Short-term vessel traffic flow forecasting by using an improved Kalman model

Vessel traffic flow forecasting is of significant importance for the water transport safety, especially in the multi-bridge water areas. An improved Kalman model combining regression analysis and Kalman filtering is proposed for short-term vessel traffic flow forecasting between Wuhan Yangtze River Bridge (hereafter WYRB) and the Second Wuhan Yangtze River Bridge (hereafter SWYRB). Given the vessel traffic flow of WYRB is positively correlated with that of SWYRB, its regression coefficient is obtained as well as the regression predictions. The predictions are further used to replace the state transition equation of Kalman filtering. The prediction results of the improved Kalman model demonstrate better agreements with field observations, and hence, illustrate good capability of the proposed method in the short-term traffic flow forecasting. The discrepancy between the model predictions and field observations is generally attributed to the inherent deficiency of Kalman filtering method and the errors resulted from automatic identification system (AIS) data (e.g. missed AIS data). The proposed method can provide a support for the real-time and accurate basis for the ship traffic planning management

A hierarchical methodology for vessel traffic flow prediction using Bayesian tensor decomposition and similarity grouping

Accurate vessel traffic flow (VTF) prediction can enhance navigation safety and economic efficiency. To address the challenge of the inherently complex and dynamic growth of the VTF time series, a new hierarchical methodology for VTF prediction is proposed. Firstly, the original VTF data is reconfigured as a three-dimensional tensor by a modified Bayesian Gaussian CANDECOMP/PARAFAC (BGCP) tensor decomposition model. Secondly, the VTF matrix (hour ✕ day) of each week is decomposed into high- and low-frequency matrices using a Bidimensional Empirical Mode Decomposition (BEMD) model to address the non-stationary signals affecting prediction results. Thirdly, the self-similarities between VTF matrices of each week within the high-frequency tensor are utilised to rearrange the matrices as different one-dimensional time series to solve the weak mathematical regularity in the high-frequency matrix. Then, a Dynamic Time Warping (DTW) model is employed to identify grouped segments with high similarities to generate more suitable high-frequency tensors. The experimental results verify that the proposed methodology outperforms the state-of-the-art VTF prediction methods using real Automatic Identification System (AIS) datasets collected from two areas. The methodology can potentially optimise relation operations and manage vessel traffic, benefiting stakeholders such as port authorities, ship operators, and freight forwarders

Deep learning-powered vessel traffic flow prediction with spatial-temporal attributes and similarity grouping

Perceiving the future trend of Vessel Traffic Flow (VTF) in advance has great application values in the maritime industry. However, using such big data from the Automatic Identification System (AIS) for accurate VTF prediction remains challenging. Deep training networks can learn valuable features from extensive historical data. This paper proposes a new learning-based prediction network, improved Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) with similarity grouping, including three views. To effectively enable the training network to capture the temporal and periodic (i.e. a spatial attribute) change characteristics of VTF, the CNN and LSTM are employed to compose spatial and temporal views, respectively. Hence, the original one-dimensional data is transformed into a matrix (hour of the day ✕ day) to adapt the input of the proposed methodology. In practical applications, VTF of multiple adjacent target regions need to be predicted simultaneously, and the changes of VTF in different areas may influence each other. To explore their hidden relationships, the similarity grouping view aims to find the target area that exhibits the most similarity with the VTF change trend of the current research area. Furthermore, similar information is combined with the features generated from the other two views to obtain the prediction results. In summary, the new advantage lies in mining the spatiotemporal attributes of data and fusing the similarity information of adjacent regions. Comparative experiments with eleven other methods on realistic VTF datasets show that the proposed method demonstrates superior prediction accuracy and stability performance