Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City

Weng, H; Liu, Y; Chen, L

Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City

Weng, H Liu, Y Chen, L

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: AI 2023: Advances in Artificial Intelligence, 2024, 14471 LNAI, pp. 265-276
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Weng, H
dc.contributor.author	Liu, Y
dc.contributor.author	Chen, L https://orcid.org/0000-0002-6468-5729
dc.date.accessioned	2024-08-14T03:31:52Z
dc.date.available	2024-08-14T03:31:52Z
dc.date.issued	2024-01-01
dc.identifier.citation	AI 2023: Advances in Artificial Intelligence, 2024, 14471 LNAI, pp. 265-276
dc.identifier.isbn	9789819983872
dc.identifier.uri	http://hdl.handle.net/10453/180406
dc.description.abstract	Due to the widespread use of portable devices and the advancement of 5G technology, we have received a significant amount of mobile data, which requires prediction models for cellular traffic data. However, accurately forecasting mobile traffic data is challenging due to the complex spatial and temporal correlations, especially when the mobile data comes from a large geographical area. To tackle this challenge, we propose a new model, called ST-InducedTrans, to dynamically explore the large geographical correlations (spatial) and periodic variations (temporal). Specifically, a Spatial Bottleneck Transformer is devised to obtain spatial correlations from the most relevant grids in the geographical area at the cost of linear complexity. For the temporal blocks, we embed the elaborately selected temporal clues into a temporal Transformer to offer useful temporal prompts for cellular prediction. Finally, several spatial and temporal blocks are effectively stitched into a whole model for complementary cellular traffic prediction. We conducted comprehensive experiments on the public real-world cellular data from Milan. Results show that our model outperforms the state-of-the-art methods on three metrics (MAE, NRMSE, and R2 ) at the cost of lower time complexity.
dc.language	en
dc.publisher	Springer Nature
dc.relation	http://purl.org/au-research/grants/arc/DP210101347
dc.relation.ispartof	AI 2023: Advances in Artificial Intelligence
dc.relation.ispartofseries	Lecture Notes in Computer Science
dc.relation.isbasedon	10.1007/978-981-99-8388-9_22
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.subject.classification	46 Information and computing sciences
dc.title	Spatial Bottleneck Transformer for Cellular Traffic Prediction in the Urban City
dc.type	Chapter
utslib.citation.volume	14471 LNAI
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
pubs.organisational-group	University of Technology Sydney/All Manual Groups
pubs.organisational-group	University of Technology Sydney/All Manual Groups/Australian Artificial Intelligence Institute (AAII)
utslib.copyright.status	recently_added	*
dc.date.updated	2024-08-14T03:31:50Z
pubs.publication-status	Published
pubs.volume	14471 LNAI

Abstract:

Due to the widespread use of portable devices and the advancement of 5G technology, we have received a significant amount of mobile data, which requires prediction models for cellular traffic data. However, accurately forecasting mobile traffic data is challenging due to the complex spatial and temporal correlations, especially when the mobile data comes from a large geographical area. To tackle this challenge, we propose a new model, called ST-InducedTrans, to dynamically explore the large geographical correlations (spatial) and periodic variations (temporal). Specifically, a Spatial Bottleneck Transformer is devised to obtain spatial correlations from the most relevant grids in the geographical area at the cost of linear complexity. For the temporal blocks, we embed the elaborately selected temporal clues into a temporal Transformer to offer useful temporal prompts for cellular prediction. Finally, several spatial and temporal blocks are effectively stitched into a whole model for complementary cellular traffic prediction. We conducted comprehensive experiments on the public real-world cellular data from Milan. Results show that our model outperforms the state-of-the-art methods on three metrics (MAE, NRMSE, and R2 ) at the cost of lower time complexity.

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http://hdl.handle.net/10453/180406