Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

Li, Li; Li, Yuebiao; Li, Zhiheng

doi:10.1016/j.trc.2013.05.008

Cited by 265 publications

(126 citation statements)

References 41 publications

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“…Data for st19, st27, st36 were completely missing Using the PM 2.5 dataset as an example, we set wt = 48 as the time sliding window, which took 48 h of data to explore the missing pattern. The missing numbers of st 19 , st 27 , st 35 and st 36 at the same time were eight (i.e., the missing pattern in Figure 1c). Data for st 19 , st 27 , st 36 were completely missing (Figure 1d).…”

Section: Datasetsmentioning

confidence: 99%

“…The missing numbers of st 19 , st 27 , st 35 and st 36 at the same time were eight (i.e., the missing pattern in Figure 1c). Data for st 19 , st 27 , st 36 were completely missing (Figure 1d). Data for {st 18 , st 19 }, {st 35 , st 36 } showed random block loss (Figure 1b).…”

Section: Datasetsmentioning

confidence: 99%

“…However, this approach fails to address two major problems. First, many prediction models do not fully utilize the essential characteristics of spatio-temporal data, which can degrade performance; second, if a consecutive series of data is all lost, prediction methods often cannot achieve complete reconstruction [19].…”

Section: Introductionmentioning

confidence: 99%

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A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Cheng

2017

IJGI

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Abstract:Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate.

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Section: Datasetsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

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A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Cheng

2017

IJGI

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“…In addition to two comparison methods, the probabilistic principle component analysis (PPCA)-based imputation method, which had been proven to be one of the most effective imputing methods in traffic data [20,21], is also compared. In this evaluation, various missing lengths and missing ratios are considered, and all missing data are intentionally generated with these missing lengths and missing ratios.…”

Section: Performance Evaluationmentioning

confidence: 99%

Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

et al. 2017

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This paper proposes a learning-based adaptive imputation method (LAI) for imputing missing power data in an energy system. This method estimates the missing power data by using the pattern that appears in the collected data. Here, in order to capture the patterns from past power data, we newly model a feature vector by using past data and its variations. The proposed LAI then learns the optimal length of the feature vector and the optimal historical length, which are significant hyper parameters of the proposed method, by utilizing intentional missing data. Based on a weighted distance between feature vectors representing a missing situation and past situation, missing power data are estimated by referring to the k most similar past situations in the optimal historical length. We further extend the proposed LAI to alleviate the effect of unexpected variation in power data and refer to this new approach as the extended LAI method (eLAI). The eLAI selects a method between linear interpolation (LI) and the proposed LAI to improve accuracy under unexpected variations. Finally, from a simulation under various energy consumption profiles, we verify that the proposed eLAI achieves about a 74% reduction of the average imputation error in an energy system, compared to the existing imputation methods.

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“…Markov Chain Monte Carlo (MCMC) imputation method [13] and Probabilistic Principal Component Analysis (PPCA) imputation method [3] are two classical statistical learning based methods. Moreover, Kernel Probabilistic Principal Component Analysis (KPPCA) [14] and Bayesian Principal Component Analysis (BPCA) method [15] have also been used for missing traffic data imputation. In 2014, Chiou et al propose an imputation method for missing traffic values by using the conditional expectation approach to functional principal component analysis (FPCA) [16], and their simulation study shows that the FPCA method performs better than the PPCA and BPCA.…”

Section: Introductionmentioning

confidence: 99%

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Shang

Yang

Gao

et al. 2018

Journal of Advanced Transportation

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Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness.

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Efficient missing data imputing for traffic flow by considering temporal and spatial dependence

Cited by 265 publications

References 41 publications

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Learning-Based Adaptive Imputation Methodwith kNN Algorithm for Missing Power Data

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

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