Prediction of Tourism Flow with Sparse Geolocation Data

TL;DR

This paper evaluates deep learning and statistical models for predicting tourism flow using sparse geolocation data, aiming to improve visitor management and prevent overcrowding in popular areas.

Contribution

It introduces a comprehensive empirical comparison of RNNs, GNNs, Transformers, and ARIMA for tourism flow prediction with sparse geolocation data, incorporating exogenous factors.

Findings

Deep learning models outperform ARIMA in prediction accuracy.

Inclusion of geolocation trajectories improves model performance.

Modern input handling enhances prediction in sparse data scenarios.

Abstract

Modern tourism in the 21st century is facing numerous challenges. Among these the rapidly growing number of tourists visiting space-limited regions like historical cities, museums and bottlenecks such as bridges is one of the biggest. 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 overcrowding. 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.