Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning

Zhao, Junhui; Nie, Yiwen; Ni, Shanjin; Sun, Xiaoke

doi:10.1109/access.2020.2978530

Cited by 25 publications

(10 citation statements)

References 33 publications

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“…The authors showed that it is possible to improve output predictions by considering the dependencies, if any exist, between input variables. The recurrent network performed far better than a standard network with missing values imputed by mean values [26].…”

Section: Deep Learning Based Imputationmentioning

confidence: 89%

Semi-GAN: An Improved GAN-Based Missing Data Imputation Method for the Semiconductor Industry

Lee

Connerton

Lee³

et al. 2022

IEEE Access

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Complete data are required for the operation, maintenance, and detection of faults in semiconductor equipment. Missing data occur frequently because of defects such as sensor, data storage, and communication faults, leading to reductions in yield, quality, and productivity. Although many attempts have been made to solve this problem in other fields, few studies have specifically addressed data imputation in the semiconductor industry. In this study, an improved generative adversarial network (GAN)-based missing data imputation for the semiconductor industry called Semi-GAN is proposed. This study introduces a machine learning approach for dealing with data imputation in the semiconductor industry. The proposed method was applied to real data and evaluated using traditional techniques. In particular, the proposed method showed excellent results compared to traditional attribution methods when all missing data ratios in the experiments were less than 20%. It was also observed to be superior when simple and repetitive patterns were omitted rather than repetitive but not simple patterns.

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Section: Deep Learning Based Imputationmentioning

confidence: 89%

Semi-GAN: An Improved GAN-Based Missing Data Imputation Method for the Semiconductor Industry

Lee

Connerton

Lee³

et al. 2022

IEEE Access

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“…The prediction methods are usually filled in the missing data according to the relationship of the time series or spatial series of traffic state. Historical average [21], ARIMA [33], LSTM [6], [23][24][25], and other methods have been proved to be effective in predicting the data based on the developing trend of traffic state. And the trend of traffic state is analyzed by using the observations from the previous steps.…”

Section: B Missing Data Imputationmentioning

confidence: 99%

Traffic State Estimation of Bus Line With Sparse Sampled Data

et al. 2020

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The traffic state of the bus line is the information basis for the bus company to make bus dispatch and travel time prediction. However, the bus GPS data is severely sparse in time and space coverage of traffic state, due to the long data sampling time interval and low bus departure frequency. Because of ignoring the severe sparseness of the bus data, the existing traffic state methods cannot reconstruct the traffic state accurately. To deal with this problem, a new traffic state estimation method for the bus line, named GAN_BS, is proposed. First, an improved generative adversarial network (GAN-I) is used to generate reasonable bus data. GAN-I aims to find the probability space of the data distribution under sparse sampling. And to reduce the size of the latent space of data, the traffic knowledge is introduced as prior information layers. Then, a traffic adaptive bilateral smoothing method (BS) is used to map discrete bus data into the continuous traffic state. The BS convolves data with a bilateral kernel, which multiplies the local action kernel with a mask of traffic state similarity. Therefore, the BS can maintain transitions between different traffic patterns while separating noise from traffic state. Finally, a set of numerical experiments are performed on the real bus data set in Changchun. The results show that the GAN-I can accurately reproduce the traffic state when the missing rate of data exceeds 50%.

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“…Support vector machine (SVM) is insensitive to outliers and has high robustness, but this algorithm is of high computational complexity [7]. Neural network has excellent performance, nevertheless, it requires a huge training data set and is prone to over-fitting [8]. The tensor factorization method has advantages in highdimensional data imputing, and makes full use of the implicit information between data of different dimensions, yet it also has problems in calculation [9,10].…”

Section: Introductionmentioning

confidence: 99%

Missing Data Imputation Approach Based on Adaptive Compressed Sensing

2022

Proceedings of 2022 the 12th International Workshop on Computer Science and Engineering

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The paper presents a missing data imputation method based on compressed Sensing (CS). First of all, the problem of data imputation is translated into the recovery of sparse vector under the framework of compressed sensing. Secondly, we propose an improved greedy reconstruction algorithm called Double Try Sparsity Adaptive Matching Pursuit (DTSAMP). The algorithm obtains the estimation of sparsity by trying twice to approximate the value of sparsity, and then approximates the estimated value in each iteration. As a result, the missing data sets can be reconstructed without prior information of the sparsity. Furthermore, the step size and support set are well controlled by setting thresholds during the iteration. The simulation results show that the proposed algorithm is superior to other methods in terms of reconstruction speed and accuracy, as well as better robustness.

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Traffic Data Imputation and Prediction: An Efficient Realization of Deep Learning

Cited by 25 publications

References 33 publications

Semi-GAN: An Improved GAN-Based Missing Data Imputation Method for the Semiconductor Industry

Semi-GAN: An Improved GAN-Based Missing Data Imputation Method for the Semiconductor Industry

Traffic State Estimation of Bus Line With Sparse Sampled Data

Missing Data Imputation Approach Based on Adaptive Compressed Sensing

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