PREDICTION OF TOURISM FLOW WITH SPARSE DATA INCORPORATING TOURIST GEOLOCATIONS Anonymous

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 valleys. 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.

1. INTRODUCTION

With increasing population and travel capacities (e.g. easy access to international flights) cultural tourism destinations have seen a rise in visitor counts. In addition, recent needs for social distancing and attendance limitations due to the global COVID-19 pandemic have confronted tourism destinations with significant challenges in e.g. creating and establishing sustainable treatment of the both urbanised and natural environment or e.g. preventing overcrowded waiting-lines. The perceptions of tourists regarding health hazards, safety and unpleasant tourism experiences may be influenced by social distance and better physical separation Sigala (2020). As far as The United Nation's 2030 Agenda for Sustainable Development UNWTO (2015) is concerned, tourism has not only the potential to contribute to several of the 17 Sustainable Development Goals (SDGs), but moreover an obligation. Only by establishing sustainable tourism it will be possible to create • sustainable cities and communities (Goal 11) • responsible consumption and production (Goal 12) • decent work and economic growth (Goal 8) Therefore, future-oriented tourism regions aim to first understand and then control visitor flows in order to • preserve and protect their natural landmarks • reduce emissions and waste as a result of overcrowding e.g. in parks or narrow city centers • establish sustainable energy consumption within tourist attractions • create harmony between residents and tourists • and maximise tourist satisfaction, which is directly connected to the economical wealth of the specific tourism region. Unfortunately, many real-world problems suffer from sparse data availability due to data compliance issues, lack of data collection or even lack of data transfer through stakeholders. In the end there are

