Prediction of Vessel Traffic Volume in Ports Based on Improved Fuzzy Neural Network

Abstract

The accurate prediction of vessel traffic volume (VTV) is very helpful to rational scheduling of port resources and reducing vessel accidents. However, the traditional VTV prediction methods face problems like the overfitting of historical data and prediction inaccuracy. To solve these problems, this paper improved the fuzzy neural network (FNN) with quantum genetic algorithm (QGA). Firstly, the basic principles of neural network (NN), fuzzy inference and the QGA were introduced in turn. Then, the weights of the FNN were optimized by the QGA. On this basis, the authors established a VTV prediction model based on the improved FNN. Finally, the established model was applied to predict the VTV in an actual port of China, in comparison with several classic NNs. The VTV data were collected based on the length and gross tonnage of vessels. The results show that the improved FNN outperformed the contrastive methods in the accuracy of VTV prediction, thanks to the optimization by the QGA. The proposed method enjoys a great application potential in the prediction of the VTV in ports. INDEX TERMS Vessel traffic volume (VTV), prediction model, fuzzy neural network (FNN), quantum genetic algorithm (QGA).

EnvClus*: Extracting Common Pathways for Effective Vessel Trajectory Forecasting

“…With the rise of neural networks in the past few years, several works utilize deep learning techniques to predict the behavior of vessels. These works may focus on different aspects of movement at sea from predicting a vessel's heave motion [36] to providing an estimate about future vessel traffic flow over an area [37]- [39]. Similarly, a number of frameworks that take advantage of their capabilities for effective trajectory forecasting have been proposed in recent years [40].…”