Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

Chen, Xinqiang; Lu, Jinquan; Zhao, Jiansen; Qu, Zhijian; Yang, Yongsheng; Xian, Jiangfeng

doi:10.3390/su12093678

Cited by 54 publications

(46 citation statements)

References 32 publications

(36 reference statements)

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“…Kim and Lee proposed a novel deep neural network model to remove AIS outliers and thus predict both medium-and long-term ship trajectory variation tendencies [23]. Similar researches can be found in [8,[24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 64%

“…To quantify the ship trajectory prediction performance, we compare the predicted AIS data with ground truth data with typical statistical measurements. 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)).…”

Section: Evaluation Metricsmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Introductionmentioning

confidence: 64%

Section: Evaluation Metricsmentioning

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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“…There have recently been some new technologies, such as the intelligent transportation system (ITS) [38][39][40], autonomous vehicles [41], and advanced driving simulator [42]. A multilayer feed-forward artificial neural network (ANN) model could be used to track drivers and vehicles in work zones; the new model performs better than the traditional deterministic queuing model in estimating the travel delay caused by the work zone [43].…”

Section: Methods For Work Zone Impact Researchmentioning

confidence: 99%

Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method

Shao

Luo

et al. 2020

Sustainability

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The impact of work zones on traffic is a common problem encountered in traffic management. The reconstruction of roads is inevitable, and it is necessary and urgent to reduce the impact of the work zone on the operation of traffic. There are many existing research results on the influence of highway work zones, including management strategies, traffic flow control strategies, and various corresponding model theories. There are also many research results on the impacts of urban road and subway construction on traffic operation, including construction efficiency, economic impact, and travel matrix. However, there are few studies concerning the choice of work zone location, and most previous studies have assumed that the work zone choice was scientific and reasonable. Therefore, it is reasonable to choose the location of the work zone and to assess whether there is room for improvement in the road form of the work zone, but this remains a research gap. Therefore, we studied a seven-lane main road T-intersection in Xi’an, China, and investigated a work zone located at this intersection that caused a road offset, leading to the non-aligned flow of main traffic. We designed two road improvement schemes and multiple transition schemes, used VISSIM software to evaluate the traffic operation of the two schemes, and used the entropy method to choose the suitability of the two schemes under different conditions. According to the results, in the best case, the driving time, delay, and number of stops are reduced by 44%, 66%, and 92%.

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“…EMD method is usually applied for original signal decomposition into its intrinsic multi-scale characteristics [20]. Generally, prediction methods that are based on signal's multi-scale characteristics are widely applied in different fields like short-term rainfall forecasting [21], short-term traffic flow prediction [22][23][24] and short-term wind power forecasting [25][26]. In the fields of water quality forecasting in aquaculture environment, Li et al [18] applied the ensemble empirical mode decomposition method to propose an efficient hybrid model for DO concentration forecasting in aquaculture based on original signal multi-scale features in order to increase the forecasting accuracy of DO content [27] in aquaculture environment.…”

Section: Related Literature Reviewmentioning

confidence: 99%

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

Eze¹,

Ajmal²

2020

Preprint

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Dissolved Oxygen (DO) concentration is a vital parameter that indicates water quality. DO short term forecasting using time series analysis on data collected from an aquaculture pond is presented here. This can provide data support for an early warning system for an improved management of the aquaculture farm. The conventional forecasting approaches are commonly characterized by low accuracy and poor generalization problems. In this article, we present a novel hybrid DO concentration forecasting method with ensemble empirical mode decomposition (EEMD) based LSTM (Long short-term memory) neural network (NN). With this method, first, the sensor data integrity is improved through linear interpolation and moving average filtering methods of data preprocessing. Next, the EEMD algorithm is applied to decompose the original sensor data into multiple intrinsic mode functions (IMFs). Finally, the feature selection is used to carefully select IMFs that are strongly correlated with the original sensor data and integrate both into inputs for the NN. The hybrid EEMD-based LSTM forecasting model is then constructed. Performance of this proposed model in training and validation sets was compared with the observed real sensor data. To obtain the exact evaluation accuracy of the forecasted results of the hybrid EEMD-based LSTM forecasting model, three statistical performance indices were adopted: MAE, MSE, and RMSE. Results presented for short term (12-hour period) and long term (1-month period) give a strong indication of suitability of this method for forecasting DO values.

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Traffic Flow Prediction at Varied Time Scales via Ensemble Empirical Mode Decomposition and Artificial Neural Network

Cited by 54 publications

References 32 publications

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

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

Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method

Dissolved Oxygen Forecasting in Aquaculture: A Hybrid Model Approach

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