Home // PREDICTION SOLUTIONS 2024, International Conference on Prediction Solutions for Technical and Societal Systems // View article

Tourist Mobility Forecasting with Region-based Flows and Regular Trips

Authors:
Fernando Terroso-Saenz
Juan Morales-García
Miguel Puig
Ginesa Martinez-del Vas
Andres Muñoz

Keywords: tourist mobility ; deep neural networks , human mobility flows ; time series forecasting

Abstract:
One of the most prominent courses of action in the tourist sector is the development of predictors to anticipate the flow of incoming and outgoing tourists of a region. To do so, most of the existing approaches usually take tourist-related flows as the only primary input to perform the prediction. The present work assesses the suitability of composing a deep-learning predictor that fuses touristic displacements with data extracted from a general-purpose human-mobility dataset. The proposal has been tested in the Region of Murcia, a Spanish administrative area with a lively tourist sector. Results show that our approach achieves up to 46% Root Mean Square Error (RMSE) reduction with respect to a baseline only relying on tourist data.

Pages: 10 to 16

Copyright: Copyright (c) IARIA, 2024

Publication date: November 3, 2024

Published in: conference

ISBN: 978-1-68558-212-8

Location: Nice, France

Dates: from November 3, 2024 to November 7, 2024