PSO-无偏灰色马尔科夫模型在船舶交通流量预测中的应用
|
摘要
为提高船舶交通流量的预测精度,利用具有全局搜索能力的粒子群算法(Particle Swarm Optimization, PSO)对无偏灰色马尔科夫模型进行优化,构建船舶交通流量预测的PSO-无偏灰色马尔科夫模型。该模型可综合考虑预测中的残差序列、状态区间、状态转移概率,自适应选取最优白化系数,用以准确表征船舶交通流量的发展趋势。以东营港2012—2017年船舶交通流量季度统计数据为例,选取前21个季度数据对模型进行训练,后2个季度数据对预测结果进行分析,与传统的无偏灰色模型和无偏灰色马尔科夫模型相比,该模型能显著地提高船舶交通流量的预测精度,其拟合精度和预测精度分别为91.439%和95.959%,验证后该模型具有科学性与有效性。
Particle Swarm Optimization(PSO) with global search capability is used to optimize the unbiased gray Markov model. The improved model can synthetically consider the residual sequence, the state interval and the state transition probabilities in the prediction, and select the optimal whitening coefficient adaptively to accurately characterize the development trend of the ship traffic flow. The quarterly statistical data of ship traffic flow from 2012 to 2017 in Dongying Port are taken to demonstrate the process. The data of the first 21 quarters are selected to train the model and the data of the latter two quarters are used for checking the forecast results. In comparison with the traditional unbiased gray model and the unbiased gray Markov model, the proposed model shows significant improvement of the prediction accuracy. The fitting accuracy and the prediction accuracy reach 91.439% and 95.959%, respectively, proving the validity of the model.
Particle Swarm Optimization(PSO) with global search capability is used to optimize the unbiased gray Markov model. The improved model can synthetically consider the residual sequence, the state interval and the state transition probabilities in the prediction, and select the optimal whitening coefficient adaptively to accurately characterize the development trend of the ship traffic flow. The quarterly statistical data of ship traffic flow from 2012 to 2017 in Dongying Port are taken to demonstrate the process. The data of the first 21 quarters are selected to train the model and the data of the latter two quarters are used for checking the forecast results. In comparison with the traditional unbiased gray model and the unbiased gray Markov model, the proposed model shows significant improvement of the prediction accuracy. The fitting accuracy and the prediction accuracy reach 91.439% and 95.959%, respectively, proving the validity of the model.
引文
[1] 冯宗宪,李刚.“一带一路”建设与周边区域经济合作推进路径[J].西安交通大学学报(社会科学版),2015,35(6):1-9.
[2] 金永兴,武晓云.新世纪航海技术的展望[J].中国航海,2002(1):3-8.
[3] 刘钊.船舶交通流特征统计分析及预测模型研究[D].武汉:武汉理工大学,2013.
[4] 李娜.基于四阶段法的船舶交通量预测方法研究[D].武汉:武汉理工大学,2010.
[5] 张树奎,肖英杰.考虑周期性波动因素的船舶交通流量预测模型[J].大连海事大学学报,2016,42(4):41-46.
[6] 郝勇,王怡.基于优化RBF网络的港口船舶交通流量预测[J].中国航海,2014,37(2):81-84.
[7] 马晓波,刘雪菲,戴冉.优化的长山水道船舶交通流量灰色系统预测模型[J].上海海事大学学报,2016,37(2):12-16.
[8] 郑友银,徐志京.基于灰色自回归模型的船舶流量预测方法[J].船海工程,2011,40(1):122-124.
[9] 吕鹏飞,庄元,李洋,等.船舶交通量的BP神经网络-马尔科夫预测模型[J].上海海事大学学报,2017,38(2):17-21.
[10] 李松,刘力军,翟曼.改进粒子群算法优化BP神经网络的短时交通流预测[J].系统工程理论与实践,2012,32(9):2045-2049.
[11] 罗贺,胡笑旋,牛艳秋,等.高次插值的GM(1,1)模型预测方法的改进[J].统计与决策,2017(5):90-93.
[12] 樊冬雪.基于优化灰色马尔科夫链模型的铁路客流量预测方法研究[D].重庆:重庆交通大学,2015.
[13] 徐建新,杨杰.煤矿百万吨死亡率动态无偏灰色马尔科夫预测[J].中国安全科学学报,2012,22(3):122-127.
[14] 李大伟,徐浩军,刘东亮,等.改进的灰色马尔科夫模型在飞行事故率预测中的应用[J].中国安全科学学报,2009,19(9):53-57.
[15] KENNEDY J,EBERHART R.Particle Swarm Optimization[J].IEEE,2002,4(8):1942-1948.
[16] 马勇.基于排队论的T型航路船舶交通通过能力研究[D].大连:大连海事大学,2008.
[2] 金永兴,武晓云.新世纪航海技术的展望[J].中国航海,2002(1):3-8.
[3] 刘钊.船舶交通流特征统计分析及预测模型研究[D].武汉:武汉理工大学,2013.
[4] 李娜.基于四阶段法的船舶交通量预测方法研究[D].武汉:武汉理工大学,2010.
[5] 张树奎,肖英杰.考虑周期性波动因素的船舶交通流量预测模型[J].大连海事大学学报,2016,42(4):41-46.
[6] 郝勇,王怡.基于优化RBF网络的港口船舶交通流量预测[J].中国航海,2014,37(2):81-84.
[7] 马晓波,刘雪菲,戴冉.优化的长山水道船舶交通流量灰色系统预测模型[J].上海海事大学学报,2016,37(2):12-16.
[8] 郑友银,徐志京.基于灰色自回归模型的船舶流量预测方法[J].船海工程,2011,40(1):122-124.
[9] 吕鹏飞,庄元,李洋,等.船舶交通量的BP神经网络-马尔科夫预测模型[J].上海海事大学学报,2017,38(2):17-21.
[10] 李松,刘力军,翟曼.改进粒子群算法优化BP神经网络的短时交通流预测[J].系统工程理论与实践,2012,32(9):2045-2049.
[11] 罗贺,胡笑旋,牛艳秋,等.高次插值的GM(1,1)模型预测方法的改进[J].统计与决策,2017(5):90-93.
[12] 樊冬雪.基于优化灰色马尔科夫链模型的铁路客流量预测方法研究[D].重庆:重庆交通大学,2015.
[13] 徐建新,杨杰.煤矿百万吨死亡率动态无偏灰色马尔科夫预测[J].中国安全科学学报,2012,22(3):122-127.
[14] 李大伟,徐浩军,刘东亮,等.改进的灰色马尔科夫模型在飞行事故率预测中的应用[J].中国安全科学学报,2009,19(9):53-57.
[15] KENNEDY J,EBERHART R.Particle Swarm Optimization[J].IEEE,2002,4(8):1942-1948.
[16] 马勇.基于排队论的T型航路船舶交通通过能力研究[D].大连:大连海事大学,2008.