%0 Journal Article
%T 基于MFO-LSSVM的船舶交通流量预测模型<br>Prediction Model of Ship Traffic Flow Based on MFO-LSSVM
%A 朱姗
%A 孙立谦
%J Open Journal of Transportation Technologies
%P 279-288
%@ 2326-344X
%D 2020
%I Hans Publishing
%R 10.12677/OJTT.2019.94034
%X <div style=\"text-align:justify;\">
	<span style=\"line-height:1.5;\">针对当前船舶交通流模型没有充分考虑流量数据本身特性、预测方法精度不高的问题，提出了一种基于飞蛾火焰优化算法(Moth-flame Optimization, MFO)和最小二乘支持向量机(least squares support vector machine, LSSVM)的预测模型，该模型主要利用飞蛾火焰算法对LSSVM模型内部参数进行优化，基于采集的数据进行模型训练和预测。为验证模型有效性，利用我国广东省船舶交通流量等相关数据进行实验，并与FOA-LSSVM、PSO-LSSVM和GA-LSSVM等模型进行对比分析，结果表明MFO-LSSVM模型具有较高的预测精度和预测效率，验证了方法的有效性，可以用于船舶交通流量的预测。<br />
Current dominant ship traffic flow prediction models don’t consider the characteristics of the data and achieve high accuracy in the prediction process, to resolve these problems, a prediction model based on moth flame optimization algorithm and least squares support vector machine is proposed from the perspective of influencing factors of ship traffic flow. The essence of the model is to optimize the internal parameters of LSSVM model by moth flame algorithm and the model is trained based on collected data. To verify the validity of the proposed model, experiments are conducted based on the relevant data of ship traffic flow in Guangdong Province of China, and compared with FOA-LSSVM, PSO-LSSVM and GA-LSSVM models, the experimental results show that the MFO-LSSVM model has higher prediction accuracy and efficiency, the effectiveness of the proposed model is verified and can be used for the prediction of ship traffic flow.</span><span style=\"line-height:1.5;\"></span> 
</div>
%K 智能交通，交通流量预测，飞蛾火焰优化算法，船舶交通流量，最小二乘支持向量机<br>Intelligent Transportation
%K Traffic Flow Prediction
%K Moth-Flame Optimization
%K Ship Traffic Flow
%K Least Squares Support Vector Machine
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=35918