Kernel Sparse Representation with Hybrid Regularization for On-Road Traffic Sensor Data Imputation

Chen, Xiaobo; Chen, Cheng; Cai, Yingfeng; Wang, Hai; Ye, Qiaolin

doi:10.3390/s18092884

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…Q. Shang proposed a model based on particle swarm optimization for fuzzy clustering and support vector regression [15]. X. Chen proposed graph regularization model [16]. These models can achieve better results than time series models, KNN methods, etc.…”

Section: A Related Workmentioning

confidence: 99%

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Gong

Zhang

2020

IEEE Access

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Due to various uncontrollable factors (such as random faulty acquisition equipment and data distortion), urban traffic flow data inevitably suffers from some form of data loss. Finding an effective filling method to estimate the missing data is of great help to the study of transportation networks. Traffic flow during a day are likely to have its regular peak period and off-peak period. For most regions of the urban road network, normally there is a certain trend in the traffic flow data. In this paper, we propose a data imputation method that employs a tensor decomposition approach, which fully considers the characteristics of the traffic flow in both time and space. The proposed method is based on high order singular value decomposition with soft thresholding core. In this method, traffic data are divided into its main trend part and the residual part, which is called detrending. And tensor decomposition is performed on these two parts separately. For each part, dynamic rank method is used to adjust the rank of tensor decomposition. With the actual 214 anonymous road segments with 10 minutes interval data in Guangdong, China, the highway data with 15 minutes interval in Madrid, Spain, and the traffic flow data from PeMS with 5 minutes interval in California, USA. The results of the different models are discussed in the case of continuous data missing and random data missing by different time intervals. In addition, by comparing with other data imputation methods, our method can fill the missing data with better performance. INDEX TERMS Detrending, incomplete data filling, singular value decomposition, tensor decomposition. CHUANFEI GONG received the B.S. degree in computer science from Tongji University, Shanghai, China, in 2017, where he is currently pursuing the master' degree with the Key Laboratory of Embedded System and Service Computing, and the Department of Computer Science. His research interests include intelligent transportation systems and data mining.

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Section: A Related Workmentioning

confidence: 99%

Urban Traffic Data Imputation With Detrending and Tensor Decomposition

Gong

Zhang

2020

IEEE Access

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“…Shao et al (2021) proposed a data recovery method based on GA-RF model by optimizing the parameters of Random Forest (RF) model by Genetic Algorithm (GA). Chen et al (2018) proposed a new interpolation method, called kernel sparse representation and elastic network regularization (KSR-EN) method. This integrates elastic network regularization and kernel methods in a unified framework that can be effectively used for missing value interpolation of road network traffic sensor data.…”

Section: Introductionmentioning

confidence: 99%

A Traffic Flow Data Recovery Method Based on An Auxiliary Discrimination Mechanism-oriented GAN Model

Wang¹,

Li²,

Yang

et al. 2022

Preprint

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Traffic flow detection methods tend to be diversified and intelligent, and the data anomalies become a prominent problem due to the influence of equipment, network and environment. Recovering abnormal data to obtain complete and accurate traffic flow data is crucial for traffic flow research work. To address this issue, traditional and mainstream traffic flow data recovery methods require a large amount of complete historical traffic flow data as a priori information for learning the trend of traffic flow data. Large amount of complete historical traffic flow data is hard to obtain in real traffic scenarios because traffic flow data collection is always accompanied by data anomalies. In this paper, we analyze the causes of abnormal data, classify the types of abnormal data, and propose identification methods applicable to different types of abnormal data based on the collection principles of freeway multi-source traffic data. We propose an auxiliary discrimination mechanism-oriented generative adversarial network (ADM-GAN) model to overcome the difficulties of traffic flow data recovery. Different from previous studies, we develop an auxiliary discrimination matrix to increase the utilization of original data, enhance the recovery accuracy, and improve the computational efficiency. We evaluate our model under different data missing rates (10%, 20%, 30%, 40% and 50%) by manually introducing missingness to the actual traffic flow data on the G50 freeway near Suzhou, China. The experimental results show that the proposed model outperforms other comparative methods. Under different missing rates, the recovery results obtained by ADM-GAN method are better than those obtained by comparative methods, and the lower the missing rate, the more obvious the advantages.

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