Prediction model of ship traffic flow based on  periodic fluctuation factors

  • ZHANG Shu-kui ,
  • XIAO Ying-jie
Expand
  • (1.Navigation College,Jiangsu Maritime Institute, Nanjing 211170,China;2.Merchant College,Shanghai Maritime University,Shanghai 201306,China)

Received date: 2016-04-01

  Revised date: 2016-06-08

  Online published: 2016-06-08

Abstract

In order to improve prediction accuracy of ship traffic flow, an improved model was developed to predict ship traffic flow based on linear growth model in consideration of all periodic fluctuation factors, such as season, climate, and so on, then the Bayesian estimation and prediction were used to solve the new model, and ship traffic flow was predicted by using the time series data of ship traffic flow. Results show that the proposed model more accords with the actual situation of traffic flow comparing with the linear growth model, and the mean absolute error of monthly ship flow decreases 3.56%, and the standard deviation decreases 3.79%, therefore, it is effective to predict ship traffic flow.

Cite this article

ZHANG Shu-kui , XIAO Ying-jie . Prediction model of ship traffic flow based on  periodic fluctuation factors[J]. Journal of Dalian Maritime University, 2016 , 42(4) : 41 -46 . DOI: 10.16411/j.cnki.issn1006-7736.2016.04.007

References

[1]王宝阔.船舶交通事故量灰色预测应用研究[J].中国航海,2011,34(1):59-62.
WANG Bao-kuo.Applied research of grey prediction for marine traffic accidents[J].Navigation of China,2011,34(1):59-62.(in Chinese)
[2]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.
ZHENG You-yin,XU Zhi-jing.Prediction method of ship flow based on grey-auto regression model[J].Ship & Ocean Engineering,2011,40(1):122-124.(in Chinese)
[3]张树奎,肖英杰.船舶交通流量预测的灰色神经网络模型[J].上海海事大学学报,2015,36(1):46-49.
ZHANG Shu-kui,XIAO Ying-jie.Grey neural network model for ship traffic flow prediction[J].Journal of Shanghai Maritime University,2015,36(1):46-49.(in Chinese)
[4]王琪,王志朋.马尔可夫灰模型的海上交通事故预测[J].中国航海,2013,36(4):119-124.
WANG Qi,WANG Zhi-ming.Forecasting of maritime traffic accidents based on the improved SCGM(1,1)c Markov model[J].Navigation of China,2013,36(4):119-124.(in Chinese)
[5]李俊.基于支持向量机的船舶交通事故预测研究[D].武汉:武汉理工大学,2008.
LI Jun.The research of maritime accidents based on the support vector machines[J].Wuhan:Wuhan University of Technology,2008.(in Chinese)
[6]陈丹,胡明华,张洪海,等.考虑周期性波动因素的中长期空中交通流量预测[J].西南交通大学学报,2015,50(3):562-568.
CHEN Dan,HU Ming-hua,ZHANG Hong-hai,et al.Forecast method for medium-long term air traffic flow considering periodic fluctuation factors[J].Journal of Southwest Jiaotong University,2015,50(3):562-568.(in Chinese)
[7]ONDER E,KUZU S.Forecasting air traffic volumes using smoothing techniques[J].Journal of China Aeronautics and Space Technologies,2014,7(1):65-70.
[8]MENON P K.New approach for modeling,analysis,and control of air traffic flow[J].Journal of Guidance,Control,and Dynamics,2014,27(5):737-744.
[9]范莹莹,余思勤.基于NARX神经网络的港口集装箱吞吐量预测[J].上海海事大学学报,2015,36(4):1-5.
FAN Ying-ying,YU Si-qin.Port container throughput forecast based on NARX neural network[J].Journal of Shanghai Maritime University,2015,36(4):1-5.(in Chinese)
[10]BOUGAS C.Forecasting air passenger traffic flows in Canada:an evaluation of time series models and combination methods[D].Quehec,Canada:Constlantinos Bougas,2013.
[11]赵玉环,郭爽.考虑随机因素的空中交通流量预测模型研究[J].中国民航大学学报,2008,26(4):59-61.
ZHAO Yu-huan,GUO Shuang.Study on forecasting model of air traffic flow considering stochastic factors[J].Journal of Civil Aviation University of China,2008,26(4):59-61.(in Chinese)
[12]WEST M,HAPPISON P J.Bayesian forecasting and dynamic models[M].2nd ed.New York:Stringer-Verlag,1997:20-27.
[13]张树奎,鲁子爱.港口集装箱吞吐量的灰色神经网络预测模型研究[J].江苏科技大学学报:自然科学版,2014,28(3):216-219.
ZHANG Shu-kui,LU Zi-ai.Prediction model of port container throughput with grey neural network[J].Journal of Jiangsu University of Science and Technology:Natural Science Edition,2014,28(3):216-219.(in Chinese)

Outlines

/