KEYWORDS: Data modeling, Data conversion, Error analysis, Statistical analysis, Safety, Statistical modeling, Neurons, Neural networks, Process modeling, Data processing
Traffic flow prediction is one of the point research contents of transportation engineering. Accurate ship traffic flow prediction is the value to ensure the safety of ship navigation and smooth channel. In order to predict the port ship traffic flow more accurately, aiming at the shortcomings of the traditional prediction model, a port ship traffic flow prediction model based on long-term and short-term memory network (LSTM) is proposed. Finally, Qingdao port is taken as an example to predict and compare with ARIMA model. The results show that, compared with ARIMA model, the mean absolute percentage error (MAPE) of LSTM model is as low as 3.476%, which indicates that the prediction accuracy of LSTM model is higher and it can be well applied to the field of ship traffic flow prediction. According to the prediction results, it can provide basic basis for channel planning and design and ship navigation management, maximize the navigation capacity of the channel, and optimize the allocation of port resources.
In order to improve the effectiveness of intelligent collision avoidance decision of ships, a collision avoidance decision model is established with the objective of guaranteeing the safety of ship navigation and reducing the loss of voyage, taking real-time data as the input quantity and the requirements of international maritime collision avoidance rules and the common practice of seafarers as the constraints, and MOEA/D (Multi-objective Evolutionary Algorithm Based on Decomposition) algorithm is used to optimize the objective function and calculate the optimal steering angle and steering timing. The simulation results show that the decision can meet the safety and economic requirements of ship collision avoidance under different encounter situations and can provide some reference for ship collision avoidance decision.
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