PREDICTION OF TOURISM FLOW WITH SPARSE DATA INCORPORATING TOURIST GEOLOCATIONS

Julian Lemmel, Zahra Babaiee, Marvin Kleinlehner, Ivan Majic, Philipp Neubauer, Johannes Scholz, Radu Grosu, Sophie Neubauer

Keywords: GNN, RNN, Transformer, Tourism, Tourism flow prediction

Abstract:

Modern tourism in the 21st century is facing numerous challenges. One of these
challenges is the rapidly growing number of tourists in space-limited regions such
as historical city centers, museums, or geographical bottlenecks like narrow val-
leys. In this context, a proper and accurate prediction of tourism volume and
tourism flow within a certain area is important and critical for visitor management
tasks such as sustainable treatment of the environment and prevention of over-
crowding. Static flow control methods like conventional low-level controllers or
limiting access to overcrowded venues could not solve the problem yet. In this
paper, we empirically evaluate the performance of state-of-the-art deep-learning
methods such as RNNs, GNNs, and Transformers as well as the classic statistical
ARIMA method. Granular limited data supplied by a tourism region is extended
by exogenous data such as geolocation trajectories of individual tourists, weather
and holidays. In the field of visitor flow prediction with sparse data, we are thereby
capable of increasing the accuracy of our predictions, incorporating modern input
feature handling as well as mapping geolocation data on top of discrete POI data.

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TL;DR: We apply state-of-the-art deep learning models such as GNNs, RNNs and Transformers to the problem of Tourism flow predictions