Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model
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...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2016-01-01
|
| Series: | MATEC Web of Conferences |
| Online Access: | http://dx.doi.org/10.1051/matecconf/20168104007 |
| _version_ | 1818368552944533504 |
|---|---|
| author | Lv Pengfei Zhuang Yuan Yang Kun |
| author_facet | Lv Pengfei Zhuang Yuan Yang Kun |
| author_sort | Lv Pengfei |
| collection | DOAJ |
| description | 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 chi-square testing: the fitting with Negative Binomial distribution 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. |
| first_indexed | 2024-12-13T23:09:46Z |
| format | Article |
| id | doaj.art-cf41e1612e46430a92406bc032b98d04 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-12-13T23:09:46Z |
| publishDate | 2016-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-cf41e1612e46430a92406bc032b98d042022-12-21T23:28:09ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01810400710.1051/matecconf/20168104007matecconf_ictte2016_04007Prediction of Ship Traffic Flow Based on BP Neural Network and Markov ModelLv PengfeiZhuang YuanYang KunThis 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 chi-square testing: the fitting with Negative Binomial distribution 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.http://dx.doi.org/10.1051/matecconf/20168104007 |
| spellingShingle | Lv Pengfei Zhuang Yuan Yang Kun Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model MATEC Web of Conferences |
| title | Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model |
| title_full | Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model |
| title_fullStr | Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model |
| title_full_unstemmed | Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model |
| title_short | Prediction of Ship Traffic Flow Based on BP Neural Network and Markov Model |
| title_sort | prediction of ship traffic flow based on bp neural network and markov model |
| url | http://dx.doi.org/10.1051/matecconf/20168104007 |
| work_keys_str_mv | AT lvpengfei predictionofshiptrafficflowbasedonbpneuralnetworkandmarkovmodel AT zhuangyuan predictionofshiptrafficflowbasedonbpneuralnetworkandmarkovmodel AT yangkun predictionofshiptrafficflowbasedonbpneuralnetworkandmarkovmodel |