ST-FVGAN: filling series traffic missing values with generative adversarial network

Yang, Bing; Kang, Yan; Yuan, Yaoyao; Li, Hao; Wang, Fei

doi:10.1080/19427867.2021.1879624

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…To improve the efficiency of the proposed model, we compare our method with CNNBranch3 [ 27 ], CNN3 [ 28 ], CNN1 [ 22 ] and CNNBranch3_fc, they all have the GAN architecture. In order to prove the advantages of geometric algebra convolution, CNNBranch3 is used as its comparative experiment.…”

Section: Methodsmentioning

confidence: 99%

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Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network

Zang

Ding

et al. 2022

Sensors

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Traffic-data recovery plays an important role in traffic prediction, congestion judgment, road network planning and other fields. Complete and accurate traffic data help to find the laws contained in the data more efficiently and effectively. However, existing methods still have problems to cope with the case when large amounts of traffic data are missed. As a generalization of vector algebra, geometric algebra has more powerful representation and processing capability for high-dimensional data. In this article, we are thus inspired to propose the geometric-algebra-based generative adversarial network to repair the missing traffic data by learning the correlation of multidimensional traffic parameters. The generator of the proposed model consists of a geometric algebra convolution module, an attention module and a deconvolution module. Global and local data mean squared errors are simultaneously applied to form the loss function of the generator. The discriminator is composed of a multichannel convolutional neural network which can continuously optimize the adversarial training process. Real traffic data from two elevated highways are used for experimental verification. Experimental results demonstrate that our method can effectively repair missing traffic data in a robust way and has better performance when compared with the state-of-the-art methods.

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Section: Methodsmentioning

confidence: 99%

“…K. Xie [ 26 ] proposes a sequential tensor completing method to reduce the computing cost of high-dimensional neural network algorithms. All the above studies have promoted the application of 3D convolutional generative adversarial networks [ 27 , 28 ] that can effectively recover traffic data in large-scale traffic networks.…”

Section: Related Workmentioning

confidence: 99%

Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network

Zang

Ding

et al. 2022

Sensors

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“…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%

“…Some utilize the Attention mechanism ( [83], [84]). Besides that, [85] makes use of additional external factors such as weather and holiday factor. Interestingly enough, that research found that external factors excluding holidays do not influence the data imputation much for missing rates less than 40%.…”

Section: ) Generative Adversarial Networkmentioning

confidence: 99%

“…Models which relied on external features such as [84], [85] might suffer a reduction in performance, but based on the experiments by [85], it would seem that external factors might not play such a big part unless the missing rate is greater than 40%, barring holiday factors, of which future researchers are encouraged to try to differentiate between holiday and non-holiday traffic datasets whenever possible.…”

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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High-resolution spatiotemporal inference of urban road traffic emissions using taxi GPS and multi-source urban features data: a case study in Chengdu, China

Li,

Jiang,

Han

et al. 2024

Urban Info

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The spatial heterogeneity and temporal variability of traffic in urban environments make traffic emissions inference challenging. To address this challenge, this study introduces a novel geographical context-based approach utilizing high-resolution taxi GPS data, incorporating multidimensional contextual factors such as road data, points of interest (POI), weather data, and population density. The proposed method can enhance the precision of traffic emissions inference compared to conventional macroscopic estimation techniques. To overcome the issue of missing data in traffic emissions inference from taxi data, three ensemble machine learning algorithms—Random Forest, Gradient Boosting Decision Trees (GBDT), and eXtreme Gradient Boosting (XGBoost)—are employed. These algorithms efficiently handle a substantial volume of taxi GPS data, achieving reduced computational time and model complexity. The proposed framework establishes localized models for each road segment, taking into consideration both geographical and external features that characterize the urban environment. This localized modeling contributes significantly to a more profound understanding of traffic dynamics. A thorough comparative analysis is conducted to assess the performance of the proposed method. Results indicate that incorporating multidimensional urban features is advantageous for traffic speed inference. Among the ensemble learning models, Random Forest outperforms others when dealing with a small missing rate or limited sample size, while XGBoost exhibits superior performance for larger missing rates or substantial sample sizes. Additionally, an analysis of the feature importance in traffic speed highlights that road network features are the most significant factors, followed by temporal characteristics, spatial attributes, POI data, and weather information. Finally, leveraging inferred traffic speed and volume information, emissions from large-scale urban road traffic are inferred based on the COPERT model. In contrast to methods relying on complex, multi-source data for emission estimation, our approach utilizes simple and easily accessible data, enabling precise estimation of emissions on a large-scale spatiotemporal basis.

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ST-FVGAN: filling series traffic missing values with generative adversarial network

Cited by 10 publications

References 29 publications

Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network

Traffic-Data Recovery Using Geometric-Algebra-Based Generative Adversarial Network

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

High-resolution spatiotemporal inference of urban road traffic emissions using taxi GPS and multi-source urban features data: a case study in Chengdu, China

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