Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model

Pengfei Lv, Yuan Zhuang, Kun Yang, M. Figueira, Z. Guo
2016 MATEC Web of Conferences  
This paper discusses the distribution regularity of ship arrival and departure and the method of prediction of ship traffic flow. Depict the frequency histograms of ships arriving to port every day and fit the curve of the frequency histograms with a variety of distribution density function by using the mathematical statistic methods based on the samples of ship-to-port statistics of Fangcheng port nearly a year. By the chisquare testing the fitting with Negative Binomial distribution and
more » ... tribution and t-Location Scale distribution are superior to normal distribution and Logistic distribution in the branch channel the fitting with Logistic distribution is superior to normal distribution Negative Binomial distribution and t-Location Scale distribution in main channel. Build the BP neural network and Markov model based on BP neural network model to forecast ship traffic flow of Fangcheng port. The new prediction model is superior to BP neural network model by comparing the relative residuals of predictive value, which means the new model can improve the prediction accuracy. , 81 6810 ICTTE 4007 4007
doi:10.1051/matecconf/20168104007 fatcat:c5v7rwtbivhn3h7eadrdsgm7ni