A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

Tang, Jinjun; Gao, Fan; Liu, Fang; Chen, Xinqiang

doi:10.1109/access.2020.2964070

Cited by 25 publications

(17 citation statements)

References 49 publications

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“…Matos et al combined an adaptive correlation filter with local re-detection features to obtain high-fidelity ship positions [7]. Similar researches can also be found in [8]- [12].…”

Section: Introductionmentioning

confidence: 81%

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Chen

Yang

et al. 2020

IEEE Access

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Ship tracking provides crucial on-site microscopic kinematic traffic information which benefits maritime traffic flow analysis, ship safety enhancement, traffic control, etc., and thus has attracted considerable research attentions in the maritime surveillance community. Conventional ship tracking methods yield satisfied results by exploring distinct visual ship features in maritime images, which may fail when the target ship is partially or fully sheltered by obstacles (e.g., ships, waves, etc.) in maritime videos. To overcome the difficulty, we propose an augmented ship tracking framework via the kernelized correlation filter (KCF) and curve fitting algorithm. First, the KCF model is introduced to track ships in the consecutive maritime images and obtain raw ship trajectory dataset. Second, the data anomaly detection and rectification procedure are implemented to rectify the contaminated ship positions. For the purpose of performance evaluation, we implement the proposed framework and another three popular ship tracking models on the four typical ship occlusion videos. The experimental results show that our proposed framework successfully tracks ships in maritime video clips with high accuracy (i.e., the average root mean square error (RMSE), root mean square percentage error (RMSPE), mean absolute deviation (MAD) and mean absolute percentage error (MAPE) are less than 10), which significantly outperforms the other popular ship trackers.INDEX TERMS Smart ship, curve fitting, kernelized correlation filter, visual ship tracking, ship occlusion.

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“…Matos et al combined an adaptive correlation filter with local re-detection features to obtain high-fidelity ship positions [7]. Similar researches can also be found in [8]- [12].…”

Section: Introductionmentioning

confidence: 81%

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Chen

Yang

et al. 2020

IEEE Access

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“…Also, Tang et al [155] leverage the SVR method to solve the problem, but they enhance the method with some denoising algorithms. They further combine one kind of denoising algorithm (ensemble empirical mode decomposition) and the fuzzy C-means neural network (FCMNN) to improve prediction accuracy [156]. To predict for multivariate traffic flows, Yan et al [157] adopt a weighted Frobenius norm to estimate similarity between multivariate time series, where the weights are determined by the PCA method.…”

Section: Traffic Forecastingmentioning

confidence: 99%

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Yuan

2021

Data Sci. Eng.

215

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Intelligent transportation (e.g., intelligent traffic light) makes our travel more convenient and efficient. With the development of mobile Internet and position technologies, it is reasonable to collect spatio-temporal data and then leverage these data to achieve the goal of intelligent transportation, and here, traffic prediction plays an important role. In this paper, we provide a comprehensive survey on traffic prediction, which is from the spatio-temporal data layer to the intelligent transportation application layer. At first, we split the whole research scope into four parts from bottom to up, where the four parts are, respectively, spatio-temporal data, preprocessing, traffic prediction and traffic application. Later, we review existing work on the four parts. First, we summarize traffic data into five types according to their difference on spatial and temporal dimensions. Second, we focus on four significant data preprocessing techniques: map-matching, data cleaning, data storage and data compression. Third, we focus on three kinds of traffic prediction problems (i.e., classification, generation and estimation/forecasting). In particular, we summarize the challenges and discuss how existing methods address these challenges. Fourth, we list five typical traffic applications. Lastly, we provide emerging research challenges and opportunities. We believe that the survey can help the partitioners to understand existing traffic prediction problems and methods, which can further encourage them to solve their intelligent transportation applications.

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“…Following the rule in previous studies [26,27], we employ the root mean square error (RMSE), mean absolute error (MAE), Frechet distance (FD), and average Euclidean distance (AED) to measure the prediction goodness. For any given ship trajectories, the prediction accuracy is quantified with the above-mentioned statistical indicators (see equations (13) to (16)). e smaller RMSE, MAE, FD, and AED indicate more accurate ship trajectory prediction accuracy, and vice versa.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…AIS data anomaly removal studies involve unsupervised clustering method and neural network based and statistical models [15,16]. Liu et al proposed an adaptive Douglas-Peucker framework to suppress AIS data outliers in the manner of data compression [17].…”

Section: Introductionmentioning

confidence: 99%

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

Chen

Ling

Yang

et al. 2020

Mathematical Problems in Engineering

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Accurate ship trajectory plays an important role for maritime traffic control and management, and ship trajectory prediction with Automatic Identification System (AIS) data has attracted considerable research attentions in maritime traffic community. The raw AIS data may be contaminated by noises, which limits its usage in maritime traffic management applications in real world. To address the issue, we proposed an ensemble ship trajectory reconstruction framework combining data quality control procedure and prediction module. More specifically, the proposed framework implemented the data quality control procedure in three steps: trajectory separation, data denoising, and normalization. In greater detail, the data quality control procedure firstly identified outliers from the raw ship AIS data sample, which were further cleansed with the moving average model. Then, the denoised data were normalized into evenly distributed data series (in terms of time interval). After that, the proposed framework predicted ship trajectory with the artificial neural network. We verified the proposed model performance with two ship trajectories downloaded from public accessible AIS data base.

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A Denoising Scheme-Based Traffic Flow Prediction Model: Combination of Ensemble Empirical Mode Decomposition and Fuzzy C-Means Neural Network

Cited by 25 publications

References 49 publications

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

Augmented Ship Tracking Under Occlusion Conditions From Maritime Surveillance Videos

A Survey of Traffic Prediction: from Spatio-Temporal Data to Intelligent Transportation

Ship Trajectory Reconstruction from AIS Sensory Data via Data Quality Control and Prediction

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