research-article

A Multi-graph Convolutional Network Framework for Tourist Flow Prediction

Authors:

Wei WeiAuthors Info & Claims

ACM Transactions on Internet Technology (TOIT), Volume 21, Issue 4

Article No.: 106, Pages 1 - 13

https://doi.org/10.1145/3424220

Published: 22 July 2021 Publication History

Abstract

With the advancement of Cyber Physic Systems and Social Internet of Things, the tourism industry is facing challenges and opportunities. We can now able to collect, store, and analyze large amounts of travel data. With the help of data science and artificial intelligence, smart tourism enables tourists with great autonomy and convenience for an intelligent trip. It is of great significance to make full use of these massive data to provide better services for smart tourism. However, due to the skewed and imbalanced visiting for point of interest located at different places, it is of great significance to predict the tourist flow of each place, which can help the service providers for designing a better schedule visiting strategy in advance. Against this background, this article proposes a multi-graph convolutional network framework, named AMOUNT, for tourist flow prediction. To capture the diverse relationships among POIs, AMOUNT first constructs three subgraphs, including the geographical graph, interaction graph, and the co-relation graph. Then, a multi-graph convolution network is utilized to predict the future tourist flow. Experimental results on two real-world datasets indicate that the proposed AMOUNT model outperforms all other baseline tourist flow prediction approaches.

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Cited By

Tong G(2024)Forecasting Smart Tourism Visitor Flows Leveraging Big Data Technology AssistanceInternational Journal of e-Collaboration10.4018/IJeC.34680920:1(1--1)Online publication date: 16-Jul-2024
https://doi.org/10.4018/IJeC.346809
Rosário ADias J(2024)Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of ThingsAdministrative Sciences10.3390/admsci1402002214:2(22)Online publication date: 23-Jan-2024
https://doi.org/10.3390/admsci14020022
Xu ZLiu TJiang RHu PGuo ZLiu C(2024)AFaceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435108:1(1-33)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643510
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Index Terms

A Multi-graph Convolutional Network Framework for Tourist Flow Prediction
1. Computing methodologies
  1. Artificial intelligence
2. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
      1. Learning to rank

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Published In

cover image ACM Transactions on Internet Technology

ACM Transactions on Internet Technology Volume 21, Issue 4

November 2021

520 pages

ISSN:1533-5399

EISSN:1557-6051

DOI:10.1145/3472282

Editor:
Ling Lu
Georgia Institute of Technology, USA

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Copyright © 2021 Association for Computing Machinery.

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Association for Computing Machinery

New York, NY, United States

Publication History

Accepted: 01 September 2021

Published: 22 July 2021

Revised: 01 August 2020

Received: 01 May 2020

Published in TOIT Volume 21, Issue 4

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Cited By

Tong G(2024)Forecasting Smart Tourism Visitor Flows Leveraging Big Data Technology AssistanceInternational Journal of e-Collaboration10.4018/IJeC.34680920:1(1--1)Online publication date: 16-Jul-2024
https://doi.org/10.4018/IJeC.346809
Rosário ADias J(2024)Exploring the Landscape of Smart Tourism: A Systematic Bibliometric Review of the Literature of the Internet of ThingsAdministrative Sciences10.3390/admsci1402002214:2(22)Online publication date: 23-Jan-2024
https://doi.org/10.3390/admsci14020022
Xu ZLiu TJiang RHu PGuo ZLiu C(2024)AFaceProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36435108:1(1-33)Online publication date: 6-Mar-2024
https://dl.acm.org/doi/10.1145/3643510
Núñez JGómez‐Pulido JRamírez R(2024)Machine learning applied to tourism: A systematic reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.1549Online publication date: 4-Jul-2024
https://doi.org/10.1002/widm.1549
Marin OCioara TAnghel I(2023)Blockchain Solution for Buildings’ Multi-Energy Flexibility Trading Using Multi-Token StandardsFuture Internet10.3390/fi1505017715:5(177)Online publication date: 10-May-2023
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Lu YWang WHu XXu PZhou SCai M(2023)Vehicle Trajectory Prediction in Connected Environments via Heterogeneous Context-Aware Graph Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2022.317394424:8(8452-8464)Online publication date: 1-Aug-2023
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Dalipi FKastrati ZÖberg T(2023)The Impact of Artificial Intelligence on Tourism Sustainability: A Systematic Mapping Review2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE)10.1109/ICCIKE58312.2023.10131818(119-125)Online publication date: 9-Mar-2023
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