College of Navigation, Jimei University, Xiamen 361021, Fujian, China.
Comput Intell Neurosci. 2022 Jun 1;2022:7219661. doi: 10.1155/2022/7219661. eCollection 2022.
This paper proposes a data-driven risk assessment model for ship overtaking based on the particle swarm optimization (PSO) improved kernel density estimation (KDE). By minimizing the mean square error between the real probability distribution of the ship overtaking point and the kernel density estimation probability distribution calculated by the current kernel density bandwidth, the longitude and latitude of the ship overtaking point are displayed by the color corresponding to the probability as the cost objective function of the search bandwidth of the algorithm. This can better show the distribution of the overtaking points of channel propagation traffic flow. A probability-based ship-overtaking risk evaluation model is developed through the bandwidth and density analysis optimized by an intelligent algorithm. In order to speed up searching the optimal variable width of the kernel density estimator for ship encountering positions, an improved adaptive variable-width kernel density estimator is proposed. The latter reduces the risk of too smooth probability density estimation phenomenon. Its convergence is proved. Finally, the model can efficiently evaluate the risk status of ship overtaking and provide navigational auxiliary decision support for pilots.
本文提出了一种基于粒子群优化（PSO）改进核密度估计（KDE）的数据驱动的船舶会遇风险评估模型。通过最小化船舶会遇点的实际概率分布与当前核密度带宽计算的核密度估计概率分布之间的均方误差，船舶会遇点的经纬度将通过与概率相对应的颜色显示出来，作为算法搜索带宽的代价目标函数。这可以更好地显示航道传播交通流的会遇点分布。通过智能算法对带宽和密度进行优化，开发了一种基于概率的船舶会遇风险评估模型。为了加快搜索船舶会遇位置的核密度估计器的最优可变带宽，提出了一种改进的自适应可变带宽核密度估计器。后者降低了概率密度估计现象过于平滑的风险。证明了其收敛性。最后，该模型可以有效地评估船舶会遇的风险状况，并为驾驶员提供航海辅助决策支持。
