ST-LBAGAN: Spatio-temporal learnable bidirectional attention generative adversarial networks for missing traffic data imputation

Yang, Bing; Kang, Yan; Yuan, Yaoyao; Huang, Xin; Li, Hao

doi:10.1016/j.knosys.2020.106705

Cited by 71 publications

(14 citation statements)

References 26 publications

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“…The model, on the other hand, requires labeled categories in the input temporal data, limiting its applicability to the realworld scenarios. Recently, due to the superiority ability of attention mechanism to model the inter-feature dependencies, scholars introduced it to the text to aid in the missing data imputation task [16], [33], [34]. In [33], Yang et al utilized the graph attention neural network [35] to learn the spatial dependence of data.…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

Attention-Driven Recurrent Imputation for Traffic Speed

Zhang

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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In practice, traffic data collection is often warned by missing data due to communication errors, sensor failures, storage loss, among other factors, leading to impaired data collection and hampering the effectiveness of downstream applications. However, existing imputation approaches focus exclusively on estimating the lost value from incomplete observations and ignore historical data. In this paper, we propose a novel neural network model, namely, Attention-Driven Recurrent Imputation Network (ADRIN), to address the problem of missing traffic data. Specifically, in ADRIN, we devise an Imputation-targeted Long Short-Term Memory (LSTM-I) module for filling in missing data. Meanwhile, we consider the periodicity of historical data and design a historical average calculation module in ADRIN. On this basis, we employ the multi-head self-attention mechanism for further extracting latent temporal features from the output of the two modules. ADRIN is capable of modeling both incomplete observation inputs and historical averages independently to estimate the missing values. We conducted comprehensive experiments on three real-world traffic datasets, to demonstrate that ADRIN consistently outperforms other baselines in a variety of scenarios. Furthermore, ablation experiments are conducted on the various modules of the model, and it is concluded that historical data can significantly enhance the imputation effect.

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Section: Deep Learning-based Methodsmentioning

confidence: 99%

Attention-Driven Recurrent Imputation for Traffic Speed

Zhang

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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“…Several studies have been conducted to use different attention information from many independent modalities. ST-LBAGAN [34] uses a bidirectional attention method to learn a feature map for missing traffic data imputation. Yu et al [35] designs a modular co-attention network which utilize the attention from video input to decode the answer from attention of given questions in order to to complete the video question answering task.…”

Section: Attention Mechanismmentioning

confidence: 99%

DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation

Wang¹,

Zhou²,

Zhou³

et al. 2022

Preprint

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Driving trajectory representation learning is of great significance for various location-based services, such as driving pattern mining and route recommendation. However, previous representation generation approaches tend to rarely address three challenges: 1) how to represent the intricate semantic intentions of mobility inexpensively; 2) complex and weak spatial-temporal dependencies due to the sparsity and heterogeneity of the trajectory data; 3) route selection preferences and their correlation to driving behavior. In this paper, we propose a novel multimodal fusion model, DouFu, for trajectory representation joint learning, which applies multimodal learning and attention fusion module to capture the internal characteristics of trajectories. We first design movement, route, and global features generated from the trajectory data and urban functional zones and then analyze them respectively with the attention encoder or feed forward network. The attention fusion module incorporates route features with movement features to create a better spatial-temporal embedding. With the global semantic feature, DouFu produces a comprehensive embedding for each trajectory. We evaluate representations generated by our method and other baseline models on classification and clustering tasks. Empirical results show that DouFu outperforms other models in most of the learning algorithms like the linear regression and the support vector machine by more than 10%.

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“…The shadow boundary loss function, shadow ceiling loss function, and shadow entrance curvature loss function are combined with an adversarial loss to guide the generator to well predict the value in void areas. The missing data in comprehensive traffic flow data [139] is small-scale data missing. In [139], the authors proposed a Spatiotemporal Learnable Bidirectional Attention Generative Adversarial Networks (ST-LBAGAN) to implement data fusion for missing traffic data imputation.…”

Section: A Missing Data Reconstructionmentioning

confidence: 99%

“…The missing data in comprehensive traffic flow data [139] is small-scale data missing. In [139], the authors proposed a Spatiotemporal Learnable Bidirectional Attention Generative Adversarial Networks (ST-LBAGAN) to implement data fusion for missing traffic data imputation. In this study, the masked reconstruction loss, perceptual loss, discriminative loss, and adversarial loss are combined as a new objective function and optimized to improve the data imputation ability.…”

Section: A Missing Data Reconstructionmentioning

confidence: 99%

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

Liu¹

2021

Preprint

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In the past decades, remote sensing (RS) data fusion has always been an active research community. A large number of algorithms and models have been developed. Generative Adversarial Networks (GAN), as an important branch of deep learning, show promising performances in variety of RS image fusions. This review provides an introduction to GAN for remote sensing data fusion. We briefly review the frequently-used architecture and characteristics of GAN in data fusion and comprehensively discuss how to use GAN to realize fusion for homogeneous RS data, heterogeneous RS data, and RS and ground observation data. We also analyzed some typical applications with GAN-based RS image fusion. This review takes insight into how to make GAN adapt to different types of fusion tasks and summarizes the advantages and disadvantages of GAN-based RS data fusion. Finally, we discuss the promising future research directions and make a prediction on its trends.

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ST-LBAGAN: Spatio-temporal learnable bidirectional attention generative adversarial networks for missing traffic data imputation

Cited by 71 publications

References 26 publications

Attention-Driven Recurrent Imputation for Traffic Speed

Attention-Driven Recurrent Imputation for Traffic Speed

DouFu: A Double Fusion Joint Learning Method For Driving Trajectory Representation

A Review on Remote Sensing Data Fusion with Generative Adversarial Networks (GAN)

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