Traffic State Data Imputation: An Efficient Generating Method Based on the Graph Aggregator

Xu, Dongwei; Hang, Peng; Wei, Chenchen; Shang, Xuetian; Li, Haijian

doi:10.1109/tits.2021.3119638

Cited by 21 publications

(6 citation statements)

References 49 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The model utilized weighted adjacency matrix based on distances to represent spatial relationships. In their study, [25] introduced a spatial interactive GCN network for the job of imputing. [26] developed a GraphSAGE model to gather spatio-temporal data from a graph created using correlation coefficients of past values.…”

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Bi-LSTM Based Variational Auto-Encoder for Multivariate IoT Data Imputation

2024

IJIES

View full text Add to dashboard Cite

In the relam of the Internet of Things (IoT), prevalence of missing data due to continuous data collection by smart devices necessitates the essential preliminary step of data imputation before engaging in information mining activities. IoT data exhibit robust interconnections in both spatial and temporal dimensions, surpassing the limitations of Euclidean space. Yet, prevailing machine learning and deep learning approaches often focus solely on temporal attributes or capture spatial features exclusively within a Euclidean framework. To address these challenges, this paper introduces a novel network named ST-Bi-LSTM-VAE (Spatio-Temporal Bidirectional Long Short-Term Memory based Variational Auto-Encoder). The architecture of ST-Bi-LSTM-VAE is primarily grounded in the Variational Auto-Encoder (VAE) framework. This innovative approach incorporates two distinct types of VAEs. The first type is dedicated to computing the adjacent matrix of the device network, a crucial input for the Graph Convolutional Network (GCN) essential in capturing intricate spatial relationships among devices. The second type of VAE is specifically tailored for data imputation, leveraging both global spatial and temporal dependencies. Empirical experiments conducted on diverse publicly available datasets substantiate the efficacy of ST-Bi-LSTM-VAE. The results obtained consistently demonstrate that proposed method surpasses baseline techniques in maintaining pattern, structure, and trend across datasets even at 50% missing gap for imputation task with 4.91% performance improvement in case of Intel Berkley Research Laboratory (IBRL) dataset and 3.5% on PRSA dataset.

show abstract

Section: Related Workmentioning

confidence: 99%

Spatio-Temporal Bi-LSTM Based Variational Auto-Encoder for Multivariate IoT Data Imputation

2024

IJIES

View full text Add to dashboard Cite

show abstract

“…While not as popular as tensor methods, GAN is a fairly popular method in missing data imputation applications due to its nature of constantly training to create a better dataset to trick the discriminator. This can be seen by the recent papers focusing on GAN methods such as [80], [83], [84], [85], [86], and [87]. As with other methods, this research tends to focus on the Spatiotemporal features of the traffic data ( [80], [84], [87]) when conducting traffic data imputation.…”

Section: ) Generative Adversarial Networkmentioning

confidence: 99%

“…This can be seen by the recent papers focusing on GAN methods such as [80], [83], [84], [85], [86], and [87]. As with other methods, this research tends to focus on the Spatiotemporal features of the traffic data ( [80], [84], [87]) when conducting traffic data imputation. Some utilize the Attention mechanism ( [83], [84]).…”

Section: ) Generative Adversarial Networkmentioning

confidence: 99%

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

2023

View full text Add to dashboard Cite

Missing data in Intelligent Transportation Systems (ITS) could lead to possible errors in the analyses of traffic data. Applying Artificial Intelligence (AI) in these circumstances can mitigate such problems. Past works focused only on specific data imputation methods, such as tensor factorization or a specific neural network model. While there are review papers covering singular topics regarding missing data, there are none in the field of traffic, to the best of our knowledge, that introduces the process of missing data collection and the viability of the traffic data collected while also broadly covering the popularly used models of recent years. This has led to non-uniformity of the terms used in missing data imputation, limited research in areas where datasets are not available, and a narrowed view of the methods used for data imputation. Hence, this paper aims to standardize the terms used in missing data classifications, look into the limitations of using available public or private datasets for urban traffic research, and discuss popular statistical and data-driven methods used by recent AI and ITS papers. It was found that tensor decomposition-based methods are the most popular for missing data imputation, followed by Generative Adversarial Networks and Graph Neural Networks, all of which rely on a large training dataset. Meanwhile, Probability Principle Component Analysis (PPCA) methods provide valuable insights via traffic analysis and are used for real-time traffic imputation. This paper also highlights the need for more efficient and reliable methods for traffic data collection, such as online APIs.

show abstract

“…It also does not support uncertainties in prediction, only models the expectation of the data distribution. Other recent works that use these methods, or their combination, are only applicable to specific applications 33 . In many applications, especially in the context of edge deployments, both input features and output labels may be missing 12 .…”

Section: Background and Related Workmentioning

confidence: 99%

DINI: data imputation using neural inversion for edge applications

Tuli

Jha

2022

Sci Rep

View full text Add to dashboard Cite

The edge computing paradigm has recently drawn significant attention from industry and academia. Due to the advantages in quality-of-service metrics, namely, latency, bandwidth, energy efficiency, privacy, and security, deploying artificial intelligence (AI) models at the network edge has attracted widespread interest. Edge-AI has seen applications in diverse domains that involve large amounts of data. However, poor dataset quality plagues this compute regime owing to numerous data corruption sources, including missing data. As such systems are increasingly being deployed in mission-critical applications, mitigating the effects of corrupted data becomes important. In this work, we propose a strategy based on data imputation using neural inversion, DINI. It trains a surrogate model and runs data imputation in an interleaved fashion. Unlike previous works, DINI is a model-agnostic framework applicable to diverse deep learning architectures. DINI outperforms state-of-the-art methods by at least 10.7% in average imputation error. Applying DINI to mission-critical applications can increase prediction accuracy to up to 99% (F1 score of 0.99), resulting in significant gains compared to baseline methods.

show abstract

Traffic State Data Imputation: An Efficient Generating Method Based on the Graph Aggregator

Cited by 21 publications

References 49 publications

Spatio-Temporal Bi-LSTM Based Variational Auto-Encoder for Multivariate IoT Data Imputation

Spatio-Temporal Bi-LSTM Based Variational Auto-Encoder for Multivariate IoT Data Imputation

Missing Traffic Data Imputation for Artificial Intelligence in Intelligent Transportation Systems: Review of Methods, Limitations, and Challenges

DINI: data imputation using neural inversion for edge applications

Contact Info

Product

Resources

About