Tensor based missing traffic data completion with spatial–temporal correlation

Ran, Bin; Tan, Huachun; Wu, Yao; Jin, Peter J.

doi:10.1016/j.physa.2015.09.105

Cited by 119 publications

(49 citation statements)

References 32 publications

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“…The covariance matrix C_T was calculated from the most relevant spatial series and the ratio of statistical expectation a j and covariance C j by using the most relevant spatial series and the missing spatial series (Lines 6-9 of Algorithm 4). Then, we joined these values into matrix Formula (20) and solved this matrix to get parameter ϕ j (Lines 10-12 of Algorithm 4). Finally, through Formula (17), interpolation result V 4,6 was calculated as…”

Section: Fine-grained Temporal Dimension Interpolationmentioning

confidence: 99%

“…In recent years, a number of studies have extended single dimension interpolation methods to consider both space and time; for example, spatio-temporal probabilistic principal component regression (ST-PCR), spatio-temporal IDW (ST-IDW), spatio-temporal kriging (ST-kriging) and the spatio-temporal heterogeneous covariance method (ST-HC) [2,3,7,9,10,[20][21][22]. ST-PCR [9] is a statistical learning-based method, which takes advantage of the statistical feature of observed data.…”

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: Fine-grained Temporal Dimension Interpolationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Cheng

2017

IJGI

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“…It can predict the nodes that should have responded in due course of time but have not by reconstructing the inherent nature of waveform which is spatially and temporally correlated. Ran et al [22] have used a similar approach where they fused traffic flow data received from multiple sensing locations to reconstruct the missing traffic data using full spatial and temporal information of traffic flow. We explore spatial temporal correlation for reliable object detection and reconstruction of its possible trajectory in progressing series of time.…”

Section: The Aspect Of Reliabilitymentioning

confidence: 99%

Tracking intruder ship in wireless environment

Rao

Kamila

2017

Hum. Cent. Comput. Inf. Sci.

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BackgroundObject tracking is one of the most practical applications of WSN. Wireless network is itself vulnerable and when combined with sensors the other errors that add upon to it are sensor failures, localization errors, prediction, and detection errors. Robust and effective solutions that could recover the information of tracking object are required for making wireless sensor networks more acceptable. Efficient energy management and enhanced network lifetime are other desired design aspects of a wireless sensor network application. Rout and Ghosh [1] tackle the problem of the area around the sink node that is a bottleneck zone as more activity is reported there, causing improper load balance that affects the overall network lifetime. They attempted to solve this problem by proposing a duty cycled wireless sensor network. The sensor nodes in the bottleneck zone are divided into two groups in their approach: simple relay sensors and network coder sensors. While the relay nodes simply forward the received data, the network coder nodes transmit using a network coding based algorithm. Energy efficiency of the bottleneck zone hence increases as more volume of data will be transmitted to the Sink with the same number of transmissions. Park and Shi [2] suggest strategies of deploying additional sensors in the bottleneck zone in due course of time to maintain a desired level of network connectivity. The major concerns of WSNs are studied by Mohsin [3] who also Abstract It is a challenging task for all Harbors or Naval Administration to restrict and monitor the movement of defense or commercial ships. Most commonly used techniques of monitoring are radars and satellite images. These techniques are not reliable as radars can be turned off voluntarily and receptions of images are affected by adverse climatic conditions. This paper proposes a reliable ship intruder detection algorithm that classifies different types of objects approaching the model system in and out of phase with the ocean waves. The proposed technique also takes care of superimposition of temporal and spatial values of nodes that are presumably deployed in the sea surface up to a certain distance. Simulation results prove that the proposed algorithm detects and classifies objects efficiently even when 50% of the nodes reporting the tracking phenomenon are tampered. Rao and Kamila Hum. Cent. Comput. Inf. Sci. (2017) Comput. Inf. Sci. (2017) 7:14 survey the performance tradeoffs in terms of energy, tracking error and other performance metrics of the available sensors in established network architectures. Tracking and monitoring techniques need to be designed by taking account of these parameters. A simple and efficient tracking system based on Oriented FAST and Rotated BRIEF (ORB) is presented by Wu et al. [4]. It is a fast binary descriptor where good matched points are obtained by computing Hamming distance. Based on the matched points and temporal-spatial constraint, location of the object in new frame is obtained. Tracking applications of wire...

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“…As multiway matrices, tensor can take full advantage of the temporal and spatial information of traffic flow data to impute the missing data with a higher precision. Subsequently, Tan et al [17,18] proposed several other tensor-based imputation methods according to different perspectives and tested the proposed methods using the traffic data from PeMS, and the results show that these methods have good performance under extreme conditions. In 2015, Tang et al [19] proposed a missing traffic imputation method based on the fuzzymeans (FCM) optimized by the genetic algorithm.…”

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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Tensor based missing traffic data completion with spatial–temporal correlation

Cited by 119 publications

References 32 publications

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

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

Tracking intruder ship in wireless environment

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

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