Traffic flow prediction based on combination of support vector machine and data denoising schemes

Tang, Jinjun; Chen, Xinqiang; Hu, Zheng; Zong, Fang; Han, Chunyang; Li, Leixiao

doi:10.1016/j.physa.2019.03.007

Cited by 148 publications

(57 citation statements)

References 44 publications

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“…More specifically, we demonstrated our proposed ship tracking model's (i.e., MVLWD model) performance with different wavelet bases, which were further compared against the other two ship tracking models. The harr basis, db basis, sym basis, coif basis, and bior basis in the WF model demonstrated the efficacy on the traffic data smooth applications [50] which showed the potential of suppressing ship position outliers in our study. In that manner, our proposed model performances were presented in detail with the WF model implemented with the above wavelet basis.…”

Section: Ship Tracking Results For Video #1mentioning

confidence: 65%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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Maritime surveillance videos provide crucial on-spot kinematic traffic information (traffic volume, ship speeds, headings, etc.) for varied traffic participants (maritime regulation departments, ship crew, ship owners, etc.) which greatly benefits automated maritime situational awareness and maritime safety improvement. Conventional models heavily rely on visual ship features for the purpose of tracking ships from maritime image sequences which may contain arbitrary tracking oscillations. To address this issue, we propose an ensemble ship tracking framework with a multi-view learning algorithm and wavelet filter model. First, the proposed model samples ship candidates with a particle filter following the sequential importance sampling rule. Second, we propose a multi-view learning algorithm to obtain raw ship tracking results in two steps: extracting a group of distinct ship contour relevant features (i.e., Laplacian of Gaussian, local binary pattern, Gabor filter, histogram of oriented gradient, and canny descriptors) and learning high-level intrinsic ship features by jointly exploiting underlying relationships shared by each type of ship contour features. Third, with the help of the wavelet filter, we performed a data quality control procedure to identify abnormal oscillations in the ship positions which were further corrected to generate the final ship tracking results. We demonstrate the proposed ship tracker’s performance on typical maritime traffic scenarios through four maritime surveillance videos.

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Section: Ship Tracking Results For Video #1mentioning

confidence: 65%

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Chen

et al. 2020

Sensors

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“…More specifically, both Partition algorithm and Sampling algorithm are used to improve the Apriori algorithm efficiency by optimizing the data scan frequency. For example, the Partition algorithm is used to mine the frequent itemset by evenly dividing the initial database, but the results are not perfectly accurate [27][28][29][30][31]. The sampling algorithm obtains the frequent itemset identification with a similar logic to that of the Partition algorithm.…”

Section: Dqcpea Algorithm For Identifying Ship Deficiencymentioning

confidence: 99%

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Chen

et al. 2020

PLoS ONE

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Early warning on the ship deficiency is crucial for enhancing maritime safety, improving maritime traffic efficiency, reducing ship fuel consumption, etc. Previous studies focused on the ship deficiency exploration by mining the relationships between the ship physical deficiencies and the port state control (PSC) inspection results with statistical models. Less attention was paid to discovering the correlation rules among various parent ship deficiencies and subcategories. To address the issue, we proposed an improved Apriori model to explore the intrinsic mutual correlations among the ship deficiencies from the PSC inspection dataset. Four typical ship property indicators (i.e., ship type, age, deadweight and gross tonnage) were introduced to analyze the correlations for the ship parent deficiency categories and subcategories. The findings of our research can provide basic guidelines for PSC inspections to improve the ship inspection efficiency and maritime safety.

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“…The forecasting results verified that EMD could improve accuracy significantly. Tang et al [25] adopted a new hybrid model for traffic volume prediction by using the combination of EEMD and SVM. The results showed this model had superior performance over the single SVM.…”

Section: Introductionmentioning

confidence: 99%

Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

et al. 2020

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Short-term traffic flow prediction is important to realize real-time traffic instruction. However, due to the existing strong nonlinearity and non-stationarity in short-term traffic volume data, it is hard to obtain a satisfactory result through the traditional method. To this end, this paper develops an innovative hybrid method based on the time varying filtering based empirical mode decomposition (TVF-EMD) and least square support vector machine (LSSVM). Specifically, TVF-EMD is firstly used to deal with the implied non-stationarity in the original data by decomposing them into several different subseries. Then, the LSSVM models are established for each subseries to capture the linear and nonlinear characteristics embedded in the original data, and the corresponding prediction results are superimposed to obtain the final one. Finally, case studies based on two groups of data measured from an arterial road intersection are employed to evaluate the performance of the proposed method. The experimental results indicate it outperforms the other involved models. For example, compared with the LSSVM model, the average improvements by the proposed method in terms of the indexes of mean absolute error, mean relative percentage error, root mean square error and root mean square relative error are 7.397, 15.832%, 10.707 and 24.471%, respectively.

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Traffic flow prediction based on combination of support vector machine and data denoising schemes

Cited by 148 publications

References 44 publications

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Robust Visual Ship Tracking with an Ensemble Framework via Multi-View Learning and Wavelet Filter

Mining ship deficiency correlations from historical port state control (PSC) inspection data

Short Term Traffic Flow Prediction of Urban Road Using Time Varying Filtering Based Empirical Mode Decomposition

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