Turning traffic volume imputation for persistent missing patterns with GNNs

Liu, Ruiqiang; Kan, Yuheng; Zhao, Shuai; Cheng, Bo; Ma, Zian; Wu, Wei

doi:10.1007/s10489-022-03568-3

Cited by 1 publication

(4 citation statements)

References 47 publications

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“…Besides GCN, there are also pieces of literature, such as [7] and [91], that make use of spatialtemporal GNN instead. In other words, instead of utilizing convolution for feature extraction and graph embedding, the research proposes other methods, such as the fusion of multiple data sources ( [7], [91]) or attention mechanism, as well as multitask learning [91].…”

Section: ) Graph Neural Networkmentioning

confidence: 99%

“…Graph Neural Networks [88], [89], [52], [90], [7], [91] Most studies focus on imputing only one traffic data, the exception being [7], which had done missing data imputation on both traffic speed and traffic volume, which leads to further proving their model's credibility. As a general guideline, future studies should take into account the accuracy, interpretability, as well as computational complexity into account when designing a model.…”

Section: Summary Of Forecasted Variables For Reviewed Literaturementioning

confidence: 99%

“…The attention mechanism is widely used in many studies due to its optimization abilities, such as by [95] for weight optimization or extracting multiple features like in [51]. Besides those two, it can be seen that a few of the literature reviewed had also incorporated the attention mechanism into their model [52], [84], [90], [91].…”

Section: A Attention Mechanismmentioning

confidence: 99%

“…However, collecting a significant number of traffic data requires a significant amount of time, and as such, researchers might not have a lot of data points when compared to the available public datasets. This can be seen from some of the reviewed papers, such as [69], [19], [7], [91], [65], [66], and [20], whereby their datasets range from 14 days to 2 months. The quantity of data collected within this short period would prove difficult for models which rely on many data to be properly trained.…”

Section: A Limitation Of Datamentioning

confidence: 99%

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

2023

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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.

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