SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction
Article No.: 81, Pages 1 - 10
Abstract
Mining spatio-temporal correlation patterns for traffic prediction is a well-studied field. However, most approaches are based on the assumption of the availability of and accessibility to a sufficiently dense data source, which is rather the rare case in reality. Traffic sensors in road networks are generally highly sparse in their distribution: fleet-based traffic sensing is sparse in space but also sparse in time. There are also other traffic application, besides road traffic, like moving objects in the marine space, where observations are sparsely and arbitrarily distributed in space. In this paper, we tackle the problem of traffic prediction on sparse and spatially irregular and non-deterministic traffic observations. We draw a border between imputations and this work as we consider high sparsity rates and no fixed sensor locations. We advance correlation mining methods with a Sparse Unstructured Spatio Temporal Reconstruction (SUSTeR) framework that reconstructs traffic states from sparse non-stationary observations. For the prediction the framework creates a hidden context traffic state which is enriched in a residual fashion with each observation. Such an assimilated hidden traffic state can be used by existing traffic prediction methods to predict future traffic states. We query these states with query locations from the spatial domain.
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Index Terms
- SUSTeR: Sparse Unstructured Spatio Temporal Reconstruction on Traffic Prediction
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Information & Contributors
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Published In
November 2023
686 pages
ISBN:9798400701689
DOI:10.1145/3589132
- General Chairs:
- Maria Luisa Damiani,
- Matthias Renz,
- Program Chairs:
- Ahmed Eldawy,
- Peer Kröger,
- Mario A Nascimento
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New York, NY, United States
Publication History
Published: 22 December 2023
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Author Tags
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- Research-article
Funding Sources
- Helmholtz School for Marine Data Science (MarDATA)
- KMS Kiel Marine Science ? Centre for Interdisci- plinary Marine Science at Kiel University.
- Deutsche Forschungsgemeinschaft (DFG)
Conference
SIGSPATIAL '23
Sponsor:
SIGSPATIAL '23: 31st ACM International Conference on Advances in Geographic Information Systems
November 13 - 16, 2023
Hamburg, Germany
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Overall Acceptance Rate 257 of 1,238 submissions, 21%
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