spatial-temporal data modeling has attracted attention due to the massive spatial-temporaldata acquired by sensors, as well as its importance in the real world. Most existing methods require transferring a huge volum...
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Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement co...
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Pavement preventive maintenance is one of the most fundamental use cases when deploying digital twins (DTs) for highway infrastructure management. To achieve this, it is essential to accurately predict the pavement conditions in future years. This paper developed a spatial-temporal Graph Attention network (STGAT) that can effectively capitalize on both spatial and temporal dependencies while addressing inherent heterogeneity in pavement data for more accurate condition predictions. On top of this, a structured assessment procedure was introduced to determine the need for preventive maintenance on road sections based on the STGAT predictions. A case study on the highway network in the United Kingdom was conducted to evaluate the method's performance. The results showed that the proposed method can achieve superior accuracy for pavement condition prediction and subsequent preventive maintenance assessment compared to existing methods, thus signifying its potential to improve the effectiveness of DTs for highway infrastructure management.
modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporaldata often exhibits multi-scale characteristics, which are uti...
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ISBN:
(纸本)9798350344868;9798350344851
modeling route representation aims to obtain contextual representations of an entire route for various traffic-related tasks. In reality, spatial-temporaldata often exhibits multi-scale characteristics, which are utilized by many studies to enhance their performance. However, there is still a lack of in-depth research on how to effectively incorporate the multi-scale spatial-temporal information into transformer structure to adequately model route representation. In this paper, we propose a novel hierarchical route representation framework called RouteMT, which effectively captures multi-scale spatial-temporal characteristics of routes and leverages a mixed-scale transformer architecture to fuse intra and inter-route features. Experiments on real data confirm RouteMT's superior performance and versatility.
Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propos...
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ISBN:
(纸本)9780992862657
Covariance sketching has been recently introduced as an effective strategy to reduce the data dimensionality without sacrificing the ability to reconstruct second-order statistics of the data. In this paper, we propose a novel covariance sketching scheme with reduced complexity for spatial-temporaldata, whose covariance matrices satisfy the Kronecker product expansion model recently introduced by Tsiligkaridis and Hero. Our scheme is based on quadratic sampling that only requires magnitude measurements, hence is appealing for applications when phase information is difficult to obtain, such as wideband spectrum sensing and optical imaging. We propose to estimate the covariance matrix based on convex relaxation when the separation rank is small, and when the temporal covariance is additionally Toeplitz structured. Numerical examples are provided to demonstrate the effectiveness of the proposed scheme.
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