LSTM-Based Deep Learning Model for Predicting Individual Mobility Traces of Short-Term Foreign Tourists

Alessandro Crivellari and Euro Beinat
Additional contact information
Alessandro Crivellari: Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria
Euro Beinat: Department of Geoinformatics—Z_GIS, University of Salzburg, 5020 Salzburg, Austria

Sustainability, 2020, vol. 12, issue 1, 1-18

Abstract: The increasing availability of trajectory recordings has led to the mining of a massive amount of historical track data, allowing for a better understanding of travel behaviors by revealing meaningful motion patterns. In the context of human mobility analysis, the problem of motion prediction assumes a central role and is beneficial for a wide range of applications, including for touristic purposes, such as personalized services or targeted recommendations, and sustainability studies related to crowd management and resource redistribution. This paper tackles a particular case of the trajectory prediction problem, focusing on large-scale mobility traces of short-term foreign tourists. These sparse trajectories, short and non-repetitive, lack spatial and temporal regularity, making prediction analysis based on individual historical motion data unreliable. To face this issue, we hereby propose a deep learning-based approach, taking into account the collective mobility of tourists over the territory. The underlying semantics of motion patterns are captured by means of a long short-term memory (LSTM) neural network model trained on pre-processed location sequences, aiming to predict the next visited place in the trajectory. We tested the methodology on a real-world big dataset, demonstrating its higher feasibility with respect to traditional approaches.

Keywords: deep learning; LSTM; neural networks; location prediction; trajectories; smart tourism (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/2071-1050/12/1/349/pdf (application/pdf)
https://www.mdpi.com/2071-1050/12/1/349/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:1:p:349-:d:304175

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().