices which utilizes MP data better. [ABSTRACT FROM AUTHOR]