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Empowering Spatial Knowledge Graph for Mobile Traffic Prediction

Published: 22 December 2023 Publication History

Abstract

Accurately predicting base station traffic volumes and understanding mobile traffic patterns is essential for smart city development, enabling efficient resource allocation and ensuring high-quality communication services. However, existing works have limitations in capturing spatial information, though the surrounding environment plays a critical role in mobile traffic prediction. In this paper, we utilize a spatial knowledge graph to represent spatial information and add important urban components to augment it making it a more effective tool for capturing environmental information. we further propose a multi-relational knowledge graph convolutional network model for mobile traffic prediction, which consists of three parts. The environmental context modelling captures spatial information from the augmented spatial knowledge graph using tucker decomposition and relational graph convolutional network. The semantic relationship modelling extracts semantic relationships between base stations and employs transformer and causal convolution to capture temporal features. The inter-attentional fusion modelling utilizes the self-attention mechanism to further capture base station relationships and predict future traffic volumes. Extensive experiments demonstrate that our proposed model significantly outperforms the state-of-the-art models by over 10% in mobile traffic prediction. The code is available at https://github.com/tsinghua-fiblab/Mobile-Traffic-Prediction-sigspatial23

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cover image ACM Conferences
SIGSPATIAL '23: Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
This work is licensed under a Creative Commons Attribution International 4.0 License.

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Published: 22 December 2023

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Author Tags

  1. knowledge graph
  2. mobile traffic prediction
  3. graph neural networks

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  • (2024)The Propagation of Congestion on Transportation Networks Analyzed by the Percolation ProcessMathematics10.3390/math1220324712:20(3247)Online publication date: 17-Oct-2024
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  • (2024)Knowledge Graphs Applications in Smart CitiesProceedings of the 2024 8th International Conference on Information System and Data Mining10.1145/3686397.3686423(136-141)Online publication date: 24-Jun-2024
  • (2024)Regional Features Conditioned Diffusion Models for 5G Network Traffic GenerationProceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems10.1145/3678717.3691312(396-409)Online publication date: 29-Oct-2024
  • (2024)UniST: A Prompt-Empowered Universal Model for Urban Spatio-Temporal PredictionProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671662(4095-4106)Online publication date: 25-Aug-2024
  • (2024)Diffusion Model-based Mobile Traffic Generation with Open Data for Network Planning and OptimizationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671544(4828-4838)Online publication date: 25-Aug-2024
  • (2024)Spatial-Temporal Large Language Model for Traffic Prediction2024 25th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM61037.2024.00025(31-40)Online publication date: 24-Jun-2024
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